Big Bet Review Fall 2009

8/2/2019 Big Bet Review Fall 2009

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HP Confidential Page 1 of 23

Extracting Knowledge and Value from Collective IntelligenceBernardo HubermanAnupriya AnkolekarMichael Brzozowski

Leslie FineScott Golder

Tad HoggGabor Szabo

Dennis WilkinsonFang Wu

Executive SummaryThe past decade has witnessed a momentous transformation in the way people use computers and theInternet to interact and exchange information. Content is now coactively produced, shared, classified, andrated on the Web by millions of people. Mobile devices providing easy, continuous access to Web ser-vices have become common. Commerce, social networking, opinion formation, and large collaborativeefforts are increasingly taking place online. This collective intelligence "cloud" represents a new phe-nomenon which is as yet poorly understood and poorly leveraged by existing applications and services.Since the cloud also represents great market potential, it is important to have a lively research program inthis domain.

Our research goal is to improve the value that users get from the collective intelligence cloud in an in-creasingly mobile and connected world. We will do this by improving our understanding of how informa-tion is created, evaluated and consumed online, and by designing, constructing and validating innovativesystems which will confer a market advantage to HP. Our proposed project is large and interdisciplinary,with a number of interdependent initiatives.

One of the most interesting and daunting consequences of the prevalence of the Web and digital media isthat information, which used to be scarce and therefore valuable, is now so ubiquitous so as be almostdevoid of monetary value. Search engines, billions of websites, targeted advertisement and easy access todigital content provide us with an overabundant supply of information for our business and entertainmentneeds. The value has now shifted to users attention and to tools for harvesting useful, contextual, trust-worthy knowledge from the flood of information. These areas make up the core of our research program.

Specifically, we will investigate the problem of attention allocation in information rich environments andits interplay with content novelty and popularity as well as user history and social standing. To addressthis problem, we will develop mathematical models for people's interactions with each other and with theavailable information. We will verify these models by analyzing existing cloud initiatives and by conduct-ing laboratory and online experiments. From these insights we will create algorithms and methods foroptimizing the presentation and rating of information in order to maximize its value to both users andproviders.

In addition, we will pursue a research program in designing and developing services for personalized in-formation access in mobile scenarios. We will develop a number of prototypes for capturing and monitor-ing personal context and then presenting users with customized, relevant results for their mobile Web in-formation searches. These prototypes will be designed and implemented taking into account the limitedattention of the user in mobile environments. We will validate these prototypes with formative and sum-mative user evaluations via field studies in natural mobile environments, and through laboratory experi-ments. Besides the design of useful prototypes, this work will also yield insights into which aspects of


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users context are most relevant for situation-specific personalization and how these can be effectivelyexploited.

A third focus of our research will be services in the enterprise. We will build services to incentivize andfacilitate knowledge sharing and harvesting through mechanisms of attention, status, and reputation.These services will incorporate what we have learned from our analysis, our empirical work, and our ex-periments. Conversely, experience from actual use of these services will improve our models and suggestfurther experiments.

The research impact of our project will be advancement of the state of the art, papers in high profile con-ferences, and exposure for HP Labs in this exciting and relevant field. The business impact will be toolsand applications for collaboration and knowledge sharing within the enterprise, for providing personal-ized information services in mobile environments, and for optimizing the presentation of information on awebsite. The knowledge sharing tool and applications for personalized information in mobile environ-ments will be available for use within HP by employees or as a service HP can provide to customers; al-gorithms for optimizing website information presentation will be applied to HP's websites and be avail-able as service for customers. We expect that the project will require four years and ten to twelve teammembers. Specifics of budget and personnel requirements and timeline are presented in sections 4 and 5.


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1 Research Contributions

MotivationAn information ecosystem exists when people who have information ("producers") connect with peoplewho desire information ("consumers"). Ideally, such an ecosystem acts to motivate producers to share andto help consumers identify information that will bring them the most value. Examples include marketswhere customers buy products that best fulfill their needs, knowledge reuse and sharing within an organi-zation, and commercial media like newspapers where information and advertising compete for visual realestate.

6 14 March 2008

Feedback

Distribution

The information ecosystem

Producers(people who

have info)

Consumers(people whocan use info)

M o t i v a t i o n

R e w a r d

When the information exposure of individuals is high, an information-rich economy ensues in which thereis keen competition for peoples attention (Falkinger 2007). It is clear that we find ourselves in such aregime today, in which information can be created and exchanged anytime and anywhere with great fluid-ity and ease. This situation presents a challenge for content providers, who need to decide what to priori-tize in order to get peoples attention, and for content consumers who attempt to extract value from theflood of available information.

A key intermediary, both on the information production side and on the consumption side, is a user's con-text. Context is based both on transient properties like a user's geographical location, schedule, tasks, andavailable device modalities, and more persistent features like a user's interests, personal tastes, and socialnetwork. Context affects how -- and why -- content gets produced and consumed.

The economics of attention, the design of information ecosystems, and the role of context therein thusrepresent three relevant and interrelated areas of research which we propose to study. A clear understand-ing of all three will allows us to implement powerful tools which bridge the barriers to perfect sharing of and access to information.


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1.1 State of the Art

Attention AllocationLimited attention has been a problem for content providers since earliest days of the Internet. The sim-plest solution is to present first the most salient items in an inventory while taking into account the visual

real estate available on a given device, with no regard for user personalization. This is the approach takenby search engines such as Google or Yahoo, who do not show all their search results on one webpage butrather prioritize and display them on consecutive pages whose value is assumed to be decreasingly lowerto the user. The same applies to large recommendation sites such as CNET, where items or stores areranked and displayed according to the number of positive rankings they receive. Shopping sites like HPsprovide another example of how products are presented, using an ordering based on a variety of heuristics(Ritter 2007).

This approach to presenting content suffers from two problems. The first resides with the content pro-vider, who needs to decide what to prioritize in order to get the users attention. It is not clear that existingprocedures actually maximize the users value. The second problem stems from the finite number of itemsthat a user can attend to in a given time interval. Because of this, a user is more likely to explore the first

few items presented to him or her. This behavior tends to reinforce the leading position of those top itemsand further increase their popularity, which in turn penalizes new content that is not yet well known (Cho2004, Pandey 2005, Halvey 2006).

