Abstract
Collaborative filtering is the most successful recommender system technology to date. It has been shown to produce high quality recommendations, but the performance degrades with the number of customers and products. In this paper, according to the feature of the rating data, we present a new similarity function Hsim(), and a signature table-based Algorithm for performing collaborative filtering. This method partitions the original data into sets of signature, then establishes a signature table to avoid a sequential scan. Our preliminary experiments based on a number of real data sets show that the new method can both improve the scalability and quality of collaborative filtering. Because the new method applies data clustering algorithms to rating data, predictions can be computed independently within one or a few partitions. Ideally, partition will improve the quality of collaborative filtering predictions. We’ll continue to study how to further improve the quality of predictions in the future research.
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References
Karypis, G.: Evaluation of Item-based Top-N Recommendation Algorithms. In: Proceedings of the Tenth International Conference on Information and Knowledge Management (CIKM), Atlanta (2001)
Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L., Riedl, J.: GroupLens: Applying Collaborative Filtering to Usenet News. Communications of the ACM, 40(3) (1997) 77–87
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In: Proceedings of CSCW’94, Chapel Hill, NC (1994)
Sarwar, B., Karypis, G., Kinstan, J., Riedl, J.: Analysis of Recommendation Algorithms for E-commerce. In: Proceedings of ACM E-commerce (2000)
Aggarwal, C.C., Wolf, J.L., Yu, P.S.: A New Method for Similarity Indexing of Market Basket Data. In: Proceedings Of the ACM SIGMOD Conference (1999) 407–418
Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules Between Sets of Items in Large Databases. In: Proc. Of the ACM-SIGMOD Int. of Conf. on Management of Data. Washington D.C. (1993) 207–216
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proc. of the 20th VLDB Conference. Santiago Chile (1994) 487–499
Karypis, G., Aggarwal R., Kumar, V., Shekhar, S.: Multilevel Hypergraph Partitioning: Application in VLSI Domain. In: Proc. ACM/IEEE Design Automation Conference (1997)
Roussopoulos, N., Kelley, S., Vincen, F.: Nearest Neighbor Queries. In: Proc. Of the ACM SIGMOD Conference Procceedings. San Jose CA (1995) 71–79
ACM.: Special Issue on Information Filtering. Communciations of the ACM, 35(12) (1992)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence(UAI-98), San Francisco (1998) 43–52
Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using Collaborative Filtering to Weave an Information Tapestry. Communications of the ACM, 35(12) (1992) 61–70
Billsus, D., Pazzani M.J.: Learning Collaborative Information Filters. In: Proceedings of ICML (1998) 46–53
Kitts, B., Freed, D., Vrieze, M.: Cross-sell: A Fast Promotion-tunable Customer-item Recommendation Method Based in Conditional Independent Probabilities. In: Proceedings of ACM SIGKDD International Conference (2000) 437–446
Wolf, J., Aggarwal, C., Wu, K., Yu, P.: Horting Hatches and Egg: A New Graph-theoretic Approach to Collaborative Filtering. In: Proceeding of ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (1999)
Good, N., Schafer, B., Konstan, J., Borchers, A., Sarwar, B., Herlocker, J., Riedl, J.: Combining Collaborative Filtering with Personal Agents For Better Recommendations. In Proceedings of the AAAI-’99 conference (1999) 439–446
Sarwar, B.M., Konstan, J.A., Borchers, A., Herlocker, J., Miller, B., Riedl, J.: Using Filtering Agents to Improve Prediction Quality in the GroupLens Research Collaborative Filtering System. In: Proceedings of CACW’98, Seattle, WA (1998)
Sarwar, B.M., Karypis, G., Konstan, J.A. Riedl, J.: Application of Dimensionality Reduction in Recommender System-A Case Study. In ACM WebKDD 2000 workshop (2000)
Beyer, K., Goldstein, J., Ramakrishnan, R., Shaft, U.: When is Nearest Neighbors Meaningful? In: ICDT Conference. Jerusalem Israel (1999) 217–235
Yang, F.Z., Zhu, Y.Y., Shi, B.L.: An Efficient Method for Similarity Search on Quantitative Transaction Data. Technique Report. Department of Computing and Information Technology, Fudan University (2002).
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Yang, F., Zhu, Y., Shi, B. (2003). A New Algorithm for Performing Ratings-Based Collaborative Filtering. In: Zhou, X., Orlowska, M.E., Zhang, Y. (eds) Web Technologies and Applications. APWeb 2003. Lecture Notes in Computer Science, vol 2642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36901-5_25
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DOI: https://doi.org/10.1007/3-540-36901-5_25
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