Author : Seelaboyina Charan Teja 1
Date of Publication :7th December 2017
Abstract: Collaborative filtering inherently suffers from the data sparsity and cold start issues. Social networks are shown helpful to help alleviate these problems. However, social connections may not be available in several real systems, whereas implicit and explicit item relationships are lack of study. During this paper, we tend to propose TrustSVD, a trust-based matrix factorization model by taking into consideration implicit and explicit item relationships. Especially, we apply an adapted approach to reveal implicit and explicit item relationships in terms of item-to-item and group-to-item associations, which are then accustomed regularize the generation of low-rank user- and item-feature matrices. Experimental results on four real-world datasets demonstrate the superiority of our proposed approach against other counterparts.
Reference :
-
- G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,” IEEE Trans. Know. Data Eng., vol. 17, no. 6, pp. 734–749, Jun. 2005.
- Y. Huang, X. Chen, J. Zhang, D. Zeng, D. Zhang, and X. Ding, “Single-trial ERPs denoising via collaborative filtering on ERPs images,” Neurocomputing, vol. 149, pp. 914–923, 2015.
- X. Luo, Z. Ming, Z. You, S. Li, Y. Xia, and H. Leung, “Improving network topology-based protein interactome mapping via collaborative filtering,” Knowl.-Based Syst., vol. 90, pp. 23–32, 2015.
- G. Guo, J. Zhang, and D. Thalmann, “A simple but effective method to incorporate trusted neighbors in recommender systems,” in Proc. 20th Int. Conf. User Model., Adaptation Personalization, 2012, pp. 114–125.
- H. Ma, H. Yang, M. Lyu, and I. King, “SoRec: Social recommendation using probabilistic matrix factorization,” n Proc. 31st Int. ACM SIGIR Conf. Res. Develop. Inform. Retrieval, 2008, pp. 931–940.
- H. Ma, D. Zhou, C. Liu, M. Lyu, and I. King, “Recommender systems with social regularization,” in Proc. 4th ACM Int. Conf. Web Search Data Mining, 2011, pp. 287–296.
- M. Jamali and M. Ester, “A matrix factorization technique with trust propagation for recommendation in social networks,” in Proc. 4th ACM Conf. Recommender Syst., 2010, pp. 135–142.
- B. Yang, Y. Lei, D. Liu, and J. Liu, “Social collaborative filtering by trust,” in Proc. 23rd Int. Joint Conf. Artif. Intell., 2013, pp. 2747–2753.