A key consideration in deciding what content to display is the interplay of novelty and popularity in aninformation ecosystem. In many applications, there is a very strong connection between novelty of con-tent and the amount of attention that people devote to it (Wu 2007). But that is not the only determinant of attention, since users often need to decide among the existing plethora of links and sites. This is wherepopularity enters the picture, for people often go to a site or click on given links for no other reason thanthe fact that many others do. Existing algorithms or heuristics for content display that we are aware of donot take into account the interplay of novelty and popularity to ensure the maximum number of hits perunit time (a week, a month, etc.).

Another important goal for content providers is to provide viewer oriented personalization of web-pagecontent. This goal is very relevant to, for example, enterprise knowledge sharing systems. Two ap-proaches to this problem are currently in use: (1) Learning user profiles; (2) Online information gatheringby shared keywords (Domshlak 2001). Both approaches are useful in particular applications, but as gen-eral solutions they have some important drawbacks: The first approach addresses only long-term userpreferences, and therefore, it is only applicable to frequent viewers. In addition, it reacts slowly to shiftsin user interests. The second approach uses keywords that show up in the material the user requested as abasis for fetching and presenting additional information.

ContextA significant proportion of the information explosion on the Web comprises personal content and interac-

tion, as people write about personal events, activities, and the places where they happened; upload photos;review restaurants, books, cameras, etc (Horrigan 2007). The world has never been flooded with so muchimplicit and explicit information about people's actions, preferences, opinions and hopes. In addition topersonal content, web pages are being annotated with all kinds of information. For example, many photosare now increasingly annotated with the location where they were taken, web pages on Wikipedia includegeographical coordinates of the places they talk about, blogs carry markup about people and their friendsand blogroll acquaintances.


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As people do things in the real world and blog about it, or use online services to search for informationabout a particular place, they are creating traces of their location-related activities online. From an indi-vidual's point of view, there are several kinds of traces: personal traces (made by me), social network traces (made by my family, friends and acquaintances), Web community traces (made by people whom Idon't know) and potentially even historical event traces. We aim to visualize and exploit these traces of community and individual activity within physical spaces to make them more meaningful and useful topeople. For example, when visiting a new place, photos and reviews made by my friends (or, failing that,other people) can be pulled up from the Web. Social traces might be used to show traces of a friendnearby or show a visitor where the lonely and crowded places are in a new city.

The overwhelming majority of this information and the way it is presented is not personalized to theusers context, impoverishing its potential value. An illustrative and persuasive example is the less-than-compelling mobile Web experience. Attention is at a premium in mobile environments and requires radi-cally different design (Brandt 2007). Context determines what people direct attention to. Several systemshave explored using location, a specific component of context, for customized information access, e.g.Geonotes for location-based notes as virtual noticeboards around physical spaces (Persson 2002), InfoRa-dar to promote group and public interactions in physical spaces (Rantanen 2004), Cybreminder for loca-tion-based reminders (Dey 2000) and comMotion for location-based personal notes created and retrievedby speech (Marmasse 2000). These systems were developed independently of the Web, which was untilnow not reliably available for mobile devices. The recent advance of geotagging(http://en.wikipedia.org/wiki/Geotagging ) on the Web of websites, photos, blogs, RSS feeds and the in-troduction of geographical annotation languages like KML (introduced initially for Google Earth) hasvastly increased the extent of location-based information currently available.

What is required now are ways to tap the Web as an unparalleled medium for content creation and inter-action by making such information accessible on mobile devices in a way that is adapted to and appropri-ate for the user's context. The mechanism and interface for information access must be designed with con-sideration to the limited attention people have in mobile environments. Even using contextual informa-tion, we expect that attention-scarcity during mobility will require a careful choice of which informationitems to present to people (Brandt 2007). Although we have focused our discussion on the mobile Web,

context is also highly salient for information retrieval on the desktop. The context of information creation,including location, intention, actions, often serves as important retrieval cue for email (Ducheneaut 2004)and documents (Blanc-Brude 2007). However, it is currently poorly used for retrieving and indexing of documents and other information on personal computers.

Facilitating and motivating knowledge sharingAn important part of a healthy information ecosystem is obtaining high-quality contributions from peopleand getting people who have valuable information to share it. The online community is most effective if consumers provide feedback on the quality information they receive and the value received from pro-ducer(s). In this way, consumers also have a role as producers to enable "crowd wisdom" development of quality and reputation measures. This feedback requirement poses a cost on the consumer to evaluate con-tent but mainly benefits the community as a whole through improved accuracy of assessment of contentand producers.

In organizations, much of the most valuable expertise people carry is in the form of tacit knowledge thatis embedded in specific situations or environments and difficult to extract (Brown 1998). Experts simplycannot codify much of this knowledge formally in any knowledge base, leading Hinds and Pfeffer to con-clude that traditional knowledge bases generally capture information or data rather than knowledge orexpertise (2003, p. 21). A shortcoming of traditional knowledge management systems is the level of formality of knowledge that must be codified to fit in a KM database (Hinds 2003). Formality often corre-


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lates with effort involved, making it time-consuming to contribute to traditional knowledge bases. Butthere are lots of other valuable resources that can be shared, many offering a window into people's tacitknowledge.

In addition, experts tend to describe their domain knowledge in more abstract conceptual terms, whilenovices tend to express their questions in more concrete terms (Sternberg 1997). This makes it difficultfor information stored in a knowledge base by experts to be located and retrieved by novices. Simplekeyword searches are often ineffective in locating relevant resources and people, suggesting that higher-level topic detection and language processing may be needed.

Enterprises are an interesting and relevant microcosm of these issues, because there are often additionalorganizational disincentives to sharing information (Hinds 2003, Argote 1999, Fisher 1997). Pay-for-performance rewards pit workers against each other, formalized channels for knowledge management aretoo rigid, and theres often little reward for time expended helping others (Hinds 2003). Such contribu-tions undoubtedly benefit the enterprise but are hard to quantify, and so recognizing meaningful contribu-tions is difficult. A large organization has a multitude of potential people and resources to explore. A newpost is made to one of HPs collaborative forums once every five minutes, so effective ways to filter andrecommend content and people are required.

An important consequence of "Web 2.0" technologies is enabling shift from classical document-centriccollaboration to community-centric collaboration. Holbrook et al. (2008) argue that email is only suitablefor point-to-point communication, rather than team collaboration. The new generation of computer-supported collaboration tools will enable not only knowledge reuse but the support of distributed commu-nities. IBM Research has explored the use of internal bookmark sharing (Millen 2006) and people tagging(Muller 2006).

