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Information Filtering

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A Conceptual Framework for Collaborative Filtering (CF) Rating-based Methods (Breese et al. 98) Memory-based methods Model-based methods Preference-based Methods (Cohen et al. 99 & Freund et al. 98) Summary & Research Directions

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A Review of

Information Filtering

Part II: Collaborative Filtering

Chengxiang Zhai

Language Technologies Institiute

School of Computer Science

Carnegie Mellon University

Outline

•

A Conceptual Framework for Collaborative

Filtering (CF)

•

Rating-based Methods (Breese et al. 98)

–

Memory-based methods

–

Model-based methods

•

Preference-based Methods (Cohen et al. 99 &

Freund et al. 98)

•

Summary & Research Directions

What is Collaborative Filtering (CF)?

•

Making filtering decisions for an individual user

based on the judgments of other users

•

Inferring individual’s interest/preferences from that

of other similar users

•

General idea

–

Given a user u, find similar users {u

, …, u

}

–

Predict u’s preferences based on the preferences of u

…, u

CF: Applications

•

Recommender Systems: books, CDs, Videos, Movies,

potentially anything!

•

Can be combined with content-based filtering

•

Example (commercial) systems

–

GroupLens (Resnick et al. 94): usenet news rating

–

Amazon: book recommendation

–

Firefly (purchased by Microsoft?): music

recommendation

–

Alexa: web page recommendation

CF: Assumptions

•

Users with a common interest will have similar

preferences

•

Users with similar preferences probably share the

same interest

•

Examples

–

“interest is IR” => “read SIGIR papers”

–

“read SIGIR papers” => “interest is IR”

•

Sufficiently large number of user preferences are

available

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