1. 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

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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