Communities, particularly involving large groups of people with easy entry and exit, face a free riderproblem where users are tempted to benefit from the production of others while contributing little or noth-ing in return (Hardin 1968). If many people choose to free ride, the quality of the content and user feed-back decreases dramatically. An example is file sharing on Gnutella (Adar 2000). Status is one powerful

approach to overcome this free rider problem (Loch 2000). Status hierarchies are pervasive in groups, andtheir basis have been attributed to at least four causes functionalism (Bales 1953), exchange theory(Blau 1964), symbolic interactionism (Stryker 1985), and dominance-conflict (Ridgeway 1995). Thesetheories differ in the extent to which status hierarchies are viewed as cooperative or competitive behav-iors, and whether they benefit the groups productivity. Nevertheless, people strive for status in a groupeven at some monetary cost (Huberman 2004). Status is particularly appropriate for online communitieswith readily available quantitative measures of contribution or consumer ratings, which may be used tohighlight the top individual contributors.

The success of one of the biggest online community projects, the online encyclopedia Wikipedia, is a major example among voluntary initiatives and has thus attracted considerable research. A Web-based sur-vey uncovered that the two single most important motivations to contribute were fun and ideology ("I

think information should be free."), trumping other reasons such as career and social motives ("People I'mclose to want me to write/edit in Wikipedia.") (Nov 2007). Altruism as displayed by Wikipedians is ratherthe exception than the rule, and designed incentive structures for contribution such as comparisons withpeers' performance do not always have the anticipated effect either (Harper 2007). Thus it remains an im-portant question to identify conditions where motivations such as altruism and status are sufficient to en-courage content creation.


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1.2 Problem Statement Our research goal is to improve the value that users get from the cloud in an increasingly mobile, con-nected and information-rich world. We will do this by improving our understanding of how information iscreated, evaluated and consumed online, and by designing, constructing and validating innovative sys-tems which will confer a market advantage to HP. Specifically, we aim to address the following prob-

lems: Attention Allocation : understanding attention allocation in information-rich environments and

its interplay with content novelty and popularity, user history and social standing and limited timeresources; utilizing this understanding to optimize the presentation of information via dynami-cally configured displays.

Context : understanding how context affects the value of information, particularly in mobile set-tings; designing and developing services for context-aware personalized information access andcreation in mobile scenarios.

Facilitating and motivating knowledge sharing : understanding and developing tools to facili-tate and incentivize knowledge sharing and harvesting through mechanisms of attention, status,and reputation.

1.3 Technical Challenges Many of the challenges we will face are typical of interdisciplinary research, in that they require expertisein a wide range of skills and domains. In particular, our research requires an integration of: (1) mathe-matical and statistical skills to produce accurate and relevant models describing underlying economic orbehavioral patterns, (2) expertise in designing, executing and analyzing the results of controlled labora-tory experiments, (3) proficiency in designing and building Web-based information systems, and (4) skillsto evaluate designs with end users in real-world scenarios.In addition to these typical challenge, we foresee the following technical challenges specific to the natureof our problem domain:

Obtaining relevant (and often sparse) data sets : In spite of rapidly growing online data sets, ob-taining just the kind of data required can be difficult. For example, the number of instances of specific information use by particular users or information around specific locations might besmall. This is particularly true for social network and location-based data. This sparseness limitsthe ability to infer niche user interests directly from available data. Supplementing this data withinferences of similarity among users can improve this situation, but requires both accurate modelsof user similarity and relationship information, such as social networks and context, from userdata. This data often confounds multiple relationship types as a single link between users and isonly a partial view of how users relate to and influence each other. Thus a major challenge is de-veloping techniques to make best use of the available data.

Data cleaning and analysis : In working with real world data, it can be challenging to isolate therelevant effect being studied and control for other variables. Cleaning the data to ensure accurateresults is also challenging and time consuming.

Location accuracy : For location based services, limited position accuracy (e.g., from cell phonetower locations or GPS) can affect the accuracy of results provided significantly. This is one areawhere we have limited control, but where we expect the state-of-the-art to improve significantly.

Automatically determining (relevant aspects of) context : Although we will have access to lots of data from peoples devices, determining personal context, e.g. activity, and social context, e.g.social network of friends, automatically from this data is quite challenging and will need sophisti-cated and creative heuristics.


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Design for small displays in mobile context : Mobile devices are faced with particularly limitedvisual real estate and it is tough to make good interfaces for mobile devices. We will attempt toutilize the physical and situated nature of mobile devices and abstract information representationsto design natural and unobtrusive interfaces that make optimal use of the available input/outputmodalities.

Measuring quality of knowledge sharing : We will use various metrics to assess the usefulness of individual contributions, but judging the quality of the contributions and extent of knowledgesharing from a combination of metrics will be a challenge.

Organizational adoption and implementation of incentives : Successful adoption of groupwaretools relies on a subtle play of network effects and individual need. In addition, exploring and im-plementing organizational incentives can be rather tricky. We will focus primarily on social andpsychological incentives and on organizational appreciation of contributions.

1.4 Approach To address these issues we propose an interdisciplinary approach combining research in economics, in-formation science, human-computer interaction, statistics and data modeling, and social networks. Ourresearch methodology combines empirical observational studies, analytical modeling, experimental lab

studies, social network analysis, and the design and construction of prototypes validated in field studies.Specifically, we will investigate the economics of attention and develop algorithms and methods for op-timizing the presentation of information so as to maximize its value to both users and providers. In addi-tion, we will design tools for increasing the value users get from information by augmenting it with spa-tial, temporal and social contexts. Furthermore, we will investigate and design suitable incentives forpeople to create and consume content. These tools and incentives also apply to enterprise information sys-tems, with differences arising from restricting participation to members of the organization and, perhaps,their customers and business partners, while respecting the proprietary nature of the information.

A. Attention Allocation

A.1 Economics of AttentionWe intend to develop general models of attention, not as a phenomenon that happens in people's heads,but in their interactions with each other and through media. Attention in this sense is measured by the in-tensity and density of signals that relate to a particular website, article, artifact, review, research program,etc. We also want to understand how attention to novel items propagates and eventually fades amonglarge populations, a key issue for the success of products and ideas. The methodology to be followed willbe to develop analytical models with predictive power and to justify them by making measurements onvery large datasets consisting of millions of individuals attending to given news or other kinds of media.We will also consider the problem of resource allocation for advertising many products in several web-sites taking into account exposure levels. We will consider the problem of resource allocation for adver-tising many products in several websites. We will then show how one can determine the optimal alloca-tion of resources into several websites both in the case of single providers and many competitive ones.

