1. 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

2. 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 you’d 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

5. 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

6. 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?

7. 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

8. Community CF 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?

9. Contexts for Implicit Ratings - Who - When - What - How (discovery) Web Browsing RSS Reading Blog posting Newsgroup- listserv use

10. 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

11. Contexts for Explicit Ratings Movies Books (Junk) mail eBay transactions Other content

12. 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

13. 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

14. 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

15. Collaboration How important is it to be able to collaborate? - Add to your own intelligence - Know about other things you don’t know about What are the best scenarios for collaboration for Information Retrieval? - Privacy - Commerce - Consistency

16. 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?

17. 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