Human Information Retrieval Model

Information Retrieval Interaction Model

M. Abu ul FazalPhD 1stHuman Information InteractionQuaid-i-Azam University Islamabad.

Introduction

• IR emerged in 50’s and 60’s as static, batch processing systems

• In 70s, the access to IT systems became dynamic and interactive

• Later, interaction became the most important feature of IR

• IR Interaction’s means, ways, models and types still– Evolving– Changing– Improving at times

• Still, Research on interactive aspects of IR has not reached a maturity

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Introduction

• Number of interactive IR models have been proposed (some are reviewed below), and the literature on IR interaction is growing.

• the traditional IR model implies interaction, but it does not addresses the interactive processes directly – not been successful in recognizing the major

variables involved in interaction – less in evaluation of interactive aspects of IR

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Interactive IR Model Characteristics

• provide an enumeration and distinction between different kinds of interactive processes going on during IR

• enumerate the major classes of variables involved in all interactions, and in specific kinds of interactions,

• incorporate the relations to major elements in the ‘computer’ side of IR systems,

• models and definitions used in HCI research be applicable in evaluation of interactive IR

• be testable in a scientific sense

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Traditional IR Model

• Two prong set (system and user) of elements and processes converging on comparison or matching

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The central questions for IR interactions:

• What variables are involved in different kinds of interaction?

• How do they affect the process, and performance or outcomes?

• How to control them? • To what extent do certain interventions (e.g.

patterns of dialogue) improve or degrade the process and outcomes?

• Can interfaces be designed so that they give choices that will improve performances in a variety of kinds of interactions?

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Belkin’s episode model

• Nick Belkin is pioneer in advancing the interactive viewpoint in information retrieval – engaged in identification of a variety of components

and processes in information seeking by users of IR systems and other information and library services, and in classification of the interactive variables

• Belkin and colleagues undertaken development of a radically different IR interaction model by considering– real problem in IR is not how to represent texts but how

to represent the users’ Anomalous State of Knowledge (ASK),

– the cognitive and situational aspects for seeking information and approaching an IR system

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• Model is connected to cognitive processes, – it concentrates and it is based on the more specific

processes of users’ information seeking behavior.

• Considers user interaction with an IR systems as a sequence of differing interactions in an episodes of information seeking

• The central process is user’s interaction with information.

• Each of the traditional IR processes can be instantiated in a variety of ways.

• Processes enumerated as– REPRESENTATION, COMPARISON, SUMMARIZATION,

NAVIGATION and VISUALIZATION88


• User engages over time in a number of different kinds of interactions, each dependent on a number of factors, such as – user’s current task, – goals, – intentions, – the history of the episode, – the kind of information objects being interacted with, – and possibly other factors, that need to be uncovered

through observation

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• The different kinds of interactions support variety of processes such as – judgment, – interpretation, – modification, – browsing and so on.

• Belkin enunciates that the problem of IR interfaces is to devise methods and ways to optimally support different kinds of interactions and different kinds of information seeking strategies

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Strengths & Weaknesses

• Tefko Saracevic identified following Strengths and Weaknesses– Strengths

• The strength of this model is – it directly addresses interaction, – and goes on to specify that there a number of types of

interactions • Depicted greater detail of information seeking strategies and

related interactions which suggests basis that what kind of approaches are needed (including design of interfaces) to optimize interaction.

– Weakness• The scripts require a great deal of specificity for every process

and situation involved, and as such had limited practical application,

• it is not clear at all that humans actually act according to scripts

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stratified model of IR interaction (Saracevic, 1997)

• The stratified model of IR interaction (Saracevic, 1997) includes– the two-track aspect of the traditional model indicating

adaptation.

• This model accounts for multiple dimensions of user involvement in IR processes i.e.– user environment and situation, user knowledge, goals, intent,

beliefs, and tasks.

• Model improves on the traditional model by showing the complexity of a user’s environment.

• Saracevic notes “deeper level cognitive and situational aspects in interaction can and often do change – problem or question is redefined, refocused

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Stratified interaction model

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Weaknesses

• David Robins identified following weaknesses– Potential weakness of the model lies in its

• lack of description of temporal effect.

– Notwithstanding, no mention of the effects time and iteration is included in his model

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Model for Interactive Web InformationRetrieval

• this model adds two new cycles in addition to the traditional query reformulation cycle– Interactive Query Refinement

• interactive query refinement loop provide specific supplemental information based on supplemental information that is relevant to the users’ information needs.

• Provide tools to support focus on query formulation task. • Visual representations of the query information for an

interactive query refinement tool is highly recommended.• A preview of the search results should be provided. This will

help to promote an understanding of whether the queries are an accurate reflection of their information needs.

• The interactive query refinement loop should promote exploration and experimentation with the query.

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• Interactive Search Results Exploration• In order to promote an effective exploration of the web search results,

many document surrogates must be retrieved and made available to the users.

• Providing coordinated views at different levels of details can allow the users to take both a micro- and a macro-view of the web search results.

• generating visual representations of the document surrogates can allow the users to more effectively understand the features of the search results.

• As the users explore the web search results, their decisions should result in a re-ranking and subsequent re-sorting of the search results.

• Further, the exploration decisions the users make must be reflected instantly in the visual representation of the search results.

• It should be made clear that these documents have been viewed, allows the users to re-rank the search results.

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My Model

• Considering Google internet family, that is offering plenty of service to its users

• These services are– Youtube, Google Plus, Gmail, Blog Service, Google Scholar,

Google Search Engine, etc

• Viewing the diversity of Services where user interact, Google can fetch the context of User or it may be helpful to greater extent

• Considering the example quoted by my fellow ‘Model’ where user came up with nude model as a result set

• Context of the Model can be easily captured by the Google if it works on the activities performed on its fellow sites

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My Model

• In social project Google Plus I have written in about me that I am doing PhD. these days.

• I regularly visits YouTube watching latest trends in HII• Reading latest blogs also• Visiting Google scholar• Regularly doing emails to my fellow using associated

Gmail account.• Entering terms continuously queries related to HII in

Google SE• By keeping the record of each activity Google can easily

assume the context for ‘Model’• For this Google essentially need to register user and keep

them logged in.2020

My Model

• Each activity of the user may be recorded for the purpose of better service to user

• Once context of the query would be understood that organization and presentation of the result set can be improved significantly

• So essentially what I am proposing in my model is few elements which can help system to understand the context of the query

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Including Context

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User Activities•Registered at Google Family & Remains Logged in

•Google Plus

•You Tube

•Blog

•G-Mail

•- - -

• ----

•So on

Fetching Context

References

• Tefko Saracevic, PhD , Modeling interaction in information retrieval (IR): a review and proposal: Proceedings of the American Society for Information Science, vol 33

• Nicholas J. Belkin, Michael Cole, and Jingjing Liu: A Model for Evaluation of Interactive Information Retrieval, School of Communication & Information Rutgers University

• Orland Hoeber and Xue Dong Yang, A Model for Interactive Web Information Retrieval: University of Regina

• David Robins, Interactive Information Retrieval: Context and Basic Notions: Louisiana State University, School of Library and Information Science

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Documents

Human Information Retrieval Model