A.2 Attention Allocation in limited-resource environmentsVirtually any online search for information can be viewed as an attempt to complete a task (whether it beresearching a new car or boning up on the latest political news). In an environment where information isabundant but time and attention are scarce, people often face the tradeoff between perfect information andthe effort needed to achieve it. So, instead they satisfice in the face of these constraints. News aggregatorsand other such tools are designed to help us view more information in less time with less effort. There islittle work, however, on how individuals choose to spread their research efforts across sources given lim-


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ited time. Nor has there been an exploration of the explicit tradeoff they make between sufficient informa-tion and sufficient effort. These questions are ripe for exploration in our experimental economics lab. An-swering these fundamental questions of how people satisfice task solutions given limited time will informmuch of our work, in particular dynamic and context-aware configuration of information displays.

A.3 Dynamic configuration of information displaysInvestigation into factors affecting attention allocation : Because people are inundated with daily mes-sages, it is of interest to understand how attention propagates and eventually fades among large popula-tions. Some key factors affecting the dynamics of user attention include novelty and popularity of the in-formation, position where the information is displayed, social influence, etc. We will build dynamic mod-els that characterize how attention changes with these factors.

Novelty and popularity : In many websites, ordering the links of a given page by their novelty can guar-antee a high degree of attention. And yet, given the role that popularity plays in attracting the attention of users, a natural question arises as to whether alternative orderings, like one giving priority to popularityover novelty, might not do better at attracting viewers to a site. We will answer this question by taking thedynamics of collective attention to a finer level of detail and examining the role that popularity and nov-elty play in determining the number of clicks within a given page.

Design of algorithms to dynamically configure information displays to optimize information presen-tation : We will study different strategies that can be deployed in order to maximize attention. Twobenchmark strategies include the one that prioritizes novelty and the one that emphasizes popularity. Wewill also examine the strategy that looks myopically into the future and prioritizes stories that are ex-pected to generate the most clicks in the next few minutes. The objective of this study is to maximize thetotal number of clicks (attention) generated from an information display in a certain amount of time.

Social influence for online advertising : While it has long been believed that online advertising can bemade more efficient by exploiting social networks and social influence, quantitative proof has beenscarce. We will study the reach of social influence as it propagates in a network and to what degree it is areliable means of a possible marketing tool. For example, we will examine how the type of relationshipembodied in a network link affects the influence. These multiple relationships, often treated in the same inprior studies and on social networking web sites, include personal friendship, shared interests, shared in-formation about content or others in the network and trust in supplying reliable information. Data on ex-plicit social networks and implicit online user behavior is being made available in the public domain inever increasing quantities, with the express intent that voluntary developers will enhance the particularweb services by contributed modules (digg.com, facebook.com, imeem.com). The implications of thisdata for research and mechanism design are far-reaching, and by connecting people's social connectionswith the way they allocate their attention we will determine the degree of maximum social influence ex-erted and the circumstances under which it can be expected. Preliminary results indicate that social influ-ence works strongest in niche communities where shared interest is most homogeneous, and we plan tostudy the unconventional cases when influence crosses community boundaries where the biggest potentialfor active intervention can be realized. Interactions across such boundaries are often difficult due to lack of the shared context, experiences and trust found in small, homogeneous communities. To reduce thisbarrier, we will examine use of reputation mechanisms for establishing trust, particularly propagatedthrough the social network itself.

B. Personalization in mobile contextsWe aim to overlay the rich virtual world of content and interactions on the physical reality of the user,enhancing the users experience in both the virtual and real worlds. Specifically, we aim to develop ser-


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vices that utilize aspects of an individual's context (such as location, interests, tasks etc.) and social net-work (i.e. communities) in order to provide a unique tailored experience.

B.1 Context FilterWe will design and implement a context filter prototype for smartphones that uses various parameters,primarily location, but also e.g. social network and time to filter information searches on the (mobile)Web and desktop. These context parameters can be easily gathered from the phones GPS unit, the ad-dress book and calendar. Furthermore, we will investigate the possibility of using the users physicaltraces in time to automatically detect her intentions (visiting vs. commuting vs. planning) and incorporatethis as a parameter in the context filter. Via user studies and logging, we will attempt to determine whichaspects of the users context are most relevant to her in which situation and how these can be most effec-tively exploited. We will explore the applicability of the algorithms developed for dynamic configurationof information displays to decide which information items to highlight. We will also explore ways to in-dex information on personal computers using a core set of context parameters.

B.2 Context-based Peripheral InformationWe will supplement the users information searches by designing and implementing mechanisms to pre-

sent personalized and relevant 'peripheral' information to the user, information that is not central to theuser's activity, but is potentially useful to know in tandem with the user's activity or the information beingpresented. Utilizing the users context, peripheral information will be drawn from the Web in the form of recommendations, or will refer to the user's personal information, such as their calendar appointments orto-do lists. We will build a proof-of-concept system to present contextual information to the user in anatural and non-obtrusive manner. We will validate the system with formative and summative userevaluations in natural mobile environments.

B.3 Context ManagerWe will develop a prototype personal proxy that provides an integrated view of the users context as de-termined by the personal information device of the user. The proxy will capture various aspects of theuser's context, taking into account what the user pays attention to and analyze it to identify the user's cur-

rent context. This system will also enable the user to configure, monitor, reflect on and correct the sys-tems learning of the users context. This explicit, self-aware conceptualization of the users context willenable the device and its other applications to automatically adapt to the users preferences and habits indifferent contexts, thus supporting seamless transitions between contexts.

B.4 Mobile content creationWe will investigate mechanisms to facilitate content creation in mobile contexts. Content produced inmobile contexts includes messages, reviews, photos, videos, audio clips etc. Content creation is typicallyvery painful on small devices due to limited input modalities and visual real estate. However, knowledgeof the content creators context can greatly simplify and enrich content creation. We will use the contextmanager to augment content created with context tags. In addition, we will develop lightweight mecha-nisms for voting and posting brief messages publicly or to specific individuals or groups in physical

places, to support for example, rating or reviewing restaurants as you exit them. With knowledge of con-text, the device can determine what you are rating and appropriately add the metadata and post it to therelevant site.

