Evaluation of Collaborative Filtering Algorithms for Recommending Articles on CiteULike

Evaluation of Collaborative Filtering Algorithms for Recommending Articles on CiteULike June 29th, 2009 HT 2009, Workshop “Web 3.0: Merging Semantic Web and Social Web” Dr. Peter Brusilovsky, Associate Professor Denis Parra, PhD Student School of Information Sciences University of Pittsburgh

Outline
Motivation
Methods
CCF
NwCF
BM25
The Study
Description of the Data
Results
Conclusions

Motivation
Based on information available on CiteULike :
Develop user-centered recommendations of scientific articles.
Investigate the potential of users’ tags in collaborative tagging systems to provide recommendations .
Compare the accuracy of user-based collaborative filtering methods .
Why CiteULike?
Popular collaborative tagging system more topic-oriented than delicious: article references.
Familiarity with the system.

CiteULike

Methods: CCF (1 / 2)
Classic Collaborative Filtering (CCF): user-based recommendations, using Pearson Correlation (users’ similarity) and adjusted ratings to rank items to recommend [1]

Methods: CCF (2 / 2) 3 4 1 4 4 1 1 3 3 2 5 3 4 2 1 3 2 2 5 3 3 2

Methods: NwCF (1 / 2)
Neighbor weighted Collaborative Filtering (NwCF): Similar to CCF, yet incorporates the “amount of neighbors rating an item” in the ranking formula of recommended items

Methods: NwCF (2 / 2) 3 4 1 4 4 1 1 3 3 2 5 3 4 2 1 3 2 2 5 3 3 2

Methods: BM25 (1 / 2)
BM25 : We obtain the similarity between users (neighbors) using their set of tags as “documents” and performing an Okapi BM25 (probabilistic IR model) Retrieval Status Value [2] calculation.

Methods: BM25 (2 / 2) Query terms Doc_1 Doc_2 Doc_3

The Study
7 subjects
To each subject, four lists of 10 recommendations (each list) were created (CCF, NwCF, BM25_10, BM25_20)
The four lists were combined and sorted randomly (due to overlapping of recommendations, less than 40 items)
Subjects were asked to evaluate relevance (relevant/somewhat relevant/not relevant) and novelty (novel/ somewhat novel/ not novel)

Description of the Data
Crawl CUL for 20 “center users” (only 7 were used for the study)
Annotation: tuple {user, article, tag}
Item # of unique instances users 358 articles 186,122 tags 51,903 annotations 902,711

Results (a) nDCG (b) Average Novelty (c) Precision_2@5 (d) Precision_2@10 (e) Precision_2_1@5 (f) Precision_2_1@10

Conclusions
The rating scale must be considered carefully in a CF approach.
NwCF, which incorporates the number of raters, decreases the uncertainty produced by items with too few ratings.
The tag-based user similarity approach shows interesting results, which can lead us to consider it a valid approach to Pearson-correlation when using CF algorithms.
We will incorporate more users in our future studies to make the results more conclusive.

Questions?

Bibliography
[1] Schafer, J., Frankowski, D., Herlocker, J. and Sen, S. 2007 Collaborative Filtering Recommender Systems. The Adaptive Web. (May 2007), 291-324.
[2] Manning, C., Raghavan, P. and Schutze, H. 2008 Introduction to Information Retrieval. Cambridge University Press.

Evaluation of Collaborative Filtering Algorithms for Recommending Articles on CiteULike

by Denis Parra-Santander on Jul 21, 2009

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Evaluation of Collaborative Filtering Algorithms for Recommending Articles on CiteULike — Presentation Transcript