Abstract
The user feedback information based Collaborative Filtering algorithm is often used to discover the hidden preferences of users and potential features of items in Recommender System for the past few years. Nevertheless, the potential relationship between different users is often ignored in the current Collaborative Filtering algorithm which contained in the user feedback information. To enhance the precision of Rating Prediction task in the Recommender System, this paper structures a new Rating Prediction method named the Review-based Rating Prediction (RRP) to explore the potential relationship among the different users. Through analyzing the reviews text, the method can derive the users’ potential relationship. Moreover, The resulting potential user relationship data was fused into the rating prediction task. The results of our experiments on open data sets suggest that our proposed model has a performance ascension over the traditional rating prediction methods.
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References
Ng, J.P.: Recommender systems handbook. Comput. Rev. 57(8), 470–474 (2016)
Wang, W., Chen, Z., Liu, J., Qi, Q., Zhao, Z.: User-based collaborative filtering on cross domain by tag transfer learningUMI. In: Proceedings of the 1st International Workshop on Cross Domain Knowledge Discovery in Web and Social Network Mining, vol. 2, pp. 10–17 (2012)
Deng, X., Deng, Z.R., Liang, X.U., Xie, P.: Optimized collaborative filtering recommendation algorithm. Comput. Eng. Des. 37(5), 1259–1264 (2016)
Chen, T., Hong, L., Shi, Y., Sun, Y.: Joint text embedding for personalized content-based recommendation. arXiv preprint arXiv:1706.01084 (2017)
Zhao, X., Zhang, L., Xia, L., Ding, Z., Yin, D., Tang, J.: Deep reinforcement learning for list-wise recommendations. arXiv preprint arXiv:1801.00209 (2017)
Zhao, G., Qian, X., Lei, X., Mei, T.: Service quality evaluation by exploring social users’ contextual information. IEEE Trans. Knowl. Data Eng. 28(12), 3382–3394 (2016)
Févotte, C., Idier, J.: Algorithms for nonnegative matrix factorization with the beta-divergence. Neural Comput. 23(9), 2421–2456 (2010)
Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. Adv. Neural. Inf. Process. Syst. 20, 1257–1264 (2007)
Yang, B., Lei, Y., Liu, J., Li, W.: Social collaborative filtering by trust. IEEE Trans. Pattern Anal. Mach. Intell. 39(8), 1633–1647 (2016)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Ma, T., et al.: Social network and tag sources based augmenting collaborative recommender system. IEEE Trans. Inf. Syst. 98(4), 902–910 (2015)
Ganu, G., Kakodkar, Y., Marian, A.: Improving the quality of predictions using textual information in online user reviews. Inf. Syst. 38(1), 1–15 (2013)
Lei, X., Qian, X., Zhao, G.: Rating prediction based on social sentiment from textual reviews. IEEE Trans. Multimed. 18(9), 1910–1921 (2016)
Hui, N., Zhe, R.: Review helpfulness prediction research based on review sentiment feature sets. Data Anal. Knowl. Discov. 31(7–8), 113–121 (2015)
Shi, W., Wang, L., Qin, J.: User embedding for rating prediction in SVD++-based collaborative filtering. Symmetry 12(1), 121 (2020)
Shi, W., Wang, L., Qin, J.: Extracting user influence from ratings and trust for rating prediction in recommendations. Sci. Rep. 10(1), 1–13 (2020)
Wang, H., et al.: Ripplenet: propagating user preferences on the knowledge graph for recommender systems. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 417–426. ACM (2018)
Quispe, L.C., Luna, J.E.O.: A content-based recommendation system using TrueSkill. In: 2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI), pp. 203–207. IEEE (2015)
Chae, D.K., Kang, J.S., Kim, S.W., Lee, J.T.: Cfgan: a generic collaborative filtering framework based on generative adversarial networks. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 137–146. ACM (2018)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, pp. 175–186. ACM (1994)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295 (2001)
Guo, G., Zhang, J., Yorke-Smith, N.: Trustsvd: collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 124–132 (2015)
Jakob, N., Weber, S.H., Müller, M.C., Gurevych, I.: Trustsvd: beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations. In: Proceedings of the 1st International CIKM Workshop on Topic-sentiment Analysis for Mass Opinion, pp. 57–64 (2009)
Li, X., Xu, G., Chen, E., Li, L.: Learning user preferences across multiple aspects for merchant recommendation. In: 2015 IEEE International Conference on Data Mining, pp. 865–870 (2015)
Almahairi, A., Kastner, K., Cho, K., Courville, A.: Learning distributed representations from reviews for collaborative filtering. In: Proceedings of the 9th ACM Conference on Recommender Systems, pp. 147–154 (2015)
Lou, P., Zhao, G., Qian, X., Wang, H., Hou, X.: Schedule a rich sentimental travel via sentimental POI mining and recommendation. In: 2016 IEEE Second International Conference on Multimedia Big Data (BigMM), pp. 33–40 (2016)
Rendle, S.: Factorization machines. In: 2010 IEEE International Conference on Data Mining, pp. 995–1000 (2010)
Zhang, W., Ding, G., Chen, L., Li, C., Zhang, C.: Generating virtual ratings from Chinese reviews to augment online recommendations. In: ACM Transactions on Intelligent Systems and Technology (TIST), pp. 1–17 (2013)
Yang, X., Steck, H., Liu, Y.: Circle-based recommendation in online social networks. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1267–1275 (2012)
Chawla, N., Wang, W.: Collaborative user network embedding for social recommender systems. In: Proceedings of the 2017 SIAM International Conference on Data Mining, pp. 381–389 (2017)
Zhang, Y., Lai, G., Zhang, M., Zhang, Y., Liu, Y., Ma, S.: Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 83–92 (2014)
Ackonwledgment
This research was funded by the Natural Science Foundation of Xinjiang Uygur Autonomous Region grant number 2020D01C034, Tianshan Innovation Team of Xinjiang Uygur Autonomous Region grant number 2020D14044, National Science Foundation of China under Grant U1903213, 61771416 and 62041110, the National Key R&D Program of China under Grant 2018YFB1403202, Creative Research Groups of Higher Education of Xinjiang Uygur Autonomous Region under Grant XJEDU2017T002.
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Shi, W., Wang, L., Cheng, S., Li, Y. (2021). User Reviews Based Rating Prediction in Recommender System. In: Fang, L., Chen, Y., Zhai, G., Wang, J., Wang, R., Dong, W. (eds) Artificial Intelligence. CICAI 2021. Lecture Notes in Computer Science(), vol 13069. Springer, Cham. https://doi.org/10.1007/978-3-030-93046-2_65
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