Abstract
In the era of big data, personalized recommendation has become an important research issue in social networks as it can find and match user’s preference. In this paper, the user trust is integrated into the recommendation algorithm, by dividing the user trust into 2 parts: user score trust and user preference trust. In view of the common items in user item score matrix, the algorithm combines the number of items with the score similarity between users, and establishes an asymmetric trust relationship matrix so as to calculate the user’s score trust. For the non-common score items, we use the attribute information of items and the scoring weight to calculate the user’s preference trust. Based on the user trust in social network, a new collaborative filtering recommendation algorithm is proposed. Besides, a new matrix factorization recommendation algorithm is proposed by combining the user trust with matrix factorization. We did the experiments comparing with the related algorithms on the real data sets of social network. The results show that the proposed algorithms can effectively improve the accuracy of recommendation.
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References
Guy, I., Ronen, I., Wilcox, E.: Do you know?: Recommending people to invite into your social network. In: Proceedings of the 14th International Conference on Intelligent User Interfaces, vol. 3, pp. 77–86 (2009)
Park, D.H., Kim, H.K., Choi, I.Y., et al.: A literature review and classification of recommender systems research. Expert Syst. Appl. 39(11), 10059–10072 (2012)
Carrer-Neto, W., Valencia-García, R.: Social knowledge-based recommender system. Application to the movies domain. Expert Syst. Appl. 39(12), 10990–11000 (2012)
Su, J.H., Yeh, H.H., Yu, P.S., et al.: Music recommendation using content and context information mining. Intell. Syst. IEEE 25(1), 16–26 (2010)
Choi, K., Suh, Y.: A new similarity function for selecting neighbors for each target item in collaborative filtering. Knowl. Based Syst. 37(1), 146–153 (2013)
Huang, Z., Chen, H., Zeng, D.: Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans. Inf. Syst. 22(1), 116–142 (2004)
Jamali, M., Ester, M.: TrustWalker: a random walk model for combining trust-based and item-based recommendation. In: ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 397–406. ACM (2009)
Lathia, N., Hailes, S., Capra, L.: Trust-based collaborative filtering. In: Karabulut, Y., Mitchell, J., Herrmann, P., Jensen, C.D. (eds.) Trust Management II. IFIP – International Federation for Information Processing, vol. 263, pp. 119–134. Springer, New York (2008)
Massa, P., Avesani, P.: Trust metrics on controversial users: balancing between tyranny of the majority. Int. J. Seman. Web Inf. Syst. 3(1), 39–64 (2007)
Guo, G., Zhang, J., Thalmann, D.: Merging trust in collaborative filtering to alleviate data sparsity and cold start. Knowl. Based Syst. 57(2), 57–68 (2013)
Tang, J., Hu, X., Gao, H., et al.: Exploiting local and global social context for recommendation. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence, pp. 2712–2718 (2013)
Liu, S.-Z., Liao, Z.-F., Wu, Y.-F., et al.: A collaborative filtering algorithm combined with user rating credibility and similarity. J. Chin. Comput. Syst. 35(5), 973–977 (2014)
Lu, K., Xie, L., Li, M.-C.: Research on implied-trust aware collaborative filtering recommendation algorithm. J. Chin. Comput. Syst. 37(2), 241–245 (2016)
Yang, B., Lei, Y., Liu, D., et al.: Social collaborative filtering by trust. In: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, pp. 2747–2753. AAAI Press (2013)
Guo, G., Zhang, J., Yorke-Smith, N.: TrustSVD: Collaborative Filtering with both the explicit and implict influence of user trust and item ratings. In: Proceedings of the 29 AAAI Conference on Artificial Intelligence, pp. 123–130 (2015)
Ji, K., Shen, H.: Making recommendations from top-N user-item subgroups. Neurocomputing 165(C), 228–237 (2015)
Lu, Z., Dou, Z., Lian, J., et al.: Content-based collaborative filtering for news topic recommendation. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 217–223 (2015)
Koren, Y.: Factor in the neighbors: scalable and accurate collaborative filtering. ACM Trans. Knowl. Discovery Data 4(1), 2010 (2010)
Sarwar, B., Karypis, G., Konstan, J., et al.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295. ACM (2001)
Su, J.H., Chang, W.Y., Tseng, V.S.: Personalized music recommendation by mining social media tags. Procedia Comput. Sci. 22(10), 303–312 (2013)
Lai, C.H., Liu, D.R., Lin, C.S.: Novel personal and group-based trust models in collaborative filtering for document recommendation. Inf. Sci. 239(4), 31–49 (2013)
Acknowledgments
This work is supported by the National Natural Science Foundation of China under Grants No. 61272186 and the Foundation of Heilongjiang Postdoctoral under Grant No. LBH-Z12068.
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Dong, Y., Zhao, C., Cheng, W., Li, L., Liu, L. (2016). A Personalized Recommendation Algorithm with User Trust in Social Network. In: Che, W., et al. Social Computing. ICYCSEE 2016. Communications in Computer and Information Science, vol 623. Springer, Singapore. https://doi.org/10.1007/978-981-10-2053-7_7
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DOI: https://doi.org/10.1007/978-981-10-2053-7_7
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