Abstract
Although recommendation techniques have achieved distinct developments over the decades, the data sparseness problem of the involved user-item matrix still seriously influences the recommendation quality. Most of the existing techniques for recommender systems cannot easily deal with users who have very few ratings. How to combine the increasing amount of different types of social information such as user generated content and social relationships to enhance the prediction precision of the recommender systems remains a huge challenge. In this paper, based on a factor graph model, we formalize the problem in a semi-supervised probabilistic model, which can incorporate different user information, user relationships, and user-item ratings for learning to predict the unknown ratings. We evaluate the method in two different genres of datasets, Douban and Last.fm. Experiments indicate that our method outperforms several state-of-the-art recommendation algorithms. Furthermore, a distributed learning algorithm is developed to scale up the approach to real large datasets.
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References
Agarwal, D., Chen, B., 2009. Regression-based Latent Factor Models. Proc. 15th ACM SIGKDD Conf. on Knowledge Discovery and Data Mining, p.19–28. [doi:10.1145/1557019.1557029]
Balabanovic, M., Shoham, Y., 1997. Content-based, collaborative recommendation. Commun. ACM, 40(3):66–72. [doi:10.1145/245108.245124]
Breese, J.S., Heckerman, D., Kadie, C.M., 1998. Empirical Analysis of Predictive Algorithm for Collaborative Filtering. Proc. 14th Conf. on Uncertainty in Artificial Intelligence, p.43–52.
Deshpande, M., Karypis, G., 2004. Item-based top-N recommendation algorithms. ACM Trans. Inf. Syst., 22(1):143–177. [doi:10.1145/963770.963776]
Jahrer, M., Tuscher, A., Legenstein, R., 2010. Combining Predictions for Accurate Recommender Systems. Proc. 16th ACM SIGKDD Conf. on Knowledge Discovery and Data Mining, p.693–702. [doi:10.1145/1835804.1835893]
Konstas, I., Stathopoulos, V., Jose, J.M., 2009. On Social Networks and Collaborative Recommendation. Proc. 32nd Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, p.195–202. [doi:10.1145/1571941.1571977]
Koren, Y., 2008. Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model. Proc. 14th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, p.426–434. [doi:10.1145/1401890.1401944]
Koren, Y., Bell, R.M., 2011. Advances in Collaborative Filtering. In: Ricci, F., Rokach, L., Shapira, B., et al. (Eds.), Introduction to Recommender Systems Handbook. Springer US, p.145–186. [doi:10.1007/978-0-387-85820-3_5]
Koren, Y., Bell, R.M., Volinsky, C., 2009. Matrix factorization techniques for recommender systems. Computer, 42(8): 30–37. [doi:10.1109/MC.2009.263]
Kschischang, F.R., Frey, B.J., Loeliger, H., 2001. Factor graphs and the sum-product algorithm. IEEE Trans. Inf. Theory, 47(2):498–519. [doi:10.1109/18.910572]
Kurucz, M., Benczúr, A.A., Csalogány, K., 2007. Methods for Large Scale SVD with Missing Values. Proc. KDD Cup and Workshop, p.31–38.
