Information Management on the WorldWide Web

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Information Management on the World-Wide Web
Junghoo John Cho UCLA Computer Science
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The Web and Information Galore
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10 Years Ago

• Reading papers for research
• Stacks of papers
• Long wait

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With Web
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Challenges (1)

• Information overload
• Too much information, too little time

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Information Overload

• XML to Google
• 14 Million matching documents!
• XML to Amazon
• 464 matching books!
• Which one to read?

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Challenges (2)

• Hidden Web
• Not indexed by Search Engines
• Hidden from an average user
• Browse every site manually?

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Challenges (3)

• Transience

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Challenges (4)

• Scattered unstructured data
• All Computer Science faculty members and graduate
  students in the US?

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Projects In Our Group

• Web Archive
• Hidden Web Integration
• Page Ranking Algorithm
• User Recommendation System

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User Recommendation System

• 464 books on XML
• Which one to read?
• The one that my colleagues and friends recommend?

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Amazons Recommendation System

• 1 5 star rating by individual users
• Books can be sorted by average user rating

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My Typical Scenario

• Sort books by their average user rating
• Browse top 20 books to decide what to read

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Questions

• Is 5 star by one user better than 4.9 star by
  100 users?
• Intuitively, I prefer 4.9 star by 100 users
• More reliable rating
• How much can I trust the rating of a particular
  person?
• How do I know that the persons rating is reliable

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Our Approach

• Inherent quality or rating of a book
• How many users recommend the book (i.e., give
  high rating) if all users have read the book?
• More user rating ? More information on the
  quality of the book
• An average user is likely to give high rating for
  a high-quality book

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Probabilistic Rating Model

• How likely is the book of 4 star rating?
• Rating probability distribution

Probability density
Book rating/quality
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Update of Rating Probability

• As more users provide rating, we update our
  probability distribution

Probability density
Book rating/quality
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Update of Rating Probability

• As more users provide rating, we update our
  probability distribution

After five-star rating by a user
Probability density
Book rating/quality
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Update of Rating Probability

• As more users provide rating, we update our
  probability distribution

After one-star rating by a user
Probability density
Book rating/quality
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Update of Rating Probability

• As more users provide rating, we update our
  probability distribution

After many ratings
Probability density
Book rating/quality
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Bayesian Inference Theory

• Given a user rating UR, what is the inherent
  rating IR?

)
(
)

(
IR
P
IR
UR
P

)

(
UR
IR
P
)
(
UR
P
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User Model

• The characteristics of a user

• Sensitivity Slope of the curve
• 1 good, 1 bad, 0 not useful

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User Model

• The characteristics of a user

• Bias Average height of the curve

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Iterative Model Refinement

• As more users rate a book, we get better
  estimates on book quality
• As we estimate a book quality better, we get
  better idea on a users sensitivity and bias

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Iterative Model Refinement
Book Rating Estimate
User-provided Rating
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Final Recommendation

• Recommend the book with the highest expected
  rating

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Initial Results

• Our system prefers a 4.9-star book by 100 people
  to a 5-star book by 1 user
• If a user gives random ratings, the system
  ignores the users rating
• More thorough evaluation on the way

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Other Projects

• Web Archive
• Hidden Web Integration
• Page Ranking Algorithm

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Ph.D. Students on the Projects
Rob Adams

• Alex Ntoulas

Victor Liu

• In Dr Chus group

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Thank You

• Questions?