Social Information Access

Peter Brusilovskywith Rosta Farzan, Jaewook Ahn, Sharon

Hsiao, Denis Parra, Michael Yudelson, Chirayu Wongchokprasitti, Sherry Sahebi

School of Information SciencesUniversity of Pittsburgh

http://www.sis.pitt.edu/~peterb

PAWS Lab (at UMAP 2012)

The New Web: the Web of People

http://www.veryweb.it/?page_id=27

Web 2.0: Fast Start, Broad Spread

• Term was introduced following the first O'Reilly Media Web 2.0 conference in 2004

• By September 2005, a Google search for Web 2.0 returned more than 9.5 million results

• In 2012 similar search returned over 2 billion results

http://datamining.typepad.com/data_mining/2005/12/the_rise_and_ri.html

Social Web Web 2.0

Social Web of Web 2.0?

The Social Web

Key Elements

• The Users’ Web• Collective

Intelligence: Wisdom of Crowds

• The power of the user

• Applications powered by user community

• Stigmergy

• User as a first-class participant, contributor, author

http://www.masternewmedia.org/news/2006/12/01/social_bookmarking_services_and_tools.htm

Amazon: Reviews and ratings

eBay: Driving a marketplace

Wikipedia: Providing content

Delicious: Sharing + Organization

Social Linking: Identity + Links

Publish Your Self: [Micro]Blogs

The Other Side of the Social Web

User content

User interaction

Which wisdom of crowds?

Social Information Access

Methods for organizing users’ past interaction with an

information system (known as explicit and implicit feedback),

in order to provide better access to information to the future

users of the system

Critical Questions

• What kind of past interaction to take into account?

• How to process it to produce “wisdom of crowds” ?

• In which context to reveal it to end users?• How to make wisdom of crowds useful in

this context?

Social Information Access: Contexts

Social Navigation– Social support of user browsing

Social Recommendation (Collaborative Filtering)– Proactive information access

Social Search– Social support of search

Social Visualization– Social support for visualization-based access to information

Social Bookmarking– Access to bookmarked/shared information facilitated with tags

Social Navigation: The Motivation

• Natural tendency of people to follow each otherMaking use of “direct” and “indirect

cues about the activities of othersFollowing trails

Footsteps in sand or snowWorn-out carpet

Using dogears and annotationsGiving direction or guidance

• Navigation driven by the actions from one or more “advice providers”

The Lost Interaction History

What is the difference between walking in a real world and browsing the Web? – Footprints– Worn-out carpet– People presence

What is the difference between buying and borrowing a book?– Notes in the margins– Highlights & underlines– Dog-eared pages– Opens more easily to more used places

Edit Wear and Read Wear (1992)

The pioneer idea of asynchronous indirect social navigation

Developed for collaborating writing and editing

Indicated read/edited places in a large document

Footprints (1997)

Wexelblat & Maes, 1997

Allowing users to create history-rich objects

Providing history-rich navigation in complex information space

Showing what percentage of users have followed each link

SN in Information Space:The History

History-enriched environments – Edit Wear and Read Wear (1992)– Social navigation systems

• Footprints, Juggler, Kalas

Collaborative filtering– Manual push and pull

• Tapestry, LN Recommender

– Modern automatic CF recommender systems

Social bookmarking– Collaborative tagging systems

Social Search

Social Navigation in Information Space

SynchronousCommunication in real time

AsynchronousUsing the Interaction of past users

DirectDirect communication between people

IndirectRelying on user presence and traces of user behavior

ChatsRecommendersQ/A Systems

Presence of other peopleHistory-enriched

environments

Direct

Indirect

Synchronous Asynchronous

EDUCO: Synchronous, Indirect SN

Amazon: Asynchronous, Indirect

•Compare with an Amazon review: “the remake of this movie is horrible, I recommend to watch the original version instead”

Traces of viewing and purchasing decisions is a valuable collective wisdom!

CourseAgent: Direct, Asynchronous

• Adaptive community-based course planning system–Provides social navigation through visual cues

http://halley.exp.sis.pitt.edu/courseagent/

Ratings: Raw Social Data

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Generating Social Navigation

Overall workloadAveraging over all ratings of the community

Overall RelevanceAverage does not work

Irrelevant to many but very relevant to one

Goal-centered algorithm16 rules

Trade-offs for Direct Approach

• Reasonably reliable• Feedback directly provided• No need to deduce and guess

• Explicit feedback is hard to obtain• Takes time to provide and requires commitment• “One out of a hundred”

• Social system, which extensively relies on explicit feedback need either large community of users or special approaches to motivate direct contributions

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Adding Motivation: Career Planning

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The Intrinsic Motivation Works

• Career Planning was not advertised and was not noticed and used by half of the students

• Contribution of experimental users who did not use Career planning (experimental group I) is close to control group

• Significant increase of all contributions for those who had and used Career planning (experimental group II)

More about CourseAgent

Farzan, R. and Brusilovsky, P. (2006) Social navigation support in a course recommendation system. In: V. Wade, H. Ashman and B. Smyth (eds.) Proceedings of 4th International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (AH'2006), Dublin, Ireland, June 21-23, 2006, Springer Verlag, pp. 91-100.

