Future Direction #3: Collaborative Filtering Motivating Observations: Relevance Feedback is useful, but expensive Humans don’t have time to give positive/negative judgements on a long list of returned web pages to improve search Effort is used once, then wasted want pooling of efforts among individuals and reuse

Collaborative Filtering Motivating Observations (continued) 2) Relevance ¹ Quality Query: bootleg CD’s Medical School Admissions REM NAFTA Simulated Annealing Alzheimer’s Many web pages can be “about” a topic (specialized unit) But there are great differences in quality of presentation, detail, professionalism, substance, etc. Possible Solution: build a supervised learnerfor quality/ NOT topic matter Train on examples of each, learn distinguishing properties

Supervised Learner for “Quality” of a Page P(Quality|Features)  independent of topic similarity salient features may include: # of links Size How often cited Variety of content “Top 5th of Web” etc, assessment of usage counter (hit count) Complexity of graphics µ quality?? Prior quality rating of server

Collaborative Filtering Problem: Different humans have different profiles of relevance/quality Query: Alzheimer’s disease Appropriate for Care Giver Relevant (High Quality) for 6th Grader Medical Researcher = A document or web page

One Solution: Pool Collective Wisdom and Compute weighted average of: ranking(pagej, Queryi) across multiple users (taking into account relevance, quality, and other intangibles However: humans have a better idea than machines of what other humans will find interesting

Collaborative Filtering Idea: instead of trying to model (often intangible) quality judgments, keep a record of previous human relevance and quality judgments Query: Alzheimer’s Users 3 1 5 7 4 2 6 Table of user rankings of web pages for a query Web pages

Solution 1: Identify individual with similar tastes (High Pearson’s coefficient on similar ranking judgments) instead of: P(relevant to me | Pagei content) compute: P(relevant to me | relevant to you)  My similarity to you * P(relevant to you | Pagei content)  Your Judgments

Solution 2: Model Group Profiles for relevance judgments (e.g. Junior High School vs. Medical Researchers) compute: P(relevant to me | relevant to groupg)  My similarity to the group * P(relevant to groupg | Pagei content)  group’s collective (avg) relevance judgments Supervised Learning