A Factored Similarity Model with Trust and Social Influence for Top-N Recommendation
Keywords:recommendation system, matrix factorization, trust, social influence, deep learning, top-n recommendation
AbstractMany trust-aware recommendation systems have emerged to overcome the problem of data sparsity, which bottlenecks the performance of traditional Collaborative Filtering (CF) recommendation algorithms. However, these systems most rely on the binary social network information, failing to consider the variety of trust values between users. To make up for the defect, this paper designs a novel Top-N recommendation model based on trust and social influence, in which the most influential users are determined by the Improved Structural Holes (ISH) method. Specifically, the features in Matrix Factorization (MF) were configured by deep learning rather than random initialization, which has a negative impact on prediction of item rating. In addition, a trust measurement model was created to quantify the strength of implicit trust. The experimental result shows that our approach can solve the adverse impacts of data sparsity and enhance the recommendation accuracy.
Anagnostopoulos, A.; Kumar, R.; Mahdian, M. (2008). Influence and correlation in social networks, Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 7-15, 2008. https://doi.org/10.1145/1401890.1401897
Bao, Y.; Fang, H.; Zhang, J. (2014). Leveraging decomposed trust in probabilistic matrix factorization for effective recommendation, Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI), 350: 30-36, 2014.
Burt, R.S. (2009); Structural holes: The social structure of competition, Harvard university press, 2009.
Deng, S.; Huang, L.; Xu, G. et al. (2017). On deep learning for trust-aware recommendations in social networks, IEEE Transactions on Neural Networks & Learning Systems, 28(5), 1164- 1177, 2017. https://doi.org/10.1109/TNNLS.2016.2514368
Deng, X.Y.; Wang, C. (2018). A hybrid collaborative filtering model with context and folksonomy for social recommendation, Ingenierie des Systemes d'Information, 23(5), 139- 157, 2018. https://doi.org/10.3166/isi.23.5.139-157
Freeman, L.C. (1977); A set of measures of centrality based on betweenness, Sociometry, 40(1), 35-41, 1977. https://doi.org/10.2307/3033543
Guy, I.; Ronen, I.; Wilcox, E. (2009). Do you know?: recommending people to invite into your social network, Proceedings of the 14th International Conference on Intelligent User Interfaces, 77-86, 2009.
Guo, G.; Zhang, J.; Yorke-Smith, N. (2016). A novel recommendation model regularized with user trust and item ratings, IEEE Transactions on Knowledge & Data Engineering, 28(7), 1607-1620, 2016. https://doi.org/10.1109/TKDE.2016.2528249
Guo, G.; Zhang, J.; Zhu, F. et al. (2017). Factored similarity models with social trust for top-n item recommendation, Knowledge-Based Systems, 122, 17-25, 2017. https://doi.org/10.1016/j.knosys.2017.01.027
Jamali, M.; Ester; M. (2010). A matrix factorization technique with trust propagation for recommendation in social networks, Proceedings of the fourth ACM conference on Recommender systems, 135-142, 2010. https://doi.org/10.1145/1864708.1864736
Kabbur, S.; Ning, X.; Karypis, G. (2013). Fism: factored item similarity models for top-n recommender systems, Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 659-667, 2013. https://doi.org/10.1145/2487575.2487589
Kempe, D.; Kleinberg, J.; Tardos, E. (2003). Maximizing the spread of influence through a social network, Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 137-146, 2003. https://doi.org/10.1145/956750.956769
Kitsak, M.; Gallos, L.K.; Havlin, S. et al. (2010). Identification of influential spreaders in complex networks, Nature Physics, 6(11), 888-893, 2010. https://doi.org/10.1038/nphys1746
Koren, Y. (2008). Factorization meets the neighborhood: a multifaceted collaborative filtering model, Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 426-434, 2008. https://doi.org/10.1145/1401890.1401944
Li, D.; Luo, Z.; Ding, Y. et al. (2017). User-level microblogging recommendation incorporating social influence, Journal of the Association for Information Science and Technology, 68(3), 553-568, 2017. https://doi.org/10.1002/asi.23681
Pan, W.; Chen, L. (2013). Gbpr: Group preference based bayesian personalized ranking for one-class collaborative filtering, Proceedings of The Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI), 13, 2691-2697, 2013.
Pastorsatorras, R.; Castellano, C.; Mieghem, P.V. et al. (2014). Epidemic processes in complex networks, Review of Modern Physics, 87(3), 120-131, 2014. https://doi.org/10.1103/RevModPhys.87.925
Peng, S.; Wang, G.; Xie, D. (2017). Social influence analysis in social networking big data: Opportunities and challenges, IEEE Network the Magazine of Global Internetworking, 31(1), 11-17, 2017. https://doi.org/10.1109/MNET.2016.1500104NM
Rendle, S.; Freudenthaler, C.; Gantner, Z. et al.. (2009). Bpr: Bayesian personalized ranking from implicit feedback, Conference on Uncertainty in Artificial Intelligence, 452-461, 2009.
Rogers, E.M. (1995). The Diffusion of Innovations, Free Press, 1995.
Sedhain, S.; Menon, A.K.; Sanner, S. et al. (2017). Low-rank linear cold-start recommendation from social data, Proceedings of the 31th AAAI Conference on Artificial Intelligence (AAAI), 1502-1508, 2017.
Xu, W.; Rezvani, M.; Liang, W. et al. (2017). Efficient algorithms for the identification of top-k structural hole spanners in large social networks, IEEE Transactions on Knowledge & Data Engineering, 29(5), 1017-1030, 2017. https://doi.org/10.1109/TKDE.2017.2651825
Xu, K.; Zheng, X.; Cai, Y. et al. (2018). Improving user recommendation by extracting social topics and interest topics of users in unidirectional social networks, Knowledge Based Systems, 140, 120-133, 2018. https://doi.org/10.1016/j.knosys.2017.10.031
Yuan, X.; Huang, B.; Wang, Y. et al. (2018). Deep learning based feature representation and its application for soft sensor modeling with variable-wise weighted SAE, IEEE Transactions on Industrial Informatics, (99): 1-1, 2018.
Yang, C. ; Sun, M. ; Zhao; W. X. et al. (2016). A neural network approach to joint modeling social networks and mobile trajectories, Acm Transactions on Information Systems, 35(4), 36, 2016. https://doi.org/10.1145/3041658
Yuan, Q.; Zhao, S.; Chen, L. et al. (2009). Augmenting collaborative recommender by fusing explicit social relationships, Workshop on Recommender Systems and the Social Web, Recsys, 2009.
Zhang, Z., Liu, Y., Jin, Z. et al. (2018). A dynamic trust based two-layer neighbor selection scheme towards online recommender systems, Neurocomputing, 285, 94-103, 2018. https://doi.org/10.1016/j.neucom.2017.12.063
Zhao, H.; Yao, Q.; Kwok, J.T. et al. (2017). Collaborative filtering with social local models, 2017 IEEE International Conference on Data Mining (ICDM), 645-654, 2017. https://doi.org/10.1109/ICDM.2017.74
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