Lee, D.D., Seung, H.S., 1999. Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755): 788–791. [doi:10.1038/44565]
Lops, P., Gemmis, M.D., Semeraro, G., 2011. Content-Based Recommender Systems: State of the Art and Trends. In: Ricci, F., Rokach, L., Shapira, B., et al. (Eds.), Introduction to Recommender Systems Handbook. Springer US, p.73–105. [doi:10.1007/978-0-387-85820-3_3]
Lu, Y., Tsaparas, P., Ntoulas, A., Polanyi, L., 2010. Exploiting Social Context for Review Quality Prediction. Proc. 19th Int. Conf. on World Wide Web, p.691–700. [doi:10.1145/1772690.1772761]
Ma, H., Yang, H., Lyu, M.R., King, I., 2008. Sorec: Social Recommendation Using Probabilistic Matrix Factorization. Proc. 17th ACM Conf. on Information and Knowledge Management, p.931–940. [doi:10.1145/1458082.1458205]
Ma, H., King, I., Lyu, M.R., 2011a. Learning to recommend with explicit and implicit social relations. ACM Trans. Intell. Syst. Technol., 2(3), Article 29. [doi:10.1145/196 1189.1961201]
Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I., 2011b. Recommender Systems with Social Regularization. Proc. 4th ACM Int. Conf. on Web Search and Data Mining, p.287–296. [doi:10.1145/1935826.1935877]
Mei, Q., Cai, D., Zhang, D., Zhai, C., 2008. Topic Modeling with Network Regularization. Proc. 17th Int. Conf. on World Wide Web, p.101–110. [doi:10.1145/1367497.1367512]
Salakhutdinov, R., Mnih, A., 2008. Probabilistic matrix factorization. Adv. Neur. Inf. Process. Syst., 20:1257–1264.
Sarwar, B., Karypis, G., Konstan, J., Riedl, J., 2001. Item-Based Collaborative Filtering Recommendation Algorithms. Proc. 10th Int. Conf. on World Wide Web, p.285–295. [doi:10.1145/371920.372071]
Shen, Y., Jin, R., 2012. Learning Personal + Social Latent Factor Model for Social Recommendation. Proc. 18th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, p.1303–1311. [doi:10.1145/2339530.2339732]
Sinha, R., Swearingen, K., 2001. Comparing Recommendations Made by Online Systems and Friends. Proc. DELOSNSF Workshop on Personalization and Recommender Systems in Digital Libraries, v.106.
Sudderth, E.B., Ihler, A.T., Isard, M., Freeman, W.T., Willsky, A.S., 2010. Nonparametric belief propagation. Commun. ACM, 53(10):95–103. [doi:10.1145/1831407.1831431]
Wang, J., Zhang, Y., 2011. Utilizing Marginal Net Utility for Recommendation in E-commerce. Proc. 34th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, p.1003–1012. [doi:10.1145/2009916.2010050]
Xin, X., King, I., Deng, H., Lyu, M.R., 2009. A Social Recommendation Framework Based on Multi-scale Continuous Conditional Random Fields. Proc. 18th ACM Conf. on Information and Knowledge Management, p.1247–1256. [doi:10.1145/1645953.1646111]
Yang, S., Long, B., Smola, A.J., Zha, H., Zheng, Z., 2011. Collaborative Competitive Filtering: Learning Recommender Using Context of User Choice. Proc. 34th Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, p.295–304. [doi:10.1145/2009916. 2009959]
Yang, X., Steck, H., Guo, Y., Liu, Y., 2012a. On Top-k Recommendation Using Social Networks. Proc. 6th ACM Conf. on Recommender Systems, p.67–74. [doi:10.1145/2365952.2365969]
Yang, X., Steck, H., Liu, Y., 2012b. Circle-Based Recommendation in Online Social Networks. Proc. 18th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, p.1267–1275. [doi:10.1145/2339530.2339728]
Zhou, T., Shan, H., Banerjee, A., Sapiro, G., 2012. Kernelized Probabilistic Matrix Factorization: Exploiting Graphs and Side Information. Proc. 12th SIAM Int. Conf. on Data Mining, p.403–414.
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Project supported by the National Natural Science Foundation of China (Nos. 61035004, 61273213, 61072043, and 61305055) and the National Defense Science Foundation of China (No. 9140A15090112JB93180)
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Huang, Lw., Chen, Gs., Liu, Yc. et al. Enhancing recommender systems by incorporating social information. J. Zhejiang Univ. - Sci. C 14, 711–721 (2013). https://doi.org/10.1631/jzus.CIIP1303
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DOI: https://doi.org/10.1631/jzus.CIIP1303