Collaborative Information Retrieval

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Collaborative Information Retrieval

• Collaborative Filtering systems
• Recommender systems
• Information Filtering
• Why do we need CIR?
• IR system augmentation
• Filtering
• Focusing on the user
• People-centric view of data
• Linking users by interests

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Recommender Systems

• Broader term than CF, may not be explicitly
  collaborating
• We get recommendations every day
• Types of recommendations
• Implicit
• Explicit
• Properties of recommendations
• Identity
• Experts
• Use of recommendations
• Aggregation from data
• Leveraging naturally occurring factors

3
Recommendation Issues

• How do you get people to cooperate?
• How good can the recommendations be?
• Find things youd never find?
• Step savings, information navigation
• Volume of recommendations vs. number of
  recommendable items?
• How accurate can the recommendations be?
• Initially
• Overall
• Over time
• What about changing interests?

4
Social Issues

• Who controls the sharing?
• Who controls the controls?
• Give to get systems
• Anonymity vs. Community
• Community of friends
• People as data points
• Free riders
• Logrolling and Over-rating

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Information Filtering IR

• How about filtering, without the collaboration?
• Individual preferences
• Implicit and Explicit
• Text is analyzed
• Feature extraction
• Recall precision measures
• Vector space identified
• Relevance Feedback
• Matched with user or rating
• Attributes are matched or added to queries

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Two sides of the same coin?

• Filtering is removing data, IR is finding data
• Dynamic datasets
• Profile-based - preferences
• Repeated use of the system, long term interests
• Precision Recall of profiles, not info?
• Different needs motivations
• Less interactive than (Web) IR?

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Community Centered CF

• What is a community?
• Helping people find new information
• Mapping community (prefs?)
• Rating Web pages
• Recommended Web pages
• Measuring recommendation quantity?
• Measuring recommendation use
• Constant status

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Community CF

• Personal relationships are not necessary
• What does this miss?
• If you knew about the user, would that help with
  the cold start problem?
• Advisors Trust
• Ratings
• Population wide
• Advisors
• Weighted sum
• How would an organization use this?

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Contexts for Implicit Ratings

• Who
• When
• What
• How (discovery)
• Web Browsing
• RSS Reading
• Blog posting
• Newsgroup- listserv use

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Social Affordance Implicit

• How can you not use ratings?
• Read wear, clicks, dwell time, chatter
• Not all resources are as identifiable
• Granular- Web pages
• Items - commercial products
• Web is a shared informaiton space without much
  sharing
• How do incent people to contribute?
• Social norms
• Rewards

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Contexts for Explicit Ratings

• Movies
• Books
• (Junk) mail
• eBay transactions
• Other content

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PHOAKS

• Wider group of people (anyone?)
• Usenet link mining for Web resources
• Raters Users
• Precision (88) - belong in category
• Recall (87) - rules classify as category
• What counts as a recommendation?
• More than one mention?
• Positive negative?
• Fair and balanced for a Community
• How do you rank resources?
• Weights
• Topics

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Fab

• Beyond black box content
• Combining recommendations content
• Tastes in the past future likes
• Identifies emerging interests
• Group awareness
• Communication (feedback)
• Profiles of content analysis compared
• Users own profile can recommend
• Relation between users can recommend
• User profile multiple interests
• Content profile static interest
• Both may change
• Items are continually presented to users

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Future Issues in Collab IR

• It may be more interesting to find a like mind
  than a resource recommendation
• Social Networking
• Ad hoc group discussions
• Allowing users control over their profile of
  interests
• Over time
• Privacy
• Difficult to capture interests
• Working with diverse content or user interests
• Visualization of recommendations areas

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Collaboration

• How important is it to be able to collaborate?
• Add to your own intelligence
• Know about other things you dont know about
• What are the best scenarios for collaboration for
  Information Retrieval?
• Privacy
• Commerce
• Consistency

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Is Filtering a Necessary Evil?

• What are the Costs of Content Filtering?
• Do you want filtering?
• What kind of filters?
• Who should control them?
• What is the importance of accuracy for filtering?
• Metadata
• Usage and appropriate content (not just for
  childern)
• Sharing filtering?

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Bonus Work

• Up to 4 points on your final course average
• Size of the project
• Quality of project work
• Individual work
• Bibliography building highlight reviews
• Collaborative Filtering since 1998
• Information Seeking in Financial Environments
• IR Agents since 1999
• IR resources organization taxonomy