1. Future Direction : Collaborative Filtering
Motivating Observations:
 Relevance Feedback is useful, but expensive
Humans don’t often have time to give positive/negative judgments on a long list of returned web pages
to improve individual searches
Effort is used once, then wasted
 want pooling and re-use of efforts access individuals
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2. Collaborative Filtering
Motivating Observations (continued) :
 Relevance ¹ Quality
Queries : bootleg CD’s NAFTA
Medical School Admissions Simulated Annealing
REM 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.
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3. build a supervised learner for quality/ NOT topic matter
Possible Solution:
build a supervised learner for quality/ NOT topic matter
Train on examples of each, learn distinguishing properties
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4. Supervised Learner for “Quality” of a Page
One Solution:
Supervised Learner for “Quality” of a Page
P(Quality|Features) in addition to topic similarity
salient features may include:
# of links
Size
How often cited
Variety of content
“Top 5th of Web” awards etc,
assessment of usage counter (hit count)
Complexity of graphics µ quality??
Prior quality rating of server
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5. 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
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6. Pool collective wisdom and compute weighted average of page rankings
One Solution:
Pool collective wisdom and
compute weighted average of page rankings
across multiple users in an affinity group
(taking into account topic relevance, quality, and other intangibles)
Hypothesis : humans have a better idea than machines of what other humans will find interesting
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7. 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
A B C D E F G 1 2 3 4
1059
1060
1061 3 1 5 7 4 2 6
Table of user rankings of web pages for a query
Web pages
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8. P(relevant to me | Pagei content) compute:
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
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9. P(relevant to me | relevant to groupg)  My similarity to the group
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
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