Abstract
Recommendation systems have been widely used in large e-commerce websites, but cold start and data sparsity seriously affect the accuracy of recommendation. To solve these problems, we propose SSL-SVD, which works to mine the sparse trust between users and improve the performance of the recommendation system. Specifically, we mine sparse trust relationships by decomposing trust impact into fine-grained factors and employing the Transductive Support Vector Machine algorithm to combine these factors. Then, we incorporate both social trust and sparse trust information into the SVD++ model, which can effectively utilize the explicit and implicit influence of trust for rating prediction in the recommendation system. Experiments show that our SSL-SVD increases the trust density degree of each dataset by more than 65% and improves the recommendation accuracy by up to 4.3%.
- Reid Andersen, Christian Borgs, and Jennifer Chayes. 2008. Trust-based recommendation systems: An axiomatic approach. In Proceedings of the 17th International Conference on World Wide Web. ACM, 199--208. DOI:https://doi.org/10.1145/1367497.1367525.Google Scholar
Digital Library
- Mukund Deshpande and George Karypis. 2004. Item-based top-N recommendation algorithms. ACM Trans. Inf. Syst. 22, 1 (2004), 143--177.Google Scholar
Digital Library
- Soude Fazeli, Babak Loni, Alejandro Bellogin, Hendrik Drachsler, and Peter Sloep. 2014. Implicit vs. explicit trust in social matrix factorization. In Proceedings of the 8th ACM Conference on Recommender systems. ACM, 317--320.Google Scholar
Digital Library
- Soude Fazeli, Babak Loni, Alejandro Bellogin, Hendrik Drachsler, and Peter Sloep. 2014. Implicit vs. explicit trust in social matrix factorization. In Proceedings of the 8th ACM Conference on Recommender Systems. ACM, 317--320.Google Scholar
Digital Library
- Lahiru S. Gallege, Dimuthu U. Gamage, James H. Hil, and Rajeev R. Raje. 2014. Towards trust-based recommender systems for online software services. In Proceedings of the 9th Cyber and Information Security Research Conference. ACM, 61--64.Google Scholar
- Peixin Gao, Hui Miao, John S. Baras, and Jennifer Golbeck. 2016. STAR: Semiring trust inference for trust-aware social recommenders. In Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 301--308.Google Scholar
Digital Library
- Chinshung Hwang and Yupin Chen. 2007. Using trust in collaborative filtering recommendation. In Proceedings of the 20th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Springer, 1052--1056.Google Scholar
Cross Ref
- J. Golbeck. 2006. Generating predictive movie recommendations from trust in social networks. In Proceedings of the International Conference on Trust Management. Springer, 93--104.Google Scholar
Cross Ref
- Guibing Guo, Jie Zhang, and Daniel Thalmann. 2012. A simple but effective method to incorporate trusted neighbors in recommender systems. In Proceedings of the 20th International Conference on User Modeling, Adaptation and Personalization (UMAP’12). 114--125.Google Scholar
Digital Library
- Guibing Guo, Jie Zhang, and Neil Yorke-Smith. 2015. TrustSVD: Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In Proceedings of the 29th AAAI Conference on Artificial Intelligence. AAAI Press, 123--129.Google Scholar
Cross Ref
- Fang Hui, Bao Yang, and Zhang Jie. 2014. Leveraging decomposed trust in probabilistic matrix factorization for effective recommendation. In Proceedings of the 28th AAAI Conference on Artificial Intelligence. AAAI Press, 30--36.Google Scholar
- Mohsen Jamali and Martin Ester. 2009. TrustWalker: A random walk model for combining trust-based and item-based recommendation. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 397--406.Google Scholar
