Email this article Framework of Matrix Factorization to Achieve Rating Prediction Task
Abstract
We propose a social client wistful estimation approach and figure every client's notion on things/items. Besides, we consider a client's own wistful properties as well as contemplate relational nostalgic impact. At that point, we consider item notoriety, which can be induced by the sentimental distributions of a client set that mirror clients' exhaustive assessment. Finally, we intertwine three components client sentiment likeness, relational nostalgic impact, and thing's notoriety closeness into our recommender framework to make a precise rating prediction. We lead an execution assessment of the three nostalgic components on a genuine dataset gathered from Yelp.
References
R. Salakhutdinov, and A. Mnih, “Probabilistic matrix factorization,” in NIPS, 2008.
X. Yang, H. Steck, and Y. Liu, “Circle-based recommendation in online social networks, ” in Proc. 18th ACM SIGKDD Int. Conf. KDD, New York, NY, USA, Aug. 2012, pp. 1267–1275.
M. Jiang, P. Cui, R. Liu, Q. Yang, F. Wang, W. Zhu, and S. Yang, “Social contextual recommendation,” in proc. 21st ACM Int. CIKM, 2012, pp. 45-54.
M. Jamali and M. Ester, “A matrix factorization technique with trust propagation for recommendation in social networks,” in Proc. ACM conf. RecSys, Barcelona, Spain. 2010, pp. 135-142.
Z. Fu, X. Sun, Q. Liu, et al., “Achieving Efficient Cloud Search Services: Multi-Keyword Ranked Search over Encrypted Cloud Data Supporting Parallel Computing,” IEICE Transactions on Communications, 2015, 98(1):190-200.
G. Ganu, N. Elhadad, A Marian, “Beyond the stars: Improving rating predictions using Review text content,” in 12th International Workshop on the Web and Databases (WebDB 2009). pp. 1-6.
J. Xu, X. Zheng, W. Ding, “Personalized recommendation based on reviews and ratings alleviating the sparsity problem of collaborative filtering,” IEEE International Conference on e-business Engineering. 2012, pp. 9-16.
X. Qian, H. Feng, G. Zhao, and T. Mei, “Personalized recommendation combining user interest and social circle,” IEEE Trans. Knowledge and data engineering. 2014, pp. 1763-1777.
H. Feng, and X. Qian, “Recommendation via user’s personality and social contextual,” in Proc. 22nd ACM international conference on information & knowledge management. 2013, pp. 1521-1524.
Z. Fu, K. Ren, J. Shu, et al., “Enabling Personalized Search over Encrypted Outsourced Data with Efficiency Improvement,” IEEE Transactions on Parallel & Distributed Systems, 2015:1-1.
D.M. Blei, A.Y. Ng, and M. I. Jordan, “Latent Dirichlet Allocation,” Journal of machine learning research 3. 2003, pp. 993-1022.
W. Zhang, G. Ding, L. Chen, C. Li , and C. Zhang, “ Generating virtual ratings from Chinese reviews to augment online recommendations,” ACM TIST, vol.4, no.1. 2013, pp. 1-17.
Z. Xia, X. Wang, X. Sun, and Q. Wang, “A Secure and Dynamic Multi-keyword Ranked Search Scheme over Encrypted Cloud Data,” IEEE Transactions on Parallel and Distributed Systems, vol. 27, no. 2, 2015, pp. 340-352.
J. Weston, R. J. Weiss, H. Yee, “Nonlinear latent factorization by embedding multiple user interests,” 7th ACM, RecSys, 2013, pp. 65-68.
J. Huang, X. Cheng, J. Guo, H. Shen, and K. Yang, “ Social recommendation with interpersonal influence,” in Proc. ECAI, 2010, pp. 601-606.
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