Abstract
With the development of personalized recommendations, information overload has been alleviated. However, the sparsity of the user-item rating matrix and the weak transitivity of trust still affect the recommendation accuracy in complex social network environments. Additionally, collaborative filtering based on users is vulnerable to shilling attacks due to neighbor preference recommendation. With the objective of overcoming these problems, a collaborative filtering recommendation method based on trust and emotion is proposed in this paper. First, we employ a method based on explicit and implicit satisfaction to alleviate the sparsity problems. Second, we establish trust relationships among users using objective and subjective trust. Objective trust is determined by similarity of opinion, including rating similarity and preference similarity. Subjective trust is determined by familiarity among users based on six degrees of separation. Third, based on the trust relationship, a set of trusted neighbors is obtained for a target user. Next, to further exclude malicious users or attackers from the neighbors, the set is screened according to emotional consistency among users, which is mined from implicit user behavior information. Finally, based on the ratings of items by the screened trusted neighbors and the trust relationships among the target user and these neighbors, we can obtain a list of recommendations for the target user. The experimental results show that the proposed method can improve the recommendation accuracy in the case of data sparsity, effectively resist shilling attacks, and achieve higher recommendation accuracy for cold start users compared to other methods.
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References
Anand, S.S., & Mobasher, B. (2003). Intelligent techniques for web personalization. In Proceedings of the International Conference on Intelligent Techniques for Web Personalization (pp. 1–36). Acapulco.
Azadjalal, M.M., Moradi, P., Abdollahpouri, A., Jalili, M. (2017). A trust-aware recommendation method based on Pareto dominance and confidence concepts. Knowledge-Based Systems, 116, 130–143.
Bilge, A., Ozdemir, Z., Polat, H. (2014). A novel shilling attack detection method. Procedia Computer of Science, 31, 165–174.
Deng, S., Huang, L., Xu, G. (2014). Social network-based service recommendation with trust enhancement. Expert Systems with Applications, 41, 8075–8084.
Dong, Z., Dong, Q., Hao, C. (2010). Hownet and its computation of meaning. In Proceedings of the 23rd International Conference on Computational Linguistics: Demonstrations (pp. 53–56). Beijing.
Ekman, P. (1993). Facial expression and emotion. American Psychology, 48, 384–392.
Felfernig, A., Jeran, M., Ninaus, G., et al. (2013). Toward the next generation of recommender systems: applications and research challenges. Multimedia Services in Intelligent Environments, 24, 81–98.
Fong, A.C.M., Zhou, B., Hui, S., Tang, J., Hong, G. (2012). Generation of personalized ontology based on consumer emotion and behavior analysis. Transactions on Affective Computing, 3, 152–164.
Gonzalez-Rodriguez, M.R., Martnez-torres, M.R., Toral, S.L. (2014). Monitoring travel-related information on social media through sentiment analysis. In Proceedings of the 7th IEEE/ACM International Conference on Utility and Cloud Computing (pp. 636–641). London.
Guo, G., Zhang, J., Yorkesmith, N. (2016). A novel recommendation model regularized with user trust and item ratings. IEEE Transactions on Knowledge Data Engineering, 28, 1–14.
Hu, X., Meng, X., Zang, Y., Shi, Y. (2014). Recommendation algorithm combing item features and trust relationship of mobile users. Journal of Software, 25, 1817–1830.
Huang, Z., Zhang, J., Zhang, B., Yu, J., Xiang, Y., Huang, D. (2016). Survey of semantics-based recommendation algorithms. Acta Electoral Sinica, 44, 2262–2275.
IJntema, W., Goossen, F., Frasincar, F., et al. (2010). Ontology-based news recommendation. In Proceedings of the EDBT/ICDT Workshops (pp. 1–6). ACM.
Javari, A., & Jalili, M. (2015). A probabilistic model to resolve diversitycaccuracy challenge of recommendation systems. Knowledge and Information Systems, 44, 609–627.
Kalai, A., Zayani, C.A., Amous, I., Abdelghani, W., Sdes, F. (2018). Social collaborative service recommendation approach based on users trust and domain-specific expertise. Future Generation of Computer System, 80, 355–367.
Karypis, G. (2001). Evaluation of item-based top-n recommendation algorithms. In Proceedings of the 10th International Conference on Information and Knowledge Management (pp. 247–254). atlanta.
Lee, W.P., & Ma, C.Y. (2016). Enhancing collaborative recommendation performance by combining user preference and trust-distrust propagation in social networks. Knowledge-Based Systems, 106, 125–134.
Li, J., Chen, C., Chen, H., Tong, C. (2017). Towards context-aware social recommendation via individual trust. Knowledge-Based Systems, 127, 58–66.
Lo, S., & Lin, C. (2007). WMR–a graph-based algorithm for friend recommendation. In Proceedings of the Atlantic Web Intelligence Conference on Advances in Intelligent Web Mastering (pp. 223–229). fontainebleau.
Lu, J., Shambour, Q., Xu, Y., et al. (2013). A web-based personalized business partner recommendation system using fuzzy semantic techniques. Computer Intelligent-Us, 29, 37–69.
Ma, X., Lu, H., Gan, Z., Zeng, J. (2017). An explicit trust and distrust clustering based collaborative filtering recommendation approach. Electronic Commerce R. A., 25, 29–39.
