Management Science and Financial Engineering

The Korean Operations Research and Management Science Society (한국경영과학회)
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Determining Absolute Interpolation Weights for Neighborhood-Based Collaborative Filtering

  • Kim, Hyoung-Do (School of Business Administration, Hanyang Cyber University)
  • Received : 2008.03.15
  • Accepted : 2009.12.20
  • Published : 2010.09.30
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Abstract

Despite the overall success of neighbor-based CF methods, there are some fundamental questions about neighbor selection and prediction mechanism including arbitrary similarity, over-fitting interpolation weights, no trust consideration between neighbours, etc. This paper proposes a simple method to compute absolute interpolation weights based on similarity values. In order to supplement the method, two schemes are additionally devised for high-quality neighbour selection and trust metrics based on co-ratings. The former requires that one or more neighbour's similarity should be better than a pre-specified level which is higher than the minimum level. The latter gives higher trust to neighbours that have more co-ratings. Experimental results show that the proposed method outperforms the pure IBCF by about 8% improvement. Furthermore, it can be easily combined with other predictors for achieving better prediction quality.

Keywords

References

  1. Adomavicius, G. and A. Tuzhilin, "Towards the Next Generation of Recommender Systems: A Survey of the State‐of‐the‐Art and Possible Extensions," IEEE Transactions on Knowledge and Data Engineering 17, 6 (2005), 734‐749. https://doi.org/10.1109/TKDE.2005.99
  2. Ahn, H. J., "A New Similarity Measure for Collaborative Filtering to Alleviate the New User Cold‐Starting Problem," Information Sciences 178, 1 (2008), 37‐51. https://doi.org/10.1016/j.ins.2007.07.024
  3. Bell, R. M. and Y. Koren, "Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights," Proceedings of the Seventh IEEE International Conference on Data Mining (ICDM) (2007), 43‐52.
  4. Breese, J. S., D. Heckerman, and C. M. Kadie, "Empirical Analysis of Predictive Algorithms for Collaborative Filtering," Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, Wisconsin, USA, (1998), 43‐52.
  5. Burke, R., "Hybrid Recommender Systems: Survery and Experiments," User Modeling and User‐Adapted Interaction 12, 4 (2002), 331‐370. https://doi.org/10.1023/A:1021240730564
  6. Funk, S., "Netflix Update: Try This at Home," http://sifter.org/~simon/juornal/ 2001211.html.
  7. Herlocker, J. L., J. A. Konstan, L. G. Terveen, and J. Riedl, "An Algorithmic Framework for Performing Collaborative Filtering," Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, CA, USA, (1999), 230‐237.
  8. Herlocker, J. L., J. A. Konstan, A. Borchers, and J. Riedl, "Evaluating Collaborative Filtering Recommender Systems," ACM Transactions on Information Systems 22 (2004), 5‐53. https://doi.org/10.1145/963770.963772
  9. Hofmann, T., "Latent Semantic Models for Collaborative Filtering," ACM Transactions on Information Systems (TOIS) 22, 1 (2004), 89‐115. https://doi.org/10.1145/963770.963774
  10. Kim, H. D., "Collaborative Filtering by Consistency‐Based Trust Definition," Proceedings of the Joint Autumn Conference of the Korea Society of Information Technology Applications and Other Societies, Chungju, Rep. of Korea, (2007), 551‐556.
  11. Kurucz, M., A. A. Benczur, and K. Csalogany, "Methods for Large Scale SVD with Missing Values," Proceedings of KDD‐Cup and Workshop at the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, CA, USA, (2007), 31‐38.
  12. Lemire, D. and A. Maclachlan, "Slope One Predictors for Online Rating‐Based Collaborative Filtering," Proceedings of the SIAM Data Mining (SDM'05), Newport Beach, USA, (2005), 471‐476.
  13. Netflix, "Netflix Movie Dataset," http://www.netflixprize.com/.
  14. Paterek, A., "Improving Regularized Singular Value Decomposition for Collaborative Filtering," Proceedings of KDD‐Cup and Workshop at the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, CA, USA, (2007), 39‐42.
  15. Sandvig, J. J., B. Mobasher, and R. Burke, "A Survey of Collaborative Recommendation and the Robustness of Model‐Based Algorithms," Bulletin of the IEEE Computer Society Technical Committee on Data Engineering 31, 2 (2008), 3‐13.
  16. Sarwar, B. M., G. Karypis, J. A. Konstan, and J. Riedl, "Item‐Based Collaborative Filtering Recommendation Algorithms," Proceedings of the 10th Int'l Conference on the World Wide Web, Hong Kong, (2001), 285‐295.
  17. Shardanand, U. and P. Maes, "Social Information Filtering: Algorithms for Automating Word of Mouth," Proceedings of the ACM CHI Conference on Human Factors in Computing Systems, Denver, CO, USA, (1995), 210‐217.
  18. Wu, M., "Collaborative Filtering via Ensembles of Matrix Factorizations," Proceedings of KDD‐Cup and Workshop at the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, CA, USA, (2007), 39‐42.