Digital Library
- Mohsen Jamali and Martin Ester. 2010. A matrix factorization technique with trust propagation for recommendation in social networks. In Proceedings of the 4th ACM Conference on Recommender Systems. ACM, 135--142.Google Scholar
Digital Library
- Wenjun Jiang, Jie Wu, and Guojun Wang. 2015. On selecting recommenders for trust evaluation in online social networks. ACM Trans. Internet Technol. 15, 4 (2015), 21 pages.Google Scholar
Digital Library
- Rong Jin, Joyce Y. Chai, and Luo Si. 2004. An automatic weighting scheme for collaborative filtering. In Proceedings of the 27th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 337--344.Google Scholar
Digital Library
- T. Joachims. 1999. Transductive inference for text classification using support vector machines. In Proceedings of the 16th International Conference on Machine Learning. Morgan Kaufmann Publishers Inc, 200--209.Google Scholar
- Ehsan Khadangi and Alireza Bagheri. 2013. Comparing MLP, SVM, and KNN for predicting trust between users in Facebook. In Proceedings of the International Econference on Computer & Knowledge Engineering. IEEE.Google Scholar
Cross Ref
- Ioannis Konstas, Vassilios Stathopoulos, and Joemon M. Jose. 2009. On social networks and collaborative recommendation. In Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 195--202.Google Scholar
- Yehuda Koren. 2008. Factorization meets the neighborhood: A multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 426--434.Google Scholar
Digital Library
- Yehuda Koren. 2010. Factor in the neighbors: Scalable and accurate collaborative filtering. ACM Trans. Knowl. Discov. Data 4, 1 (2010), 1--24.Google Scholar
Digital Library
- Neal Lathia, Stephen Hailes, and Licia Capra. 2008. Trust-based collaborative filtering. In Proceedings of the IFIP International Conference on Trust Management. 119--134.Google Scholar
Cross Ref
- Ming Li, Benjamin Dias, Wael El-Deredy, and Paulo J. G. Lisboa. 2007. A probabilistic model for item-based recommender systems. In Proceedings of the ACM Conference on Recommender Systems. ACM, 129--132.Google Scholar
- G. Linden, B. Smith, and J. York. 2003. Item-based top-N recommendation algorithms. IEEE Internet Comput. 722, 1 (2003), 76--80.Google Scholar
Digital Library
- Bin Liu and Hui Xiong. 2013. Point-of-interest recommendation in location-based social networks with topic and location awareness. In Proceedings of the SIAM International Conference on Data Mining. SIAM, 396--404.Google Scholar
Cross Ref
- Mengdi Liu, Guangquan Xu, and Xi Zheng. 2018. Roundtable Gossip Algorithm: A novel sparse trust mining method for large-scale recommendation systems. In Proceedings of the International Conference on Algorithms and Architectures for Parallel Processing. Springer, 495--510.Google Scholar
Cross Ref
- Yiding Liu, Tuan-Anh Nguyen Pham, Gao Cong, and Quan Yuan. 2017. An experimental evaluation of point-of-interest recommendation in location-based social networks. Proc. VLDB Endow. 10, 10 (2017), 1010--1021.Google Scholar
Digital Library
- Hao Ma, Irwin King, and Michael R. Lyu. 2009. Learning to recommend with social trust ensemble. In Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 203--210.Google Scholar
- Hao Ma, Haixuan Yang, Michael R. Lyu, and Irwin King. 2008. SoRec: Social recommendation using probabilistic matrix factorization. In Proceedings of the 17th ACM Conference on Information and Knowledge Management. ACM, 931--940.Google Scholar
Digital Library
- Paolo Massa and Paolo Avesani. 2007. Trust-aware recommender systems. In Proceedings of the ACM Conference on Recommender Systems. ACM, 17--24.Google Scholar
Digital Library
