Abstract
Memory-based collaborative filtering algorithms are widely used in practice. But most existing approaches suffer from a conflict between prediction quality and scalability. In this paper, we try to resolve this conflict by simulating the ”word-of-mouth” recommendation in a new way. We introduce a new metric named influence weight to filter neighbors and weight their opinions. The influence weights, which quantify the credibility of each neighbor to the active user, form accumulatively in the process of the active user gradually provides new ratings. Therefore, when recommendations are requested, the recommender systems only need to select the neighbors according to these ready influence weights and synthesize their opinions. Consequently, the scalability will be significantly improved without loss of prediction quality. We design a novel algorithm to implement this method. Empirical results confirm that our algorithm achieves significant progress in both aspects of accuracy and scalability simultaneously.
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Deshpande, M., Karypis, G.: Item-based top-N recommendation algorithms. ACM Transactions on Information Systems (TOIS) 22(1), 143–177 (2004)
Herlocker, J.L., Konstan, J.A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, pp. 230–237 (1999)
Ma, H., King, I., Lyu, M.R.: Effective missing data prediction for collaborative filtering. In: SIGIR 2007: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 39–46. ACM, New York (2007)
Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web, pp. 285–295 (2001)
Xue, G.R., Lin, C., Yang, Q., Xi, W.S., Zeng, H.J., Yu, Y., Chen, Z.: Scalable collaborative filtering using cluster-based smoothing. In: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 114–121 (2005)
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© 2009 Springer-Verlag Berlin Heidelberg
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Li, N., Li, C. (2009). Accumulative Influence Weight Collaborative Filtering Recommendation Approach. In: Chien, BC., Hong, TP. (eds) Opportunities and Challenges for Next-Generation Applied Intelligence. Studies in Computational Intelligence, vol 214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92814-0_12
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DOI: https://doi.org/10.1007/978-3-540-92814-0_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-92813-3
Online ISBN: 978-3-540-92814-0
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