Keynote @iSWAG2015

Capturing Insights from Users ActionsBeyond Explicit Information

michele trevisiol

michele trevisiol2015

Understanding the Users

explicit user data

structured

semi-structuredexplicit user

user generated content

structured

semi-structuredimplicit user interactions

explicit user data

structured un-structured

semi-structured

Understanding the Usersstructured

un-structured

semi-structured

explicit feedback optimal data, but very rare

(limited in applications/items/attributes)

un-structured

semi-structured

user generated content good data, common and with lot of knowledge, but often difficult to use

un-structured

semi-structured

implicit data tough data, but very common(it is often hard to understand)

user generated content good data, common and with lot of knowledge, but often difficult to use

explicit, clear and structuredstructured

understanding the users from explicit feedback

implicit, noisy and unstructuredun-structured

understanding the users from implicit feedback

navigational patternsuser behavior

item importancecontent recommendation

browsing graphreferrer graph

user generated contentunderstanding the users from their content

semi-structured

“ the users are always leaving information behind them ”

understanding the users from their contentsemi-structured

reviews / opinionscommentsmedia (images / visual content)meta-data (gps, tags, ..)interests + socialtweets / vine videos

understanding the users from their contentsemi-structured

semi-structuredUnderstanding the users from their content

beyond the scope of their action

“ the users are always leaving

Loud and Trendy: Crowdsourcing Impressions of Social Ambiance in Popular

Indoor Urban Places, CH’15

“connecting people with great local businesses”

5 stars rating explicit informationclear and easy to understand

unstructured and noisycontains extremely meaningful information

Understand User’s Taste

identify the “food words” inside the reviewunderstand the user’s opinion

build a user taste profile

build a user taste profile build a restaurant “kitchen quality” profile

user taste profile

restaurant kitchen quality

profile

user taste profile

profile

user visits a new place

user taste profile

profile

user taste profile

profile

food or menu recommendation

“Buon Appetito - Recommending Personalized Menus”, HT’14

user taste profile

profile

what the user likesthe “specialities” of the restaurant: serendipity?

food or menu recommendation

“Buon Appetito - Recommending Personalized Menus”, HT’14

How to do it?

food words sentiment

How to do it?

“Comparing and Combining Sentiment Analysis Methods”, CONS’13

WOW +1lamb

How to do it?

delicious +1

WOW +1

dauphinoise potatoes

How to do it?

Recommendation Experiment.Food/Menu Recommender

Recommendation Experiment.

[avg-sent] most frequent positive food items among the profiles (> threshold)

[user-words] user-based CF with weighted items by positive sentiments

[menu-words] frequent and good menu/item sets (Fuzzy Apriori)

[zero-sent] most frequent food items among the profiles (no sentiments)

Food/Menu Recommender

Recommendation Experiment.

[avg-sent] most frequent positive food items among the profiles (> threshold)

[user-words] user-based CF with weighted items by positive sentiments

[menu-words] frequent and good menu/item sets (Fuzzy Apriori)

[zero-sent] most frequent food items among the profiles (no sentiments)

Food/Menu Recommender

User Implicit Feedbacksemi-structuredstructured un-structured

User Implicit Feedback

un-structured

User Interactions

user’s session — what we know about the user’s behavior

User Interactions

user’s session — what we know about the user’s behaviorexternal referrer URL — where the user is coming from

User Browsing Graph

user session

User Browsing Graph

collect all browsing sessions BrowseGraph (wighted graph)

“BrowseRank: letting web users vote for page importance”, SIGIR’08 “Image Ranking Based on Users Browsing Behavior”, SIGIR’12

User Browsing Graph

“Discovering Social Photo Navigation Patterns”, ICME’12

identifying from where users are entering the website

capture users’ interest (collecting user’s browsing patterns)

User Browsing Graph

(external) referrer URL

User Browsing Graph

(external) referrer URL

Does the referrer URL tell us something about

the user’s session?

User Browsing GraphDoes the referrer URL tell us something about the user’s session?

User Browsing Graph

search engine

social network

labeling referrer URLs (top domains)

Does the referrer URL tell us something about the user’s session?