C. Facilitating and motivating knowledge sharingWe aim to lower the barriers to sharing and provide incentives to motivate people to contribute. A book-mark may take only a few seconds to share; a technical report may take hours to compose. Such contentmay not necessarily be formalized, and a knowledge base may not be the appropriate forum. Simple tools


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specialized to varying formats of expression allow users to integrate these practices into their daily work.Although we focus on enterprises, many of the insights gained from this work can also be generalized toother distributed collaborative systems, such as Web forums, open source networks, or consumer applica-tions.

To answer who knows what or who does what, we will bring all shared resources together under oneroof so people can go to a centralized service to monitor their peers shared activity. It then becomesmuch easier for people to contribute to this system, since they have a variety of media. There are a varietyof potential incentives for people to share information:

Economic . People receive some sort of financial reward or bonus. Organizational . People receive higher status within an organization. (Huberman 2004) Social . People feel they are supporting a community or somehow reciprocating by participating,

or that they are building up a reputation for themselves. Psychological . People feel good about helping others, showing off their knowledge, and com-

manding the attention of their peers.

We intend to explore combinations of these incentives to find the right mix to encourage informationsharing. Perhaps an additional motivation for people to contribute information is knowing that it willeventually be used for something. We propose to explore these issues by building and evaluating systemsfor enterprise audiences.

C.1 Rewarding substantive contributionsFor any organization to institutionalize knowledge and opinion sharing as a practice it needs to be meas-urable and quantifiable. A persuasive interface to convey the value of users contributions is essential,especially in organizations that do not formally reward such contributions yet. But having tools to meas-ure not just the quantity but the quality of users participation enables organizations to evaluate the strate-gic value of expertise exchange and consider organizational incentives where appropriate. Moreover, webelieve it will increase the overall value of information being shared. For example, BRAIN used eco-nomic incentives to encourage people to honestly reveal the certainty of their beliefs, yielding more accu-rate predictions (Chen 2003). We propose to encourage valuable contributions by closing the loop be-tween information consumers and information providers, providing both implicit and explicit feedback onwhich pieces of information were deemed valuable, and by whom. In this way we're effectively "crowd-sourcing" the evaluation of a contribution's quality.

Mechanisms to reward substantive contributions include:

Explicit feedback from information consumers to authors. For instance, comments left on a blogare a form of public validation of a post's value.

Implicit feedback from observing diffusion and consumption of a contribution. For example,tracking how many people click on a post, forward it, or save it for later retrieval to identifypopular or useful items. Exposing to authors the attention given their content by consumers yieldsa psychological reward, as described in the previous section.

Moderation systems where users vote on the usefulness of an idea or comment. Both submittingcontent and voting are rewarded, to encourage people to help evaluate posts. To encourage peopleto evaluate new posts as well, larger rewards are offered for previously-unrated items. Authors of posts or comments that are favorably voted on by peers also receive greater rewards.


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C.2 Attention as incentive for peer productionAs a consequence of the recent Web 2.0 movement, a large proportion of the web content has been cre-ated by the regular users rather than a small number of experts. Thus it is of importance to understand thekey incentives for peer production on the web. One of our central hypothesis is that people making con-tributions to the web (e.g. wikipedia, digg, youtube) are rewarded by others' attention. The more attentionthey receive, the more they contribute. Conversely, insufficient attention eventually leads to a terminationof user activities. We will test this hypothesis by measuring and analyzing data from a number of popularwebsites.

C.3 Expertise locationWe will build systems that support emergent communities of practice and distributed teams across an or-ganization, by making people and their individual contributions more findable. We will investigate meth-ods to motivate and reward useful contributions and to better connect information consumers with infor-mation providers. Eventually we want to deliver personalized feeds of information and content generatedby users' peers across the organization, with tools to track the effectiveness of distribution and providefeedback to content authors. Such tools will promote knowledge reuse and distributed collaboration.

C.4 Idea evaluationWe will explore ways to tap the wisdom of large crowds to develop and evaluate ideas (e.g., new busi-ness, process improvement, etc.). These may include voting mechanisms for people to rank a set of ideasby their quality, self-moderating discussion forums for people to debate the pros and cons of ideas, andprediction markets where people place bets on worthy ideas.

This will allow a wider field of ideas to contend for the limited attention of a review board, and facilitatecritical discussion and development of nascent ideas. Potential applications within HP include the innova-tion program office (IPO) process and the Corporate Environmental Responsibility business idea contest.

2 Commercial and Business Contributions

2.1 Optimizing dynamic information presentation for websites The problem of limited attention is key to the amount of time users devote to sites and the bundles theydecide to purchase. Thus any improvement in this category can have large impact on any online type of business. To give a concrete example, HP Shopping today has annual revenues of about $800 million, sothat an improvement of a few percent in the attach rates that products generate would have a large impact.Similarly, dynamic websites such as Yahoo, NY Times, Youtube, etc. also depend on large numbers of visitors to advertise and sell content. Thus any algorithm that improves the attention that people devote tothese sites over a time period is valuable.

By better understanding the economics of attention allocation and building tools to provide users with aricher, more targeted experience, we can capitalize on a large segment of the online retail market. When

asked Thinking of your most recent online purchase, which of the following best describes how youfound the product? only 44% of consumers had both the website and product in mind. 33% had productin mind but not website and 7% had website in mind but not product (3% had neither website nor prod-uct) (North American Technographics Retail Online Survey Q3 2007). The ability to tailor web contentto these shoppers either through their search engine when choosing a retailer or by showing them poten-tially interesting product once they arrive at the retailer of choice has massive revenue potential. (NorthAmerican Technographics Retail Online Survey Q3 2007)In fact, a recent Forrester report states that, While many consumers have very specific objectives in mindwhen they visit a Web site, consumers online are less likely to browse because few enticing solutions ac-


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tually exist that enable them to replicate the process of discovery that works so effectively in stores.Recommendation engine vendors such as Certona and Aggregate Knowledge and social shopping toolslike Kaboodle are certainly steps toward resolving this problem. 34% of consumers who noticed recom-mendations purchased products based on aforementioned recommendations. (North American Techno-graphics Retail And Customer Service Online Survey Q2 2007)

Most of the current engine vendors in this space are small and privately held, which makes revenues hardto estimate. However, a recent Forrester Report (Forrester 2007) lists Aggregate Knowledge, Baynote,Certona, Criteo, and CleverSet all at revenues of $3-10m. Nearly every firm in this space is small withonly a short track record. A powerful tool released from HP is sure to give larger retailers a larger degreeof comfort than one from these fledgling startups.

