Information about Webkdd2006

"A Random-Walk Based Scoring Algorithm Applied to Recommender Engines"

Presentation at WebKDD2006 conference.

Presentation at WebKDD2006 conference.

Recommending Problem Too many resources (products, movies, books, papers, web pages...) Resource filtering is time consuming Help users during the filtering process Personalized ranking of resources Suggest useful resources to a given user WebKDD 2006 Workshop on Knowledge Discovery on the Web, Aug. 20, 2006, at KDD 2006, Philadelphia, PA, USA

Too many resources (products, movies, books, papers, web pages...)

Resource filtering is time consuming

Help users during the filtering process

Personalized ranking of resources

Suggest useful resources to a given user

Problem Ingredients A set of users: U = { u 1 , u 2 , ... , u |U| } A set of items: P = { p 1 , p 2 , ... , p |P| } A set of opinions: t i,k = ( u i , p k , r i,k ) Correlation between items: C i,k = corr ( p i , p k ) NOTE: the correlation matrix has to be normalized WebKDD 2006 Workshop on Knowledge Discovery on the Web, Aug. 20, 2006, at KDD 2006, Philadelphia, PA, USA

A set of users: U = { u 1 , u 2 , ... , u |U| }

A set of items: P = { p 1 , p 2 , ... , p |P| }

A set of opinions: t i,k = ( u i , p k , r i,k )

Correlation between items:

C i,k = corr ( p i , p k )

NOTE: the correlation matrix has to be normalized

Goal User u preferences Other users’ preferences Item correlation matrix C Properly rank items in set P with respect to: WebKDD 2006 Workshop on Knowledge Discovery on the Web, Aug. 20, 2006, at KDD 2006, Philadelphia, PA, USA

User u preferences

Other users’ preferences

Item correlation matrix C

The Idea Correlation Graph obtained by Correlation Matrix Spread user preferences over the Correlation Graph WebKDD 2006 Workshop on Knowledge Discovery on the Web, Aug. 20, 2006, at KDD 2006, Philadelphia, PA, USA

Correlation Graph obtained by Correlation Matrix

Spread user preferences over the Correlation Graph

ItemRank Algorithm Iterative equation: IR ItemRank values vector for user u IR i ItemRank value for item p i d preference vector for user u About 20 iterations to converge WebKDD 2006 Workshop on Knowledge Discovery on the Web, Aug. 20, 2006, at KDD 2006, Philadelphia, PA, USA

Iterative equation:

IR ItemRank values vector for user u

IR i ItemRank value for item p i

d preference vector for user u

About 20 iterations to converge

ItemRank for Movies Movie Graph User u rates some movies (non-zero entries in d ) Correlation Graph built by co-occurrence of movies in user preference lists ItemRank suggests good movies to be watched WebKDD 2006 Workshop on Knowledge Discovery on the Web, Aug. 20, 2006, at KDD 2006, Philadelphia, PA, USA

Movie Graph

User u rates some movies (non-zero entries in d )

Correlation Graph built by co-occurrence of movies in user preference lists

ItemRank suggests good movies to be watched

Movie DataSet 943 users 1,682 movies 100,000 ratings Ratings from 1 to 5 5 standard training/test splittings WebKDD 2006 Workshop on Knowledge Discovery on the Web, Aug. 20, 2006, at KDD 2006, Philadelphia, PA, USA

943 users

1,682 movies

100,000 ratings

Ratings from 1 to 5

5 standard training/test splittings

Movie DataSet WebKDD 2006 Workshop on Knowledge Discovery on the Web, Aug. 20, 2006, at KDD 2006, Philadelphia, PA, USA

Experimental Results Macro-DOA and difference with MaxF (in %): ItemRank with binary graph:

Macro-DOA and difference with MaxF (in %):

ItemRank with binary graph:

ItemRank for Papers Paper Graph User u selects some interesting papers (non-zero entries in d ) Correlation Graph is a weighted citation graph connecting papers ItemRank suggests good reference candidates WebKDD 2006 Workshop on Knowledge Discovery on the Web, Aug. 20, 2006, at KDD 2006, Philadelphia, PA, USA

Paper Graph

User u selects some interesting papers (non-zero entries in d )

Correlation Graph is a weighted citation graph connecting papers

ItemRank suggests good reference candidates

Paper DataSet WebKDD 2006 Workshop on Knowledge Discovery on the Web, Aug. 20, 2006, at KDD 2006, Philadelphia, PA, USA

Experimental Results WebKDD 2006 Workshop on Knowledge Discovery on the Web, Aug. 20, 2006, at KDD 2006, Philadelphia, PA, USA

Performance Issues BAD NEWS ItemRank computation is user dependent Correlation Graph can be huge GOOD NEWS User clustering is possible (also soft-clustering) C matrix is sparse: efficient way to compute PageRank-like algorithms WebKDD 2006 Workshop on Knowledge Discovery on the Web, Aug. 20, 2006, at KDD 2006, Philadelphia, PA, USA

BAD NEWS

ItemRank computation is user dependent

Correlation Graph can be huge

GOOD NEWS

User clustering is possible (also soft-clustering)

C matrix is sparse: efficient way to compute PageRank-like algorithms

Future Works Scalability improvements: Users soft-clustering Efficient computation with sparse matrices Correlation matrix learning Comparison with graph regularization frameworks Applications to new datasets WebKDD 2006 Workshop on Knowledge Discovery on the Web, Aug. 20, 2006, at KDD 2006, Philadelphia, PA, USA

Scalability improvements:

Users soft-clustering

Efficient computation with sparse matrices

Correlation matrix learning

Comparison with graph regularization frameworks

Applications to new datasets

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Microsoft PowerPoint - WEBKDD2006 Author: Augusto Created Date: 8/25/2006 2:04:24 PM ...

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Unsupervised Approach for Identifying Users’ Political Orientations YoussefMeguebli 1,MounaKacimi2,Bich-LiˆenDoan ,andFabricePopineau1 1 ... (WebKDD2006 ...

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Nearest-Biclusters Collaborative Filtering Panagiotis Symeonidis Alexandros Nanopoulos Apostolos Papadopoulos Yannis Manolopoulos Aristotle University ...

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Official Full-Text Publication: Incorporating Usage Information into Average-Clicks Algorithm. on ResearchGate, the professional network for scientists.

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onKnowledgeDiscoveryontheWeb,WebKDD2006 Philadelphia, PA, USA,August 20, 2006 Revised Papers 1 3. Page 3. Series Editors

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