Abstract
Collaborative Filtering (CF) is a popular way to build recommender systems and has been widely deployed by many e-commerce websites. Generally, there are two parallel research directions on CF, one is to improve the prediction accuracy ~ (i.e., effectiveness) of CF algorithms and others focus on reducing time cost of CF algorithms ~ (i.e., efficiency). Nevertheless, the problem of how to combine the complementary advantages of these two directions, and design a CF algorithm that is both effective and efficient remains pretty much open. To this end, in this paper, we provide a Matrix Factorization based on Co-Clustering (MFCC) algorithm to address the problem. Specifically, we first adopt a co-clustering algorithm to cluster the user-item rating matrix into several separate sub rating matrices. After that, we provide an efficient matrix factorization algorithm by utilizing the strong connections of users and items in each cluster. In the meantime, this process is also efficient as we can simultaneously compute the matrix factorization for each cluster as there exists little interactions among different clusters. Finally, the experimental results show both the effectiveness and efficiency of our proposed model.
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Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE TKDE 17, 734–749 (2005)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7, 76–80 (2003)
Sarwar, B., Karypis, G., Konstan, J., et al.: Item-based collaborative filtering recommendation algorithms. In: WWW, pp. 285–295 (2001)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42, 30–37 (2009)
Wu, L., Chen, E., Liu, Q., et al.: Leveraging tagging for neighborhood-aware probabilistic matrix factorization. In: CIKM, pp. 1854–1858 (2012)
Najafabadi, M.K., Mahrin, M.N., Chuprat, S., et al.: Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit data. Comput. Hum. Behav. 67, 113–128 (2017)
Hong, R., Hu, Z., Wang, R., Wang, M., Tao, D.: Multi-view object retrieval via multi-scale topic models. IEEE Trans. Image Process. 25, 5814–5827 (2016)
Wu, Y., Liu, X., Xie, M., et al.: Improving collaborative filtering via scalable user-item co-clustering. In: WSDM, pp. 73–82 (2016)
Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. In: NIPS, pp. 1257–1264 (2007)
Zhang, H., Shen, F., Liu, W., He, X., Luan, H., Chua, T.-S.: Discrete collaborative filtering. In: SIGIR, pp. 325–334 (2016)
Wang, Z., Wang, X., Qian, H.: Item type based collaborative algorithm. In: CSO, pp. 387–390 (2010)
Shi, X.Y., Ye, H.W., Gong, S.J.: A personalized recommender integrating item-based and user-based collaborative filtering. In: ISBIM, pp. 264–267 (2008)
Zhang, H., Zha, Z.-J., Yang, Y., Yan, S., Chua, T.-S.: Robust semi nonnegative graph embedding. IEEE Trans. Image Process. 23, 2996–3012 (2014)
Dhillon, I.S., Mallela, S., Modha, D.S.: Information-theoretic co-clustering. In: KDD, pp. 89–98 (2003)
Agarwal, D., Merugu, S.: Predictive discrete latent factor models for large scale dyadic data. In: SIGKDD, pp. 26–35 (2007)
Xiao-Guang, L., Ge, Y., Da-Ling, W., et al.: Latent concept extraction and text clustering based on information theory. JSW, 2276–2284 (2008)
Geiger, B.C., Amjad, R.A.: Hard Clusters Maximize Mutual Information (2016)
Hu, L., Chan, K.C.C.: Fuzzy clustering in a complex network based on content relevance and link structures. TFS 24, 456–470 (2016)
Hu, W.U., Wang, Y.J., Wang, Z., et al.: Two-phase collaborative filtering algorithm based on co-clustering. JSW 21, 1042–1054 (2010)
Mei, J.P., Wang, Y., Chen, L., et al.: Large scale document categorization with fuzzy clustering. TFS 25, 1239–1251 (2016)
Bu, J., Shen, X., Xu, B., et al.: Improving collaborative recommendation via user-item subgroups. TKDE 28, 2363–2375 (2016)
Chai, T., Draxler, R.R.: Root mean square error (RMSE) or mean absolute error (MAE)? - Arguments against avoiding RMSE in the literature. GMD 7, 1525–1534 (2014)
Liu, Q., Ge, Y., Li, Z., et al.: Personalized travel package recommendation. In: IEEE ICDM, pp. 407–416 (2011)
Wu, L., Ge, Y., Liu, Q., et al.: Modeling the evolution of users’ preferences and social links in social networking services. IEEE TKDE 29, 1240–1253 (2017)
Hong, R., Zhang, L., Zhang, C., Zimmermann, R.: Flickr circles: aesthetic tendency discovery by multi-view regularized topic modeling. IEEE Trans. Multimed. 18, 1555–1567 (2016)
Wu, L., Liu, Q., Chen, E., Yuan, N.J., Guo, G., Xie, X.: Relevance meets coverage: a unified framework to generate diversified recommendations. ACM TIST 7, 39 (2016)
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (NSFC) under grants 61472116 and 61502139, Natural Science Foundation of Anhui Province under grant 1608085MF128 and 1708085QF155, and the Open Projects Program of National Laboratory of Pattern Recognition under grant 201600006 and 201700017.
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Yang, W., Wu, L., Liu, X., Fan, C. (2018). MFCC: An Efficient and Effective Matrix Factorization Model Based on Co-clustering. In: Huet, B., Nie, L., Hong, R. (eds) Internet Multimedia Computing and Service. ICIMCS 2017. Communications in Computer and Information Science, vol 819. Springer, Singapore. https://doi.org/10.1007/978-981-10-8530-7_35
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DOI: https://doi.org/10.1007/978-981-10-8530-7_35
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