2.2 Services for personalized information access in mobile environments Providing personalized services for mobile information access will be a new business for HP. The soft-ware applications capturing context data about the user will run on HP smartphones, such as the iPaQ,while the information search and filtering will take place on external HP servers. The most crucial aspect

of the service is however the actual natural and unobtrusive interface for context-based personalized in-formation. This will of course reside on the mobile information device itself.

Although the mobile Web space has been relatively neglected until now, many Web companies are mov-ing into this space. Gartner has emphasized the real world Web as one of the top 10 strategic technolo-gies for 2008, where the term refers to places where information from the Web is applied to the particu-lar location, activity or context in the real world. It is intended to augment the reality that a user faces, notto replace it as in virtual worlds. For example in navigation, a printed list of directions from the Webdo not react to changes, but a GPS navigation unit provides real-time directions that react to events andmovements. Google's Android system and Yahoo's recently released Fire Eagle(http://fireeagle.yahoo.net/ ), a service to share your location with sites and services online, enabling cus-tomized services, exemplify burgeoning interest in this field. Location-based services, in particular search

and advertising, are poised for dramatic growth (Morgan Keegan & Co. 2007)

Nokias recent acquisition of NAVTEQ, a leading digital maps provider, points to its ambitions as a loca-tion-based services company. Nokia will remain a key competitor in the context-based mobile servicesspace, however, HP can focus on and improve the mobile Web experience. In this space, Yahoo!, Googleand Microsoft are more likely competitors. However, they face the disadvantage of being Web companiesrather than hardware providers (although Google's Android mobile OS might come close), which requiresthem to store significant data about people on their servers leading to privacy concerns. HP will have adistinct advantage here in having access to the data on the user's device.

Most context-aware services require a fairly sophisticated phone with not just multimedia capabilities andaccess to Internet but also GPS or similar location-sensing capabilities. Although location-awareness is

typically associated with devices with GPS, there are companies, such as Loopt (www.loopt.com), whichoffer location-awareness services for dumb phones too. It is expected that while the aggregate industrygrowth rate for mobile phones will remain unaffected, changes in end-user perceptions will cause a redis-tribution of market share in favor of smartphones with greater capabilities and processing power (MorganKeegan & Co. 2007). The ability to effectively integrate software applications into mobile handsets in amanner that is intuitive to the end-user will prove to be the critical factor in capitalizing on the redistribu-tion occurring within the market (Morgan Keegan & Co. 2007). Since the mobile Web playing field isstill relatively level, HP has an opportunity to enter the space and capture market share with relatively low


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effort. As Google, Yahoo!, Nokia and other competitors consolidate and escalate their offerings, this win-dow may not be open for too long.

In the following calculations, we make a key assumption, that we only offer our services to devices withGPS. This need not hold, but it is a conservative lower bound on the devices we can service.

Our services are usable by people who own mobile phones that are Web-enabled and have GPS. Accord-ing to Gartner, worldwide mobile phone sales for 2007 are in the order of 1.134 billion units. Of these,today approximately 12% of handsets sold ship with a GPS chip, and that this will increase to nearly 40%by 2010, or roughly 500 million handsets (Gartner 2007). We expect our services to be most easily usableon smartphones. Market data from Gartner shows that smartphones accounted for roughly 8.8% of mobilehandset unit sales for 2006, with 83.3 million smartphones sold worldwide. This market is anticipated togrow dramatically as consumers start purchasing smartphones, at a 5-year CAGR of 54.1%, with unitsales reaching 466.4 million by 2010 (Morgan Keegan & Co. 2007). Thus, a conservative estimate of thetotal available market by 2010 for our services is around 700 million units.

We expect our services to be used primarily by young consumers who are seeking information while outand about. A mobile subscriber survey by M:Metrics in March 2007 found that browsing news and in-formation is the most-used mobile Internet application in the US (9.6% of survey respondents), UK(13.3%) and France (7.5%) and close second in Germany and Italy. Assuming very conservatively thatthis proportion stays constant, it implies that by 2010 around 67 million handsets will be used primarilyfor searching for information and news. Assuming HP captures 20% of market share as the primary appli-cation for information search, this is equivalent to about 13.5 million people. Overall, the market for loca-tion-based services was about $149 million in 2006 and is expected to grow to $3.2 billion by 2010, a116% growth rate, the highest for any mobile service besides mobile video. (Morgan Keegan & Co. 2007)

There are several possible business models for providing personalized mobile services. One possibility isto focus on our application interface on the mobile device and accordingly sell the application itself as asoftware package. Another possibility in line with HPs strategic emphasis on cloud services is to offerpersonalized contextual information as a subscribed service. The danger here is that, despite the obvious

benefits we provide in aggregating and personalizing information, people are used to the free Web ex-perience and might not be willing to pay for it when they are mobile. A variation here would be to tie upwith mobile phone carriers, such as Verizon and T-mobile. If we earn $1 per month from T-mobiles 25million subscribers by offering this service free to them, this translates into an annual revenue stream of $300 million. Note that besides our servers, there is hardly any production cost.

Finally, we could rely on the ubiquitous Internet business model of providing the service for free and thenselling the attention of our users to advertisers. Assuming people daily make 5 mobile informationsearches using our context-based services and our prime market is 13.5 million people, this is 67.5 millioninformation searches daily. If we earn about $1 per 1000 searches, this translates to a very conservativeestimate of $20.25 million per year from just mobile advertising. The market size for mobile advertisingin 2006 was about $33.2 million, but projected to be $4 billion by 2011. (Kelsey Group 2007, IDC 2007).

Mobile search advertising sales are expected to balloon from $33.2M this year to $1.4B by 2012 (IDC2007), so this might be a very profitable strategy. Given that the motivation of our work is that mobileusers have scarce attention resources, we should be very careful in how saliently we present advertising tomobile users.

2.3 Enterprise information systems According to Forrester, IBM and Microsoft currently lead in this space (Driver 2007). IBM's Lotus Con-nections is a social network in a box currently offered for sale. Connections does not attempt to filter or


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recommend content, nor is it capable of dealing with custom applications outside the Lotus system. Mi-crosofts Knowledge Network mines expertise and collaboration patterns from users email, but the pri-vacy implications are dire, requiring end users to decide which emails can be indexed from a client-sidetool. Other prominent vendors moving into this space include BEA, Oracle, and SAP (Koplowitz 2007).