Mei, J.J., Zhu, Y.M., Gao, Y.Q. (1983). TongYiCi CiLin,. Shanghai: Shanghai Lexicographic Publishing House.
Min, K., Ma, C., Zhao, T., Li, H. (2015). Bosonnlp: an ensemble approach for word segmentation and POS tagging. In Proceedings of the 4th CCF Conference on Natural Language Processing and Chinese Computing (pp. 520–526).
Musto, C., de Gemmis, M., Semeraro, G., Lops, P. (2017). A multi-criteria recommender system exploiting aspect-based sentiment analysis of users’ reviews. In Proceedings of the 11th ACM Conference on Recommender Systems (pp. 321–325). como.
Orellana-Rodriguez, C., Diaz-Aviles, E., Nejdl, W. (2015). Mining affective context in short films for emotion-aware recommendation. In Proceedings of the 26th ACM Conference on Hypertext and Social Media (pp. 185–194). new york.
Pan, Y., He, F., Yu, H. (2016). Social recommendation algorithm using implicit similarity in trust. Chinese Journal of Computer, 39, 1–19.
Pazzani, M. (1999). A framework for collaborative content-based and demographic filtering. Artificial Intelligence Review, 13, 393–408.
Qi, X., Yang, C., Li, X., Chen, J. (2011). A trust calculating algorithm based on social networking service users context. Journal of Computer, 34, 2403–2413.
Qiu, X., Qian, P., Yin, L., Wu, S., Huang, X. (2015). Overview of the NLPCC 2015 shared task: Chinese word segmentation and POS tagging for micro-blog texts. In Proceedings of the 4th CCF Conference on Natural Language Processing and Chinese Computing (pp. 541–549).
Schafer, J., Frankowski, D., Herlocker, J., Sen, S. (2007). Collaborative filtering recommender systems. The Adaptive Web, 4321, 291–324.
Vagliano, I., Monti, D., Morisio, M. (2017). Semrevrec: a recommender system based on user reviews and linked data. In Proceedings of the Poster Track of the 11th ACM Conference on Recommender Systems (pp. 1–3). como.
Wang, X., Zhang, X., Wu, J. (2015). Collaborative filtering recommendation algorithm based on one-jump trust model. Journal of Communications, 36, 193–200.
Wang, R., Jiang, Y., Li, Y., Lou, J. (2016). A collaborative filtering recommendation algorithm based on multiple social trusts. Journal of Computer Research Device, 53, 1389–1399.
Wu, D., Lu, J., Zhang, G. (2015). A fuzzy tree matching-based personalized e-learning recommender system. IEEE Transactions on Fuzzy System, 23, 2412–2426.
Xia, H., Fang, B., Gao, M., Ma, H., Tang, Y., Wen, J. (2015). A novel item anomaly detection approach against shilling attacks in collaborative recommendation systems using the dynamic time interval segmentation technique. Information Sciences, 306, 150–165.
Xu, L., Lin, H., Pan, Y. (2008). Constructing the affective lexicon ontology. Journal of The China Society for Scientific and Technical Information, 27, 180–185.
Yang, Z., Cai, Z., Guan, X. (2016). Estimating user behavior toward detecting anomalous ratings in rating systems. Knowledge-Based Systems, 111, 144–158.
Yang, Z., Cai, Z., Yang, Y. (2017). Spotting anomalous ratings for rating systems by analyzing target users and items. Neurocomputing, 240, 25–46.
Yilmazel, B. Y., & Kaleli, C. (2016). Robustness analysis of arbitrarily distributed data-based recommendation methods. Expert Systems with Applications, 44, 217–229.
Yin, G., Zhang, Y., Dong, Y., Han, Q. (2014a). A constrained trust recommendation using probabilistic matrix factorization. Acta Electoral Sinica, 42, 904–911.
Yin, J., Wang, Z., Li, Q., Su, W. (2014b). Personalized recommendation based on large-scale implicit feedback. Journal of Software, 25, 1953–1966.
Zhang, F., Lu, Y., Chen, J., Liu, S., Ling, Z. (2017a). Robust collaborative filtering based on non-negative matrix factorization and R1-norm. Knowledge-Based Systems, 118, 177–190.
Zhang, Z., Liu, Y., Z Jin, R. (2017b). Zhang, Selecting influential and trustworthy neighbors for collaborative filtering recommender systems. In Proceedings of the 7th IEEE Computing and Communication Workshop and Conference (pp. 1–7). las vegas.
Zhou, W., Wen, J., Xiong, Q., Gao, M., Zeng, J. (2016). Svm-tia a shilling attack detection method based on svm and target item analysis in recommender systems. Neurocomputing, 210, 197–205.
Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 61672039, No. 61602009, and No. 61772034), the Natural Science Foundation of Anhui Province (No. 1508085QF133 and No. 1608085MF145), and the Research Program of the Anhui Province Education Department (No. KJ2014A088).
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Guo, L., Liang, J., Zhu, Y. et al. Collaborative filtering recommendation based on trust and emotion. J Intell Inf Syst 53, 113–135 (2019). https://doi.org/10.1007/s10844-018-0517-4
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DOI: https://doi.org/10.1007/s10844-018-0517-4