User Browsing Graph

search engine

social network

labeling referrer URLs (top domains)

Does the referrer URL tell us something about the user’s session?

classify Flickr web pages (photos, groups, profile, …)

The Predictive Power of the Referrer Domain

sample of 2 months of Flickr logs

Apache Web Logs<user_id, timestamp, referrer_url, current_url, user_agent>

~300M page views ~40M user sessions ~10M unique users

Flickr Data

From Search Engines: • standard + advanced search (CC)

From Mail: • manage friends

Visitors behave differently depending on where they come from

Users tend to perform similar sessions when coming from the same referrer class (domain)

Note: referrer URL comes for free!

Visitors behave differently depending on where they come from

Users tend to perform similar sessions when coming from the same referrer class (domain)

Note: referrer URL comes for free!

What kind of knowledge the referrer URL adds

within the BrowseGraph ?

User Browsing Graph

User Browsing Graph2 months of logs ~300M page views ~40M user sessions ~10M unique users

Flickr Data

User Browsing Graph2 months of logs ~300M page views ~40M user sessions ~10M unique users

Flickr Data

considering only photo web page

User Browsing Graph

BrowseGraph (wighted graph)

ranking of photos based on browsing behavior

“Image Ranking Based on Users Browsing Behavior”, SIGIR’12

2 months of logs ~300M page views ~40M user sessions ~10M unique users

Flickr Data

considering only photo web page

Photo Ranking Through the Browse Graph

centrality based approaches

PageRank

BrowseRank

PageRank

BrowseRank

standard approaches explicit + stats

PageRank

BrowseRank

standard approaches explicit + stats

Favorites

View Time

PageRank BrowseRank Favorites Views TimeSpent

Evaluation

internal popularity — how popular is the photo within Flickr?

Evaluation

collective attention — implicit visibility of the photo

Evaluation

external popularity — how popular is the photo outside Flickr?

Evaluation

external popularity — how popular is the photo outside Flickr?

diversity — how diverse is the ranking?

Internal Popularity

x-axis: top N results ([1,1000] images) y-axis: cumulative value of the features

legend

Internal Popularity

legend

Internal Popularity

Favorites : ranks images with highest internal engagement

legend

Photo Ranking Through the Browse GraphCollective Attention

legend

Favorites and Views are not very correlated

Photo Ranking Through the Browse GraphExternal Popularity

legend

Favorites : low correlation with external visibility

Page/BrowseRank : very high correlation —thanks to

the referrer?

Diversity

View and Time : rank better tagged photos

Diversity

View and Time : rank better tagged photos

BR and PG : rank better photos that have more tags

Visual Inspection ~OctoberArtistic Events Series Peculiar

BR 4 3 1 2

PR 4 3 2 1

Favorites 4 2 4 0

View Time 1 2 7 0

BR 4 3 1 2

PR 4 3 2 1

Favorites 4 2 4 0

View Time 1 2 7 0

BR 4 3 1 2

PR 4 3 2 1

Favorites 4 2 4 0

View Time 1 2 7 0

Artistic Events Series Peculiar

BR 4 3 1 2

PR 4 3 2 1

Favorites 4 2 4 0

View Time 1 2 7 0

Series : they capture temporary interest of few

communities very well connected inside Flickr

RecapAnalysis of the Browsing Logs

About the Referrer URL :information about the session the user is going to dounderstanding how the webpages are linked from the external world

About the BrowseGraph :discovering content “voted” by the usersextending the informativeness with the Referrer URL

About the Referrer URL : information about the session the user is going to do understanding how the webpages are linked from the external world

About the BrowseGraph : discovering content “voted” by the users extending the informativeness with the Referrer URL

About the Referrer URL : information about the session the user is going to do understanding how the webpages are linked from the external world

About the BrowseGraph : discovering content “voted” by the users extending the informativeness with the Referrer URL

Can we predict the content the user is going to consume?

un-structured

implicit information (navigational patterns)

un-structured

browsing graph (referrer graph)

un-structured

prediction / recommendation

browsing graph (referrer graph)