Transferring expertise across an organization is difficult; a 1998 survey found that only 13% of Americanand European organizations thought they were doing a good job at this (Ruggles, 1998). Meanwhile, con-sumers have adopted distributed Internet services as a means of sharing and finding information. Web 2.0services like del.icio.us, Digg, and Facebook enable people to discover resources from their social net-works. A whole new generation is entering the workforce expecting to be able to collaborate the sameways at workmore efficiently, rapidly, and at lower cost (Koplowitz 2008).

As a result, enterprise social software is projected to be a $3 billion market by 2011 (Radicati, 2007) andto grow by 41% annually over the next four years (Eid, 2007). Worldwide, collaborative applicationsrevenue is projected to be an $8 billion market by 2011, of which $3 billion will be integrated groupwaresystems (Levitt 2007). Additionally, Gartner (2007) predicts that enterprise content management softwarewill be a $5 billion market by 2011. Forrester reports that in 2008, one in three businesses in North Amer-ica and Europe is planning to invest in "Web 2.0 tools--namely wikis, blogs, and RSS" (Young 2008).McKinsey reports that nearly half of executives familiar with Web 2.0 technologies are planning to investin collective intelligence, peer-to-peer networking, or social networking tools (McKinsey 2007). Cur-rently, most firms looking to implement social software are large enterprises of 1000 employees or more(Young 2008).

However, the market is still growing and there is still room for innovation, although the window of oppor-tunity is closing. Potential business models for commercialization include offering an integrated socialsoftware system as a service in our portfolio for our IT outsourcing customers, or as a software packagefor companies that manage their own IT. Since much of an enterprise's knowledge is proprietary, IT cus-tomers may not want to expose it outside their firewall, implying a market for custom delivery engineer-ing. Eventually it may be possible to consider gateways for companies to export limited sections of rele-vant content to business partners, providing an opportunity for HP to leverage its broad IT customer port-

folio.

3 Team

3.1 Team Members Bernardo Huberman is a Senior Fellow and Director of the Social Computing Lab. He has worked exten-sively on the nature and dynamics of the Web as well as in the design of mechanisms for harvestingknowledge from large distributed groups that are in use today. His interest in the ecology of informationled him to focus on the economics of attention as one of the key drivers for the production and consump-tion of information in the web. He holds a Ph.D. in Physics from the University of Pennsylvania.

Anupriya Ankolekar is a Visiting Scholar for Semantic Web at HP Labs for the coming year. She will fo-cus on developing personalized services for mobile information access. Her background is in human-computer interaction, online communities, especially open source software development communities,and Web technologies, especially the Semantic Web and Web services. She has experience in designingand implementing Web systems for collaboration and evaluating them in the field. She received a Ph.D.in human-computer interaction from Carnegie Mellon University. Presently she is a visiting scientist andher appointment ends in April 2009. We intend to keep her as full member of the lab.


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Mike Brzozowski' s research foci are social networks, persuasive technology, and computer-supported col-laboration. He applies user-centered interaction design and machine learning techniques to building col-laborative software systems and adaptive user interfaces. Mike holds an MS degree from Stanford incomputer science, specializing in human-computer interaction.

Leslie Fine is an applied game theorist, mechanism designer, and experimental economist. Her primaryareas of interest are in market design, incentive systems within corporations, information flows, and novelexperimental methods. Leslie received her Ph.D. from Caltech, where she studied information market de-sign and incentive compatible mechanisms.

Scott Golder has been part of the Information Dynamics Lab for nearly three years, during which time heperformed the first quantitative scholarly analysis of social tagging systems. His background is in thestudy of electronic communities and social hierarchies. He is experienced in the design of collaborativesystems, especially for group annotation and information organization. He plans to leave for school in thefall of 2008 and will thus have to be replaced with someone with a similar set of skills.

Tad Hogg will focus on modeling content creation, sharing and use in online communities, designing in-centive mechanisms and testing them experimentally. He has worked on various projects investigatingaggregate behavior of groups in economic contexts, including mechanisms for establishing reputation,adjusting risk behaviors, information aggregation and developing economic applications of emergingtechnologies such as quantum information processing. He holds a Ph.D. in physics from Stanford.

Gabor Szabo' s recent research has been centered around networks in various natural systems, whose con-nections appear random at first but share intrinsically similar statistical properties. Heavy emphasis hasbeen put on social systems (online communities and interpersonal communication networks) where heapplied stochastic modeling and computational tools to predict future behavior. He holds a PhD in phys-ics from the Budapest University of Technology.

Dennis Wilkinson has focused on quantitative and empirical studies of collaborative and peer productionsystems. This includes model fitting, theoretical analysis, and algorithm design in a variety of settings

including email networks, engineering design efforts, and online communities. His background is physics(Ph.D. Stanford) and he has experience with and a good understanding of a variety of mathematical andstatistical methods.

Fang Wu' s past research focused on social networks, mechanism design and stochastic modeling. He ispresently committed to the economics of attention, striving to understand the central role that attentionplays in many kinds of information systems. He does both empirical and theoretical work. He holds anM.S. in statistics and a Ph.D. in physics from Stanford University.

3.2 Skill sets In addition to our current team, we will need one or two design people who will bring expertise in the rep-resentation and visualization of information in mobile systems with small visual real estate, and a superband creative hacker for implementing many of the mechanisms we design. A future need for our projectmight be one or two people with expertise in data mining.


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4 Resources NeededWe have outlined a fairly large research program in this proposal. This research requires a dedicated in-terdisciplinary team of about 10-12 researchers over 4 years that will span the range from analytics toempirical studies.

Upfront costsFor our mobile personalization work, we will require about $5000 for purchasing equipment, such assmartphones and servers for providing context-aware mobile services.

Annual expensesIn order to design and validate new models in our experimental economics lab, we will require a budgetfor compensating participants. We will begin with small-scale experiments in the lab and then test themore promising ideas via field tests (involving both some software development, running a web site for awhile, and rewards for participants). While our needs may vary, we can assume two to three experimentalprojects per year, each requiring approximately 16 experiments with an average population of 16 studentseach. Our expected needs are between $40,000 and $60,000 annually. To compensate subjects in otheruser studies, used to evaluate interaction techniques and system designs, we also need about $4000 annu-ally.

