Abstract
Nowadays, products are increasingly abundant and diverse, which makes user more fastidious. In fact, user has demands on a product in many aspects. A user is satisfied with a product usually because he or she likes all aspects of the product. Even only few of his or her demands or interests did not be satisfied, the user will have a bad opinion on the product. Usually, user’s rating value for an item can be divided into two parts. One is influenced by his or her rating bias and other user’s rating for the item. The other is determined by his or her real opinion on the item. The process of rating an item can be considered as an expression of user’s psychological behavior. Based on this rating psychology, a novel collaborative filtering algorithm is proposed. In this algorithm, if one latent demand of the user is not satisfied by the item, the corresponding rating value will be multiplied by a penalty value which is less than 1. The parameters in the model are estimated using stochastic gradient descent method. Experiment results show that this algorithm has better performance than state-of-the-art algorithms.
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References
Adomavicius, G., Tuzhilin, A.: Towards the Next Generation of Re-commender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering 17, 634–749 (2005)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. In: Advances in Artificial Intelligence, pp. 1–19 (2009)
O’Connor, M., Herlocker, J.: Clustering items for collaborative filtering. In: Proceedings of the ACM SIGIR Workshop on Recommender Systems, SIGIR 1999 (1999)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Recommender systems for large-scale e-commerce: Scalable neighborhood formation using clustering. In: Proceedings of the Fifth International Conference on Computer and Information Technology (2002)
Canny, J.: Collaborative filtering with privacy via factor analysis. In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (2002)
Vucetic, S., Obradovic, Z.: Collaborative Filtering Using a Regression-Based Approach. Knowledge and Information Systems 7(1), 1–22 (2005)
Li, B., Yang, Q., Xue, X.: Can movies and books collaborate?—Cross-domain collaborative filtering for sparsity reduction. In: Proceedings of IJCAI 2009, pp. 2052–2057 (2009)
Su, X., Khoshgoftaar, T.M.: Collaborative Filtering for Multi-class Data Using Belief Nets Algorithms. In: Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence (2006)
Miyahara, K., Pazzani, M.J.: Collaborative filtering with the simple Bayesian classifier. In: Proceedings of the 6th Pacific Rim International Conference on Artificial Intelligence (2000)
Hofmann, T.: Latent semantic models for collaborative filtering. ACM Transactionson Information Systems 22(1), 89–115 (2004)
Hofmann, T., Puzicha, J.: Latent class models for collaborative filtering. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI 1999), pp. 688–693 (1999)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. Journal of Machine Learning Research 3(4-5), 993–1022 (2003)
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Application of Dimensionality Reduction in Recommender System-A Case Study. In: ACM 2000 KDD Workshop on Web Mining for e-commerce-Challenges and Opportunities (2000)
Sarwar, B., Konstan, J., Riedl, J.: Incremental singular value decomposition algorithms for highly scalable recommender systems. In: Fifth International Conference on Computer and Information Science (2002)
Funk, S.: Netflix update: Try this at home, Tech. Rep. (2006), http://www.sifter.org/~simon/journal/20061211.html
Koren, Y.: Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering Model. In: Proceedings of KDD 2008 (2008)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Goldberg, K., Roeder, T., Gupta, D., et al.: Eigentaste: A Constant Time Collaborative Filtering Algorithm. Information Retrieval 4(2), 133–151 (2001)
Chen, G., Wang, F., Zhang, C.: Collaborative Filtering Using Orthogonal Nonnegative Matrix Tri-factorization. Information Processing & Management 45(3), 368–379 (2009)
Salakhutdinov, R., Mnih, A., Hinton, G.: Restricted boltzmann machines for collaborative filtering. In: Proceedings of 24th Annual International Conferenceon Machine Learning (2007)
Zhou, T.C., Ma, H., King, I., et al.: Tagrec: Leveraging tagging wisdom for recommendation. In: Proceedings of the 2009 International Conference on Computational Science and Engineering, vol. 4, pp. 194–199 (2009)
Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems, vol. 20 (2008)
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Zhang, H., Liu, C., Li, Z., Zhang, X. (2013). Collaborative Filtering Based on Rating Psychology. In: Wang, J., Xiong, H., Ishikawa, Y., Xu, J., Zhou, J. (eds) Web-Age Information Management. WAIM 2013. Lecture Notes in Computer Science, vol 7923. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38562-9_67
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DOI: https://doi.org/10.1007/978-3-642-38562-9_67
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