Abstract
Recommender Systems (RS) have been comprehensively analyzed in the past decade, Matrix Factorization (MF)-based Collaborative Filtering (CF) method has been proved to be an useful model to improve the performance of recommendation. Factors that inferred from item rating patterns shows the vectors which are useful for MF to characterize both items and users. A recommendation can concluded from good correspondence between item and user factors. A basic MF model starts with an object function, which is consisted of the squared error between original training matrix and predicted matrix as well as the regularization term (regularization parameters). To learn the predicted matrix, recommender systems minimize the squared error which has been regularized. However, two important details have been ignored: (1) the predicted matrix will be more and more accuracy as the iterations carried out, then a fix value of regularization parameters may not be the most suitable choice. (2) the final distribution trend of ratings of predicted matrix is not similar with the original training matrix. Therefore, we propose a Dynamic-MF algorithm and fine tuning method which is quite general to overcome the mentioned detail problems. Some other information, such as social relations, etc, can be easily incorporated into this method (model). The experimental analysis on two large datasets demonstrates that our approaches outperform the basic MF-based method.
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References
Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749.
Bobadilla, J., Ortega, F., & Hernando, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109–132.
Bokde, D., Girase, S., & Mukhopadhyay, D. (2015). Matrix factorization model in collaborative filtering algorithms: A survey. In Proceedings of 4th international conference on advances in computing, communication and control (pp. 136–146).
Dueck, D., & Frey, B.J. (2004). Probabilistic sparse matrix factorization. Technical report PSI TR, 2004–023.
Forsati, R., Mahdavi, M., Shamsfard, M., & Sarwat, M. (2014). Matrix factorization with explicit trust and distrust side information for improved social recommendation. ACM Transactions on Information Systems, 32(4), 1–38.
Kim, M.C., & Chen, C. (2015). A scientometric review of emerging trends and new developments in recommendation systems. Scientometrics, 104, 239–263.
Koren, Y. (2008). Factorization meets the neighborhood: A multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 426–434).
Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30–37.
Lee, D.D., & Seung, H.S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.
Lee, D.D., & Seung, H.S. (2000). Algorithms for non-negative matrix factorization. In Proceedings of neural information processing systems.
Liu, J.T., Wu, C.H., & Liu, W.Y. (2013). Bayesian Probabilistic Matrix Factorization with Social Relations and Item Contents for recommendation. Decision Support Systems, 55(3), 838–850.
Liu, W., Wu, C.H., Feng, B., & Liu, J.T. (2015). Conditional preference in recommender systems. Expert Systems with Applications, 42(2), 774–788.
Lu, L.Y., Medo, M., Yeung, C.H., Zhang, Y.C., & Zhang, Z.K. (2012). Recommender systems. Physics Reports, 519(1), 1–49.
Luo, X., Zhou, M., Xia, Y., & Zhu, Q.S. (2014). An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems. IEEE Transactions on Industrial Informatics, 10(2), 1273–1284.
Ma, H., King, I., & Lyu, M.R. (2009a). Learning to recommend with social trust ensemble. In Proceedings of the 32nd international ACM SIGIR conference on research and development in information retrieval (pp. 203–210).
Ma, H., Lyu, M.R., & King, I. (2009b). Learning to recommend with trust and distrust relationships. In Proceedings of the 3rd ACM conference on recommender systems (pp. 189–196).
Ma, H., Zhou, D., Liu, C., Lyu, M.R., & King, I. (2011a). Recommender systems with social regularization. In Proceedings of the forth international conference on web search and web data mining (pp. 287–296).
Ma, H., Zhou, D.C., Lyu, M.R., & King, I. (2011b). Improving recommender systems by incorporating social contextual information. ACM Transactions on Information Systems, 29(2), 219–230.
Marin, L., Isern, D., & Moreno, A. (2013). Dynamic adaptation of numerical attributes in a user profile. Applied Intelligence, 39(2), 421–437.
Massa, P., & Avesani, P. (2004). Trust-aware collaborative filtering for recommender systems. Lecture Notes in Computer Science, 492–508.
Massa, P., & Avesani, P. (2009). Trust metrics in recommender systems. In Computing with social trust (pp. 259–285).
Melville, P., Mooney, R.J., & Nagarajan, R. (2002). Content-boosted collaborative filtering for improved recommendations. In Proceedings of the 18th national conference on artificial intelligence (pp. 187–192).
Qian, X., Zhao, G., & Mei, T. (2014). Personalized recommendation combining user interest and social circle. IEEE Transactions on Knowledge and Data Engineerning, 26(7), 1763–1777.
Resnik, P., Iacovou, N., Suchak, M., Bergsorm, P., & Riedl, J. (1994). Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of the ACM conference on computer supported cooperative work (pp. 175–186).
Salakhutdinov, R., & Mnih, A. (2008a). Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In Proceedings of the 25th international conference on machine learning (pp. 880–887).
Salakhutdinov, R., & Mnih, A. (2008b). Probabilistic matrix factorization. In Proceedings of the 22nd annual conference on neural information processing system (pp. 1257–1264).
Sarwar, B., Karypis, G., Konstan, J., & Reidl, J. (2001). Item based collaborative filtering recommendation algorithms. In Proceedings 10th international conference on world wide web (pp. 285–295).
Shan, H., & Banerjee, A. (2010). Generalized probabilistic matrix factorizations for collaborative filtering. In The 10th IEEE international conference on data mining (pp. 1025–1030).
Sherchan, W., Nepal, S., & Paris, C. (2013). A survey of trust in social networks. ACM Computing Surveys, 45(4), 115–123.
Wierzowiecki, G., & Wierzbicki, A. (2010). Efficient and correct trust propagation using closelook. In IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology (pp. 676–681).
Zhang, Z., & Liu, H. (2015). Social recommendation model combining trust propagation and sequential behaviors. Applied Intelligence, 43(3), 695–706.
Zhang, S., Wang, W.H., Ford, J., & Makedon, F. (2006). Learning from incomplete ratings using non-negative matrix factorization. In Proceedings of the 6th slam conference on data mining (pp. 549–553).
Zhang, Z.Y., Liu Y., & Qing, A.Z. (2014). A weighted method to update network user preference profile dynamically. International Journal of Interdisciplinary Telecommunications and Networking, 6(2), 52–67.
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This work has been supported by the Fundamental Research Funds for the Central Universities 2016YJS028.
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Zhang, Z., Liu, Y., Xu, G. et al. Recommendation using DMF-based fine tuning method. J Intell Inf Syst 47, 233–246 (2016). https://doi.org/10.1007/s10844-016-0407-6
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DOI: https://doi.org/10.1007/s10844-016-0407-6