Abstract
Several key applications like recommender systems deal with data in the form of ratings made by users on items. In such applications, one of the most crucial tasks is to find users that share common interests, or items with similar characteristics. Assessing the similarity between users or items has several valuable uses, among which are the recommendation of new items, the discovery of groups of like-minded individuals, and the automated categorization of items. It has been recognized that popular methods to compute similarities, based on correlation, are not suitable for this task when the rating data is sparse. This paper presents a novel approach, based on the SimRank algorithm, to compute similarity values when ratings are limited. Unlike correlation-based methods, which only consider user ratings for common items, this approach uses all the available ratings, allowing it to compute meaningful similarities. To evaluate the usefulness of this approach, we test it on the problem of predicting the ratings of users for movies and jokes.
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References
Antonellis, I., Molina, H.G., Chang, C.C.: Simrank++: query rewriting through link analysis of the click graph. Proceedings of the VLDB Endowment 1(1), 408–421 (2008)
Bell, R.M., Koren, Y., Volinsky, C.: Modeling relationships at multiple scales to improve accuracy of large recommender systems. In: KDD 2007: Proc. of the 13th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 95–104. ACM, New York (2007)
Bell, R.M., Koren, Y.: Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In: ICDM 2007: Proc. of the 2007 Seventh IEEE Int. Conf. on Data Mining, pp. 43–52. IEEE Computer Society, Washington (2007)
Deshpande, M., Karypis, G.: Item-based top-N recommendation algorithms. ACM Transaction on Information Systems 22(1), 143–177 (2004)
Fouss, F., Renders, J.-M., Pirotte, A., Saerens, M.: Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Transactions on Knowledge and Data Engineering 19(3), 355–369 (2007)
Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval 4(2), 133–151 (2001)
Golub, G.H., Van Loan, C.F.: Matrix computations, 3rd edn. Johns Hopkins University Press, Baltimore (1996)
Gori, M., Pucci, A.: Itemrank: a random-walk based scoring algorithm for recommender engines. In: Proc. of the 2007 IJCAI Conf., pp. 2766–2771 (2007)
Jeh, G., Widom, J.: Simrank: a measure of structural-context similarity. In: KDD 2002: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 538–543. ACM, New York (2002)
Kim, B.M., Li, Q., Park, C.S., Kim, S.G., Kim, J.Y.: A new approach for combining content-based and collaborative filters. Journal of Intelligent Information Systems 27(1), 79–91 (2006)
Kondor, R.I., Lafferty, J.D.: Diffusion kernels on graphs and other discrete input spaces. In: ICML 2002: Proc. of the Nineteenth Int. Conf. on Machine Learning, pp. 315–322. Morgan Kaufmann Publishers Inc., San Francisco (2002)
Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J.: GroupLens: applying collaborative filtering to usenet news. Communications of the ACM 40(3), 77–87 (1997)
Kunegis, J., Lommatzsch, A., Bauckhage, C.: Alternative similarity functions for graph kernels. In: Proc. of the Int. Conf. on Pattern Recognition (2008)
Li, J., Zaiane, O.R.: Combining usage, content, and structure data to improve Web site recommendation. In: Bauknecht, K., Bichler, M., Pröll, B. (eds.) EC-Web 2004. LNCS, vol. 3182, pp. 305–315. Springer, Heidelberg (2004)
Luo, H., Niu, C., Shen, R., Ullrich, C.: A collaborative filtering framework based on both local user similarity and global user similarity. Machine Learning 72(3), 231–245 (2008)
Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW 2001: Proc. of the 10th Int. Conf. on World Wide Web, pp. 285–295. ACM, New York (2001)
Takács, G., Pilászy, I., Németh, B., Tikk, D.: Major components of the gravity recommendation system. SIGKDD Exploration Newsletter 9(2), 80–83 (2007)
Yildirim, H., Krishnamoorthy, M.S.: A random walk method for alleviating the sparsity problem in collaborative filtering. In: RecSys 2008: Proc. of the 2008 ACM Conf. on Recommender systems, pp. 131–138. ACM, New York (2008)
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Desrosiers, C., Karypis, G. (2010). A Novel Approach to Compute Similarities and Its Application to Item Recommendation. In: Zhang, BT., Orgun, M.A. (eds) PRICAI 2010: Trends in Artificial Intelligence. PRICAI 2010. Lecture Notes in Computer Science(), vol 6230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15246-7_7
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DOI: https://doi.org/10.1007/978-3-642-15246-7_7
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