Farzan, R. and Brusilovsky, P. (2011) Encouraging User Participation in a Course Recommender System: An Impact on User Behavior. Computers in Human Behavior 27 (1), 276-284.

Knowledge Sea II: Indirect, Asynchronous

•Social Navigation to support course readings

Knowledge Sea II (+ AnnotatEd)

Trade-off for indirect approach

• Feedback is easy to get• Users provide feedback simply by navigating and doing

other regular actions• It works quite well

• Most useful pages tend to rise as socially important• Social navigation cues attract users

• Indirect feedback might not be reliable• A click or other action in the interface is a small

commitment, may be a result of error• “Tar pits”

• Main challenge of systems based on indirect approach: increase the reliability of indirect feedback• Better processing of unreliable events (time, scrolling)• Use more reliable events (cf. browsing vs. purchase)

Knowledge See II: Beyond clicks

• Make better use of existing feedback• Switched from click-based calculation of user

traffic to time based• Time and patterns can provide more reliable

evidence

• Added annotation-based social navigation• Annotations are more reliable• Users are eager to provide annotations and

even categorize them into positive/regular

Spatial Annotation Interface

A Spatial Annotation Interface adds social navigation on the page level

Staking a space Commenting

38BooksOnline'08

Page-level Navigation Support

Visual Cues - annotation background and borderBackground Style•Background filling Ownership •Background colorOwner’s attitude

Border style•Border color Positiveness•Border thickness# of comments•Border strokePublic or personal

39BooksOnline'08

Annotation-based SN does work

• Usage• With additional navigation

support map-based and browsing-based access emerged as the primary access way

• Effect on navigation• Significant increase of link

following (pro-rated normalized access)

• Impact• Annotation leads students

to valuable pages

Back to Motivation Issue

BooksOnline'08

Annotations are explicit actions used for implicit feedback and as with all explicit actions, it come with motivation problems.

More on KS-II and AnnotatEd

Farzan, R. and Brusilovsky, P. (2005) Social navigation support through annotation-based group modeling. In: L. Ardissono, P. Brna and A. Mitrovic (eds.) Proceedings of 10th International User Modeling Conference, Berlin, July 24-29, 2005, Springer Verlag, pp. 463-472

Farzan, R. and Brusilovsky, P. (2008) AnnotatEd: A social navigation and annotation service for web-based educational resources. New Review in Hypermedia and Multimedia 14 (1), 3-32.

Brusilovsky, P. and Kim, J. (2009) Enhancing Electronic Books with Spatial Annotation and Social Navigation Support. In: Proceedings of the 5th International Conference on Universal Digital Library (ICUDL 2009), Pittsburgh, PA, November 6-8, 2009

What is Social Search?

- Social Information Access in Search context

- A set of techniques focusing on:• collecting, processing, and organizing

traces of users’ past interactions • applying this “community wisdom” in

order to improve search-based access to information

Variables Defining Social Search

Which users?• Creators• Consumers

What kind of interaction is considered?• Browsing• Searching• Annotation• Tagging

What kind of search process improvement?• Off-line performance improvement of search engines• On-line user assistance

The Case of Google PageRank

Which users?

Which activity?

What is affected?

How it is affected?

How it improves search?

http://www.labnol.org/internet/google-pagerank-drop-stop-worrying/4835/

How Search Could be Changed?

Let’s classify potential impact by stages

Before search During search After search

Search Engines: Improve Finding

Use social data to expand document index (document expansion)

What we can get from page authors?Anchor text provided on a link to the page

What we can get from searchers?Page selection in response to the query (Scholer,

2002)Query sequences (Amitay, 2005)

What we can get from page visitors?Page annotations (Dmitriev et al., 2006)Page tags (Yanbe, 2007)

Search Engines: Improve Ranking

What we can get from page authors?Links (Page Rank)

What we can get from searchers?Page selection in response to the query (DirectHit)

What we can get from page visitors beyond seatch context?Page visit countPage tags (Yanbe, 2007; Bao, 2007)Page annotations

Combined approachesPageRate (Zhu, 2001), (Agichtein, 2006)

Using Social Wisdom Before Search

Can be done by both search engines and external interfaces

Query checking - now standardSuggesting improved/related queries

Example: query networks (Glance, 2001)

Automatic query refinement and query expansionUsing past queries and query sequences - what the user is

really looking for (Fitzpatrick, 1997; Billerbeck, 2003; Huang, 2003)

Using anchors (Kraft, 2004)Using annotations, tags

Using Social Wisdom After Search

Better ranking, link promotion• Link re-ordering using social wisdom (based on

the result selection traces by earlier searchers)

Suggesting additional results• Suggest results (or sites!) found by earlier

searchers

Providing social annotations• Link popularity, past link selection by socially

connected users

Challenges of Social Search

• Matching similar users• Number of page hits is not reliable (DirectHit failure)• Using “everyone” social data is a bad idea – need not good

pages overall, but those that match a query• Even matching with users who issue the same query is not

reliable enough – same query, very different goals!

