1. KMS & Collaborative Filtering Why CF in KMS? CF is the first type of application to leverage tacit knowledge People-centric view of data Preferences matter - Implicit - Explicit Are people just data points? - Neo-Taylorism - Efficiency over Quality for data collection

2. 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

3. Community CF Community CF “Personal relationships are not necessary” What does this miss? If you knew about the user, would that help with thte cold start problem? Advisors Ratings - Population wide - Advisors - Weighted sum How would an organization use this?

4. 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

5. 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?

6. Social (Filtering) 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

7. Social Filtering Very dependent on the society (types of users) Very dependent on the information (Web pages, books, restaurants) PageRank becomes PersonRank? - Matching your interests and then using it as a filter for both other people and other items? Person, Document & Time Extracting Implicit ratings - Reading time, # of accesses - Own, rent, borrow - Amount paid vs. avg cost, time to market

8. 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

9. 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?

10. Tapestry First system to apply these ideas among a group of people Email & mailing lists - more email? Technical proof of concept architecture Queries - strengths & weaknesses Annotations are key, the extra information adds to the document for searching and knowing who has already read it How well do you have to know the other readers/raters? Difficult to use queries, small document set

11. Active Collaborative Filtering Getting the community involved “people looking for information should be able to make use of what others have already found and evaluated” p1 Allow people to send “pointers” to information to colleagues. - Link to the information with additional contextual information/comments by sharer - Keeps recommendation local - Is supported by social norms (reputation, status) Lotus Notes How is it used? - Sharing - Information “digests” - Looking at other’s bookshelves

12. Active CF Advantages Active Intent by the person who finds and evaluates a document to purposefully share with particular people - Comments are specifically for your friends & co-workers, perhaps specific people - What about for your own re-retrieval or notes? Better as people find more documents, less of a measure of popularity Leverages Gatekeeper behavior Uses “forms, views and databases” (like a list of bookmarks) then adding macros to automate or filter Online lists, email & private or group databases Good for smaller groups and smaller document sets

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. PHOAKS Wider group of people (anyone?) Usenet news (more text) Link mining for Web resources What counts as a recommendation? - More than one mention? - Positive & negative? Fair and balanced for a Community How do you rank resources? - Weights - Topics

15. Referral Web Leverage the informal network in an org - Finding help & finding helper context Using a referral chain to get expert help - Determining expertise by association - Getting help by chain of association Creates referral network automatically - How about asking? - Neither way is always accurate Uses existing networks, not help building new ones - Find a friend of a friend - Can be applied to anything people in the group are interested in Makes relationships visible

16. Social Affordance & Implicit How can you not use ratings to learn? 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

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

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

19. Active CF Classic paper issues Leveraging what others do Finding what is already found? Take advantage of universal publishing How about filtering, without the collaboration? - Individual preferences - Implicit and Explicit Is “wisdom” being accumulated?

20. Sharing References Pointers Packages of Information General flexibility Private and Public resources and ratings

21. Future Issues in Collaboration 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