5 Timeline and Key Milestones

5.1 Project timeline (1-5 years) Research ac-tivities and du-ration in per-son-years (PY)

Year 1 Year 2 Year 3 Year 4

A.1

Economicmodel of atten-tion(6 PY)

Develop general mod-els of attention basedon online interactionsand investigate howattention to novelitems propagates andfades among largepopulations.

Develop a methodol-ogy for maximizing theattach rates of consum-ers of HP shopping;Apply to the website,run field tests

A.2

Attention Allo-cation in lim-ited-resourceenvironments(3 PY)

Investigate how indi-viduals choose tospread their researchefforts across sourcesgiven limited time.

A.3

Algorithms todynamicallyconfigure in-formationdisplays tooptimize in-formation pres-entation (12 PY)

Data collection andsanitization from pub-lic-access social web-sites, with emphasison the social network of registered users andidentification of dis-tinguishing statisticalfeatures describingsocial influence.

Develop and verifyappropriate stochasticmodels to describe theobserved behavior(both for social linkingand influence propaga-tion). Design algo-rithms that dynamicallyconfigure items dis-played in a finite space

Identify characteristics of online communities that showsigns of extraordinary (positiveor negative) influence anddevelop anticipatory algo-rithms to detect and utilizeunexpected dynamic spreadingpatterns from early times. Im-plement previous algorithmsand do field tests

Integrate thesocial componentwith the dynamicdisplay configu-ration system.Transfer algo-rithms to com-mercial websites


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Research ac-tivities and du-ration in per-son-years (PY)

Year 1 Year 2 Year 3 Year 4

B.1 Context Filter(2 PY)

Design and implemen-tation of configurablecontext filter

Summative evaluation

of context filter innatural mobile contextsand investigation of which aspects of con-text are most salient forinformation searches

B.2Peripheral In-formation (3PY)

Early prototypes of peripheral informationsystem

Develop of system to presentcontextually relevant periph-eral information; draws fromYear 2 of B.1

Evaluation of system

B.3 Context Man-ager (2 PY)Initial design of con-text manager

Design and implemen-tation of context man-ager

User studies of effectiveness of context manager

B.4 Mobile ContentCreation (3 PY)Initial design of loca-tion-based messagingsystem, draws fromYear 1 work of B.3

Design and implementation of messaging and initial design of location-based aggrega-tion/voting mechanisms

User study of ease of mobilecontent creation

C.1Rewarding sub-stantive contri-butions (2 PY)

Test and implement mecha-nisms for rewarding contribu-tions. Verify the mechanismwith user studies and dataanalysis.

C.2

Attention as In-centive for PeerProduction(2 PY)

Analyze data from a number of popular websites to testwhether attention is an incen-tive for contributions

C.3 Expertise Loca-tion (4 PY)

Build tools to makeresources and peoplefindable. Verify theapproach with userstudies and ethnogra-phy.

Build tools to delivercustomizable, personal-ized information feeds.Verify the approachwith user studies andexperiments.

C.4 Idea Evaluation(2 PY)

Apply rewardmechanisms toidea generationand evaluation.Verify the ap-proach with userstudies and alarge trial.


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5.2 Milestones and deliverables at each year

Year 1A.1: Models of attention based on online interactions and investigation of how attention to novel itemspropagates and fades among large populations.A.2: Model of research effort allocation of individuals across sources given limited time.A.3: Sanitized social network data from a number of public-access social websites and identification of generic and distinguishing statistical features that describe social influence.B.1: A proof-of-concept context-based information search filtering system for mobile devices.B.3: Initial design of context manager.C.3: Tools for locating resources and people (expertise), which have been developed using formative userstudies and ethnography.

Year 2A.1: Apply methodology for maximizing the attach rates of consumers of HP shopping.A.3: Verified stochastic models to describe observed behavior (both for social linking and influence

propagation). A high-level algorithm to optimize the attention generated from an information display withspace constraint.B.1: Summative evaluation of context filter in natural mobile contexts and investigation of which aspectsof context are most salient for information searches.B.2: An early prototype of context-based peripheral information system.B.3: A proof-of-concept implementation of a context manager for personal information devices.B.4: Initial design of location-based messaging system, draws from Year 1 work of B.3.C.3: Tools to deliver customizable, personalized information feeds, which have been developed usingformative user studies and experiments.

Year 3A.3: Identification of characteristics of online communities that show signs of extraordinary (positive ornegative) influence and anticipatory algorithms to detect and utilize unexpected dynamic spreading pat-terns from early times.B.2: A proof-of-concept system to present peripheral information to the user in mobile contexts. Developof system to present contextually relevant peripheral information; draws from Year 2 of B.1B.3: User studies of effectiveness of context manager.B.4: Design and implementation of messaging and initial design of location-based aggregation/votingmechanisms.C.1: Mechanisms for rewarding contributions developed and verified through user studies and data analy-sis on the tools developed in Year 1 and 2.C.2: Investigation of attention as incentive for contributions via data analysis from popular websites.

Year 4A.3: Dynamic display configuration system with integrated social component developed in Year 3.B.2: Large-scale field study of developed context-aware personalization services in mobile environments.B.4: User study of ease of mobile content creation .C.4: Refinement reward mechanisms for idea generation and evaluation, which have been verified viauser studies and a large field trial.


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6 MetricsIn the 4 years of our research program, we will primarily measure our progress in two ways. One isthrough publications at select conferences and in select journals in the following areas:

Human-computer interaction: ACM SIGCHI Conference on Computer-Human Interaction (CHI),

International conference on Mobile Human Computer Interaction (Mobile HCI), InternationalConference on Ubiquitous Computing (Ubicomp), International Conference on Intelligent UserInterfaces (IUI), IEEE Pervasive Computing

Information systems: ACM Conference on Computer-Supported Collaborative Work (CSCW),IEEE International Conference on Computer and Information Technology (CIT), European Con-ference on Information Systems (ECIS), International Conference on Information Systems (ICIS)

Web science: ACM World Wide Web Conference (WWW), IEEE/WIC/ACM International Con-ference on Web Intelligence (WI), Journal of Web Semantics, ACM Conference on E-commerce(EC), International Conference on E-Commerce (ICEC)

Secondly, we expect to develop a number of prototypes embodying our mechanisms, algorithms and con-cepts. Our research in building systems is also likely to lead to the creation of intellectual property for HP.

Accordingly, we expect to file a number of invention disclosures and patents. Naturally, we intend totransfer our algorithms and systems to HP business units as appropriate and as soon as feasible.

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