• Reliability of social feedback• A click on a result link is not a reliable evidence of quality and

relevance• Need to do a wise mining of search sessions and sequences

• Fusing query relevance and social wisdom• Single ranking is not the best way to express two dimensions

of relevance

AntWorld: Quest-Based Approach– Quests establish similarities between users– Relevance between documents and quests is provided by

explicit feedback

Quest Approach to Social Search

Evaluation of Quest approach: SERF (Jung, 2004)– Results with recommendations were shown on over 40%

searches. – In about 40% of cases the users clicked and 71.6% of

these clicks were on recommended links! If only Google results are shown users clicked in only 24.4% of cases

– The length of the session is significantly shorter (1.6 vs 2.2) when recommendations are shown

– Ratings of the first visited document are higher if it was recommended (so, appeal and quality both better)

I-SPY: Community-Based Search

I-SPY: Mechanism

Community-query-hit matrixUser similarity defined by communities and

queriesResult selection provide implicit feedback

Other Ways to Increase Reliability

• Moving from single query to query sequences• What the user selected at the end

• Moving from page recommendation to site recommendation

White, R., Bilenko, M., and Cucerzan, S. (2007) Studying the use of popular destinations to enhance web search interaction. In: SIGIR '07, Amsterdam, The Netherlands, July 23 - 27, 2007, ACM Press, pp. 159-166

Social Search with Visual Cues

General annotation

Question

Praise

Negative

Positive

Similarity score

Document with high traffic (higher rank)

Document with positive annotation (higher rank)

Query relevance and social relevance shown separately: rank/annotation

Annotation-Based Search: Impact

Acceptance– Users noticed and applied social visual cues

• Frequency of usage - viewed more documents per query with social visual cues

– Users agreed with the need for social search• Survey results

Performance– Social Visual Cues are taken into account for

navigation• Social Navigation cues are twice as more influential in

affecting user navigation decision than high rank– Social visual Cues provide higher prediction for

page quality that high rankMore information

– Ahn, J.-w., Farzan, R., and Brusilovsky, P. (2006) Social search in the context of social navigation. Journal of the Korean Society for Information Management 23 (2), 147-165.

SIA Challenges across Contexts

• Increasing reliability of indirect sources• Time spent reading vs. simple click• Query sequences vs. simple result access

• Adding more reliable evidences of relevance/quality/interests• Annotation vs. browsing• Purchasing/downloading vs. viewing• May add the problem of motivation!

• Basis for user similarity (not “all for all”)• Co-rating in recommender systems (sparsity!)• Users with similar goals (CourseAgent)• Single class in Knowledge Sea II (still topic drift!)• Quest or community in AntWorld and iSpy

More Challenges:Merging the Technologies

• Different branches of SIA have little connections to each other• Social navigation use navigation data to assist

navigation• Social search use search traces to assist future

searchers• Many opportunities to merge two or more SIA

technologies• Social Web system with broader SIA

• Use several kinds of user traces to support a specific SIA technology

• Offer several kinds of SIA• Earlier work: Social Navigation + Social Search

– ASSIST ACM– ASSIST YouTube

• Social Navigation + Recommendation• Adding Social Visualization

ASSIST-ACM: Social Search + Nav

Re-ranking result-list based on search and

browsing history information

Augmenting the links based on search and

browsing history information

Farzan, R., et al. (2007) ASSIST: adaptive social support for information space traversal. In: Proceedings of 18th conference on Hypertext and hypermedia, HT '07,, pp. 199-208

CoMeT: Social wisdom for talks

http://pittcomet.info

Some New Ideas in CoMeT

• Broader set of evidences• View, annotate, tag, schedule talks, send to

friends, connect to peers• Declare affiliations (similarity!)• Join and post links to a set of communities

• Combining in-context (visual cues) and out-of context (ranking) guidance

• Exploring the power of “top N”• Powerful, but dangerous!

Conference Navigator Project

• Social conference support system – combining social and personalized guidance

Social Visualization with VIBE

Social Visualization in CN3

TalkExplorer: Integrating recommendations and SNS visually

Community vs. Peer-Based NS: E-learning

• Progressor and Progressor+ projects• Problem: guide students to most

appropriate educational content – examples, problems, etc.

• Using reliable indicators of student progress (problem solving success)

• Provide visualization to better support guidance

• Explore peer-based and community-based SNS

Parallel Introspective Views

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Progressor

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Progressor+

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Students spent more time in Progressor+

Quiz =: 5 hours Example : 5 hours 20 mins

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Students achieved higher Success Rate

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p<.01

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Non-adaptive adaptive

Social, adaptive, single content Progressor+

How Social Guidance Works