Connecting with the collective: self-contained reranking for collaborative recommendation

ABSTRACT

Collaborative recommendation (CR) approaches have proven effective for the Top-N recommendation task. We introduce a novel approach, Rerank-CR, that further improves the Top-N results of an arbitrary CR algorithm using a post-processing step involving Bayesian reranking. The defining characteristic of Rerank-CR is that reranking is self contained, meaning that it requires no external resources, but rather makes use of information derivable from the original user-item matrix. Rerank-CR achieves top performance when used for incorporating collection-level information reflecting global tendencies as constraints into conventional CR, which we refer to as 'connecting with the collective'. Because information about the preferences of the collective is derived directly from the dataset, Rerank-CR has no need of an explicit model of rating styles within a certain community. Further, it is possible to adapt the domain of application (e.g., change to a different cultural setting) without explicit intervention. We evaluate Rerank-CR with experiments that demonstrate the ability of the basic Rerank-CR concept to improve an initial Top-N recommendation list and also the additional improvement achieved by 'multimodal' Rerank-CR, which integrates the collective modality. Additional experiments confirm that the performance of Rerank-CR is significant across different datasets.

References

1. Adomavicius G., and Tuzhilin, A., 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowledge and Data Engineering, 17, 6, 734--749.
2. Cui, J., Wen, F. and Tang, X., 2008. Real time Google and live image search re-ranking. In Proc. ACM MM '08, 729--732.
3. Deshpande, M., and Karypis, G., 2004. Item-based top-N recommendation algorithms. ACM Trans. on Information Systems, 22, 1, 143--177.
4. Herlocker, J. L. and Konstan, J. A., 2001. Content-Independent Task-Focused Recommendation. IEEE Internet Computing, 5, 6, 40--47.
5. Herlocker, J., Konstan, J., Borchers, A., and Riedl, J., 1999. An algorithmic framework for performing collaborative filtering. In Proc. ACM SIGIR '99, 230--237.
6. Herlocker, J., Konstan, J., Terveen, L. G., and Riedl, J. 2004. Evaluating collaborative filtering recommender systems. ACM Trans. Information Systems, 22, 1, 5--53.
7. Hsu, W. H., Kennedy, L. S., and Chang, S.-F., 2006. Video search reranking via information bottleneck principle. In Proc. ACM MM '06, 35--44.
8. Kamps, J., 2005. Improving Retrieval Effectiveness by Reranking. In Proc. ECIR '05, 283--295.
9. Kim, H.-N., Ji, A.-T., Kim, H.-J., and Jo, G.-S., 2007. Error-based collaborative filtering algorithm for top-n recommendation. In Proc. Joint International Conferences on Asia-Pacific Web Conference and Web-Age Information Management, 594--605.
10. Koh, N. S., Hu, N., and Clemons, E. K. 2010. Do online reviews reflect a product's true perceived quality? - An investigation of online movie reviews across cultures. In Proceedings of the 43rd IEEE Hawaii international Conference on System Sciences, 1--10.
11. Linden, G., Smith, B., and York, J., 2003. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7, 1, 76--80.
12. Liu, T.-Y. 2009. Learning to rank for information retrieval. Foundations and Trends in Information Retrieval, 3, 3, 225--331.
13. Liu, Y., Mei, T., and Hua, X., 2009. CrowdReranking: Exploring multiple search engines for visual search reranking. In Proc. ACM SIGIR '09, 500--507.
14. Resnick, P., Iacovou, N., Suchak, M., Bergstorm, P., and Riedl, J., 1994. Grouplens: An open architecture for collaborative filtering of netnews. In Proc. of ACM CSCW '94, 175--186.
15. Sarwar, B., Karypis, G., Konstan, J., and Riedl, J., 2000. Application of dimensionality reduction in recommender system: A case study. In Proc. WebKDD Workshop.
16. Sarwar, B., Karypis, G., Konstan, J., and Reidl, J., 2001. Item-based collaborative filtering recommendation algorithms. In Proc. WWW '01, 285--295.
17. Shih, Y. and Liu, D., 2008. Product recommendation approaches: Collaborative filtering via customer lifetime value and customer demands. Expert Syst. Appl., 35, 1--2, 350--360.
18. Sigurbjörnsson, B., and van Zwol, R., 2008. Flickr tag recommendation based on collective knowledge. In Proc. ACM WWW '08, 327--336.
19. Tan, T. F., and Netessine, S., 2009. Is Tom Cruise threatened? Wharton Business School Working Notes Paper. University of Pennsylvania. Retrieved December 11, 2009. http://knowledge.wharton.upenn.edu/papers/1361.pdf
20. Tian, X., Yang, L., Wang, J., Yang, Y., Wu, X., and Hua X.-S., 2008. Bayesian video search reranking. ACM MM '08, 131--140.
21. World of hits. In: Economist. Nov 26, 2009.

Index Terms

• Information systems

  • Information retrieval

    • Retrieval tasks and goals

      • Document filtering

      • Information extraction