Collaborative filtering for social tagging systems: an experiment with CiteULike

ABSTRACT

Collaborative tagging systems pose new challenges to the developers of recommender systems. As observed by recent research, traditional implementations of classic recommender approaches, such as collaborative filtering, are not working well in this new context. To address these challenges, a number of research groups worldwide work on adapting these approaches to the specific nature of collaborative tagging systems. In joining this stream of research, we have developed and compared three variants of user-based collaborative filtering algorithms to provide recommendations of articles on CiteULike. The first approach, Classic Collaborative filtering (CCF) uses Pearson correlation to calculate similarity between users and a classic adjusted ratings formula to rank the recommendations. The second approach, Neighbor-weighted Collaborative Filtering, takes into account the number of raters in the ranking formula of the recommendations. The third approach explores an innovative way to form the user neighborhood based on a modified version of the Okapi BM25 model over users' tags. Our results suggest that both alterations of CCF are beneficial. Incorporating the number of raters into the algorithms leads to an improvement of precision, while tag-based BM25 can be considered as an alternative to Pearson correlation to calculate the similarity between users and their neighbors.

REFERENCES

• 1: Pazzani, M. and Billsus, D. 2007 Content-Based Recommendation Systems. The Adaptive Web. (May 2007), 325--341.
• 2: Schafer, J., Frankowski, D., Herlocker, J. and Sen, S. 2007 Collaborative Filtering Recommender Systems. The Adaptive Web. (May 2007), 291--324.
• 3: Massa, P. and Avesani, P. 2004 Trust-Aware Collaborative Filtering for Recommender Systems. In Proceedings of OTM Confederated International Conferences, CoopIS, DOA, and ODBASE (Agia Napa, Cyprus, Oct. 25--29, 2004). 492--508.
• 4: Tso-Sutter, K. H., Marinho, L. B., and Schmidt-Thieme, L. 2008. Tag-aware recommender systems by fusion of collaborative filtering algorithms. In Proceedings of ACM Symposium on Applied Computing (Fortaleza, Brazil, March 16 -- 20, 2008). SAC '08. ACM, New York, NY, 1995--1999.
• 5: de Gemmis, M., Lops, P., Semeraro, G., and Basile, P. 2008. Integrating tags in a semantic content--based recommender. In Proceedings of the 2008 ACM Conference on Recommender Systems (Lausanne, Switzerland, October 23 -- 25, 2008). RecSys '08. ACM, New York, NY, 163--170.
• 6: Bogers, T. and van den Bosch, A. 2008. Recommending scientific articles using citeulike. In Proceedings of the 2008 ACM Conference on Recommender Systems (Lausanne, Switzerland, October 23 -- 25, 2008). RecSys '08. ACM, New York, NY, 287--290.
• 7: Manning, C., Raghavan, P. and Schutze, H. 2008 Introduction to Information Retrieval. Cambridge University Press.
• 8: McNee, S. M., Kapoor, N., and Konstan, J. A. 2006. Don't look stupid: avoiding pitfalls when recommending research papers. In Proceedings of the 20th Anniversary Conference on Computer Supported Cooperative Work (Banff, Alberta, Canada, November 04 -- 08, 2006). CSCW '06. ACM, New York, NY, 171--180.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Selection process

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Information filtering

General Terms:
Algorithms, Experimentation, Measurement, Performance

Keywords:
collaborative filtering, recommender systems, tagging