- Yitong Meng, Guangyong Chen, Jiajin Li, and Shengyu Zhang. 2018. Psrec: Social recommendation with pseudo ratings. In Proceedings of the 12th ACM Conference on Recommender Systems. ACM, 397--401.Google Scholar
Digital Library
- John O’Donovan and Barry Smyth. 2005. Trust in recommender systems. In Proceedings of the 10th International Conference on Intelligent User Interfaces. ACM, 167--174.Google Scholar
Digital Library
- Hae-Sang Park and Chi-Hyuck Jun. 2009. A simple and fast algorithm for K-medoids clustering. Expert Syst. 32, 6 (2009), 3336--3341.Google Scholar
Digital Library
- Dimitrios Rafailidis and Fabio Crestani. 2017. Learning to rank with trust and distrust in recommender systems. In Proceedings of the 11th ACM Conference on Recommender Systems. ACM, 5--13.Google Scholar
Digital Library
- Ruslan Salakhutdinov and Andriy Mnih. 2007. Probabilistic matrix factorization. In Proceedings of the 20th International Conference on Neural Information Processing Systems (NIPS’07). 1257--1264.Google Scholar
Digital Library
- Arbia Riahi Sfar, Enrico Natalizio, Yacine Challal, and Zied Chtourou. 2018. A roadmap for security challenges in the Internet of Things. Dig. Commun. Netw. 4, 2 (2018), 118--137.Google Scholar
Cross Ref
- Jian Wei, Jianhua He, Kai Chen, Yi Zhou, and Zuoyin Tang. 2017. Collaborative filtering and deep learning based recommendation system for cold start items. Expert Syst. Applic. 69, 1 (2017), 29--39.Google Scholar
Cross Ref
- Guangquan Xu, Zhiyong Feng, Huabei Wu, and Dexin Zhao. 2007. Swift trust in virtual temporary system: A model based on Dempster-Shafer theory of belief functions. Int. J. Elect. Commerce 12, 1 (2007), 93--127.Google Scholar
Digital Library
- Xiaoming Li, Guangquan Xu, Xi Zheng, Kaitai Liang, Emmanouil Panaousis, Tao Li, Wei Wang, and Chao Shen. 2019. Using sparse representation to detect anomalies in complex WSNs. ACM Transactions on Intelligent Systems and Technology 10 (2019), 18 pages.Google Scholar
Digital Library
- Bao Yang, Yu Lei, Dayou Liu, and Jiming Liu. 2013. Social collaborative filtering by trust. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence. AAAI Press, 2747--2753.Google Scholar
- Bao Yang, Yu Lei, Jiming Liu, and Wenjie Li. 2017. Social collaborative filtering by trust. IEEE Trans. Pattern Anal. Mach. Intell. 39, 8 (2017), 1633--1647.Google Scholar
Digital Library
- Zhi Yang, Jilong Xue, Xiaoyong Yang, Xiao Wang, and Yafei Dai. 2016. VoteTrust: Leveraging friend invitation graph to defend against social network sybils. IEEE Trans. Depend. Secure Comput. 13, 4 (2016), 488--501.Google Scholar
Digital Library
- Lina Yao, Quan Z. Sheng, Xianzhi Wang, Wei Emma Zhang, and Yongrui Qin. 2018. Collaborative location recommendation by integrating multi-dimensional contextual information. ACM Trans. Internet Technol. 18, 3 (2018), 24 pages.Google Scholar
Digital Library
- Weilong Yao, Jing He, Guangyan Huang, and Yanchun Zhang. 2014. Modeling dual role preferences for trust-aware recommendation. In Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM, 975--978.Google Scholar
Digital Library
- Markus Zanker. 2012. The influence of knowledgeable explanations on users’ perception of a recommender system. In Proceedings of the 6th ACM Conference on Recommender Systems. ACM, 269--272.Google Scholar
Digital Library
- Guangquan Xu, Yao Zhang, Litao Jiao, Emmanouil Panaousis, Kaitai Liang, Hao Wang, and Xiaotong Li. 2019. DT-CP: A Double-TTPs based contract-signing protocol with lower computational cost. IEEE Access (Early Access). DOI:10.1109/ACCESS.2019.2952213Google Scholar
Index Terms
SSL-SVD: Semi-supervised Learning--based Sparse Trust Recommendation
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