Browse Graph on NewsPredicting News Articles Consumption

Different Context : News Website

[1] www.theguardian.com/news/datablog/2012/jun/22/website-visitor-statistics-nielsen-may-2012-google [2] www.people-press.org/2012/09/27/section-2-online-and-digital-news-2

Different Context : News Website major portion of the overall Web traffic [1,2]

thousands of news articles per day

thousands of news articles per dayvery short sessions [3]

[3] R. Kumar and A. Tomkins. A characterization of online browsing behavior. WWW, page 561, 2010

many visitors are newcomers

many visitors are newcomerslinks to news articles shared around the Web

Yahoo News BrowseGraph

~500M pageviews

Social Network Search Engine

~500M pageviews

“Cold-start News Recommendation with Domain-dependent Browse Graph”, RecSys’14

~500M pageviews

Domain-Dependent BrowseGraph

..or just referrerGraph.

Browse Graph on NewsPredicting News Articles ConsumptionYahoo News

BrowseGraph

hypothesis : news articles consumed are differentiable by the referrer domains

BrowseGraph

implement and evaluate a recommender system based on

the referrerGraphs

BrowseGraph

sessions are very short

average number of hops during browsing sessions

BrowseGraph

very different size

BrowseGraph

very different size well connected

BrowseGraph

Nodes Overlap and Importance

BrowseGraph

Nodes Overlap and Importancehom

google

homepage

google

facebook

twitter

Jaccard Similarity of Node Sets

BrowseGraph

google

homepage

google

facebook

twitter

BrowseGraph

google

homepage

google

facebook

twitter

google

homepage

google

facebook

twitter

Kendall Between News PageRanks

BrowseGraph

Most Common Categories

BrowseGraph

different graph structuredifferent interest of the users:

individual articles (node)news articles topics

importance (PageRank ranking)

BrowseGraph

first news page

BrowseGraph

first news page

BrowseGraph

graph selection

• ReferrerGraph [ref]

first news page

BrowseGraph

graph selection

node selection• random [rnd]

random

first news page

BrowseGraph

graph selection

node selection• random [rnd] 40

first news page

BrowseGraph

graph selection

80popular

• popular [pop]

first news page

BrowseGraph

graph selection

• popular [pop]

252015

first news page

BrowseGraph

graph selection

• popular [pop]

252015

• edge [edge]

first news page

BrowseGraph

graph selection

• popular [pop]

252015

• edge [edge]

first news page

• content-based [cb](TF-IDF + similarity)

BrowseGraph

graph selection

• popular [pop]

252015

• edge [edge]

first news page

BrowseGraph

graph selection

• popular [pop]

252015

• edge [edge]

first news page

• Full Graph [full]

BrowseGraph

graph selection

• popular [pop]

252015

• edge [edge]

first news page

• Full Graph [full]• Mix Approach [mix]

Precision @1

BrowseGraph

averaged over 1,438 hourly graphs (~350K per hour)

Precision @1

BrowseGraph

MRR @3

BrowseGraph

MRR @3

BrowseGraph

About the ReferrerGraph :

BrowseGraph

prediction information of the referrer URL + collective behaviors of the users

BrowseGraph

able to capture interest of users —even for cold-start problem

BrowseGraph

able to capture interest of users —even for cold-start problem

extremely powerful in the news context

RecapUser Interaction

users browsing traces

users browsing traces BrowseGraph

prediction and

personalization

User interactionsFuture Work

Extending Implicit Signals location data (IP Address, Mobile GPS)device type (tablet vs. mobile vs. desktop)custom webpage data (Social Media, …)

Integrating User Profilelong term user informationuser’s profile changes over time (with respect to the referrer?)

Experiment Different Graphsgraph of actions instead of pageviews? (share actions, explicit activity, ads, …)

un-structured

semi-structured

structured

un-structured

capture insights from user’s content

semi-structured

structured

un-structured

capture insights from user’s content

capture insights from user’s interaction

semi-structured

structured

michele trevisiol

Questions?

micheletrevisiol.com

un-semst

michele trevisiol2015

Questions?

micheletrevisiol.com

un-semst

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