Abstract
Recommender systems are considered as a promising approach to solve the problem of information overload. In collaborative filtering recommender systems, one of the most accurate and scalable algorithms is matrix factorization. As an alternative to this popular latent factor model, Euclidean embedding model presents the relationship between users and items intuitively, and generates recommendations fast. In this paper, a temporal Euclidean embedding (TEE) model is proposed by incorporating temporal factors of rating behavior. Through experiments on Netflix and Movielens data sets, we show the improvement of prediction accuracy, while keeping the efficiency of recommendation generation.
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References
Candillier, L., Meyer, F., Boulle, M.: Comparing State-of-the-Art Collaborative Filtering Systems. In: Perner, P. (ed.) MLDM 2007. LNCS (LNAI), vol. 4571, pp. 548–562. Springer, Heidelberg (2007)
Desrosiers, C., Karypis, G.: A comprehensive survey of neighborhood-based recommendation methods. In: Handbook on Recommender Systems. Springer, Heidelberg (2009)
Kurucz, M., Benczúr, A., Csalogány, K.: Methods for large scale SVD with missing values. In: KDD 2007: Netflix Competition Workshop (2007)
Peterek, A.: Improving regularized singular value decomposition for collaborative filtering. In: KDD 2007: Netflix Competition Workshop (2007)
Takacs, G., Pilaszy, I., Nemeth, B., Tikk, D.: Matrix factorization and neighbor based algorithms for the Netflix Prize problem. In: Proc., 2008 ACM Conference on Recommender Systems (RECSYS 2008), pp. 267–274 (2008)
Borg, I., Groenen, P.: Modern multidimensional scaling. Springer, New York (1997)
Khoshneshin, M., Nick Street, W.: Collaborative filtering via Euclidean embedding. In: Recsys 2010 (2010)
Cox, M.A.A., Cox, T.F.: Multidimensional scaling. In: Handbook of Data Visualization. Springer Handbooks of Computational Statistics, vol. III, pp. 315–347 (2008)
Fodor, I.K.: A survey of dimension reduction techniques. Technical Report UCRL-ID-148494, Lawrence Livermore National Laboratory (2002)
Fisher, D., Hildrum, K., Hong, J., Newman, M., THomas, M., Vuduc, R.: Swami.: A framework for collaborative filtering algorithm development and evaluation. In: SIGIR 2000, Citeseer (2000)
Bennet, J., Lanning, S.: The Netflix Prize. In: KDD Cup and Workshop (2007), www.netflixprize.com
Koren, Y.: Collaborative filtering with temporal dynamics. In: KDD 2009 (2009)
Lathia, N., Hailes, S., Capra, L., Amatriain, X.: Temporal diversity in recommender systems. In: SIGIR 2010 Proceeding of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval (2010)
Ding, Y., Li, X.: Time weight collaborative filtering. In: Proc. 14th ACM International Conference on Information and Knowledge Management (CIKM 2004), pp. 485–492 (2004)
Lu, Z., Agalwal, D., Dhillon, I.S.: A spatio-temporal approach to collaborative filtering. In: Proceedings of the 3rd ACM Conference on Recommender Systems, RecSys 2009, pp. 13–20. ACM, New York (2009)
Xiong, L., Chen, X., Huang, T.-K., Schneider, J., Carbonell, J.G.: Temporal collaborative filtering with Bayesian probabilistic tensor factorization. In: Proceedings of SIAM Data Mining 2010 (2010)
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Yin, L., Wang, Y., Yu, Y. (2012). Collaborative Filtering via Temporal Euclidean Embedding. In: Sheng, Q.Z., Wang, G., Jensen, C.S., Xu, G. (eds) Web Technologies and Applications. APWeb 2012. Lecture Notes in Computer Science, vol 7235. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29253-8_44
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DOI: https://doi.org/10.1007/978-3-642-29253-8_44
Publisher Name: Springer, Berlin, Heidelberg
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