Abstract
Cold Start (CS) and sparse evaluation problems dramatically degrade recommendation performance in large-scale recommendation systems such as Taobao and eBay. We name this degradation as the sparse trust problem, which will cause the decrease of the recommendation accuracy rate. To address this problem we propose a novel sparse trust mining method, which is based on the Roundtable Gossip Algorithm (RGA). First, we define the relevant representation of sparse trust, which provides a research idea to solve the problem of sparse evidence in the large-scale recommendation system. Based on which the RGA is proposed for mining latent sparse trust relationships between entities in large-scale recommendation systems. Second, we propose an efficient and simple anti-sparsification method, which overcomes the disadvantages of random trust relationship propagation and Grade Inflation caused by different users have different standard for item rating. Finally, the experimental results show that our method can effectively mine new trust relationships and mitigate the sparse trust problem.
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Taobao index. https://shu.taobao.com/industry. Accessed 14 Apr 2017
Zhang, Z.: RADAR: a reputation-driven anomaly detection system for wireless mesh networks. Wirel. Netw. 16(8), 2221–2236 (2010)
Saini, R.: Jammer-assisted resource allocation in secure OFDMA with untrusted users. IEEE Trans. Inf. Forensics Secur. 11(5), 1055–1070 (2016)
Guo, X.: Eliminating the hardware-software boundary: a proof-carrying approach for trust evaluation on computer systems. IEEE Trans. Inf. Forensics Secur. 12(2), 405–417 (2017)
Zhang, J.: Evaluating the trustworthiness of advice about seller agents in e-marketplaces: a personalized approach. Electron. Commer. Res. Appl. 7(3), 330–340 (2008)
Yang, Z.: VoteTrust: leveraging friend invitation graph to defend against social network sybils. IEEE Trans. Dependable Secure Comput. 13(4), 488–501 (2016)
Liu, Y.: ActiveTrust: secure and trustable routing in wireless sensor networks. IEEE Trans. Inf. Forensics Secur. 11(9), 2013–2027 (2016)
Xu, G.: Swift trust in a virtual temporary system: a model based on the Dempster-Shafer theory of belief functions. Int. J. Electron. Commer. 12(1), 93–126 (2007)
Gao, P.: STAR: semiring trust inference for trust-aware social recommenders. In: Proceedings of the 10th ACM Conference on Recommender Systems, Boston, Massachusetts, pp. 301–308 (2016)
Guo, G.: From ratings to trust: an empirical study of implicit trust in recommender systems. In: Proceedings of the 29th Annual ACM Symposium on Applied Computing, Gyeongju, Republic of Korea, pp. 248–253 (2014)
Xu, G.: TRM: computing reputation score by mining reviews. In: AAAI Workshop: Incentives and Trust in Electronic Communities, Phoenix, Arizona (2016)
Zhu, C.: An authenticated trust and reputation calculation and management system for cloud and sensor networks integration. IEEE Trans. Inf. Forensics Secur. 10(1), 118–131 (2015)
Zhou, P.: Toward energy-efficient trust system through watchdog optimization for WSNs. IEEE Trans. Inf. Forensics Secur. 10(3), 613–625 (2015)
Guo, G.: A novel recommendation model regularized with user trust and item ratings. IEEE Trans. Knowl. Data Eng. 28(7), 1607–1620 (2016)
Cho, J.H.: A survey on trust modeling. ACM Comput. Surv. 48(2), 1–40 (2015)
Jiang, W.: Understanding graph-based trust evaluation in online social networks: methodologies and challenges. ACM Comput. Surv. 49(1), 1–35 (2016)
Guo, G.: TrustSVD: collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In: Twenty-Ninth AAAI Conference on Artificial Intelligence, Austin, Texas, pp. 123–129 (2015)
Guo, L.: A trust-based privacy-preserving friend recommendation scheme for online social networks. IEEE Trans. Dependable Secure Comput. 12(4), 413–427 (2015)
Zhong, Y.: A computational dynamic trust model for user authorization. IEEE Trans. Dependable Secure Comput. 12(1), 1–15 (2015)
Yao, W.: Modeling dual role preferences for trust-aware recommendation. In: Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, Gold Coast, Queensland, pp. 975–978 (2014)
Konstas, I.: On social networks and collaborative recommendation. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Boston, MA, pp. 195–202 (2009)
Chen, W.: Collaborative filtering for Orkut communities: discovery of user latent behavior. In: Proceedings of the 18th International Conference on World Wide Web, Madrid, Spain, pp. 681–690 (2009)
Lansing, J.: Trust in cloud computing: conceptual typology and trust-building antecedents. database: the DATABASE for advances. Inf. Syst. 47(2), 58–96 (2016)
Zhang, D.: Cold-start recommendation using bi-clustering and fusion for large-scale social recommender systems. IEEE Trans. Emerg. Top. Comput. 2(2), 239–250 (2017)
Kamvae, S.D.: The Eigentrust algorithm for reputation management in P2P networks. In: Proceedings of the 12th International Conference on World Wide Web, Budapest, Hungary, pp. 640–651 (2003)
Roundtable Algorithm. http://www.top-news.top/news-12840672.html. Accessed 12 Apr 2017
Ron, Z.: A Programmer’s Guide to Data Mining: The Ancient Art of the Numerati, 1st edn. The People’s Posts and Telecommunications Press, Beijing (2015)
Ling, G.: Ratings meet reviews, a combined approach to recommend. In: Proceedings of the 8th ACM Conference on Recommender Systems, pp. 105–112. ACM, Foster City (2014)
Zhao, D.: A distributed and adaptive trust evaluation algorithm for MANET. In: Proceedings of the 12th ACM Symposium on QoS and Security for Wireless and Mobile Networks, pp. 47–54. ACM, New York (2016)
Massa, P.: Trust-aware recommender systems. In: Proceedings of the 2007 ACM Conference on Recommender Systems, pp. 17–24. ACM, Malta (2007)
Guo, G.: ETAF: an extended trust antecedents framework for trust prediction. In: Proceedings of the 2014 International Conference on Advances in Social Networks Analysis and Mining (ASONAM), China, pp. 540–547 (2014)
Ma, H.: Recommender systems with social regularization. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 287–296. ACM, Hong Kong (2011)
Ma, H.: Learning to recommend with social trust ensemble. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Boston, MA, USA, pp. 203–210 (2009)
Guha, R.: Propagation of trust and distrust. In: Proceedings of the 13th International Conference on World Wide Web, pp. 403–412. ACM, New York (2004)
Golbeck, J.A.: Computing and Applying Trust in Web-Based Social Networks. University of Maryland, College Park (2005)
Acknowledgement
This work has been partially sponsored by the National Science Foundation of China (No. 61572355, U1736115), the Tianjin Research Program of Application Foundation and Advanced Technology (No. 15JCYBJC15700), and the Fundamental Research of Xinjiang Corps (No. 2016AC015).
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Liu, M. et al. (2018). Roundtable Gossip Algorithm: A Novel Sparse Trust Mining Method for Large-Scale Recommendation Systems. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11337. Springer, Cham. https://doi.org/10.1007/978-3-030-05063-4_37
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