Abstract
Recommendation models that use collaborative filtering consider the influence of friends and neighbors when recommending suitable items for a target user. Most of these neighborhood-based models use the actual ratings from neighbors to predict the ratings of the target user toward target items, which often leads to a low accuracy prediction caused by the improper rating-range problem. Recently, rating conversion methods have been proposed to address this issue. Because each friend/neighbor can have a different level of influence on the target user, we propose a friend module, which converts their ratings to match the target user’s perspective and assigns different weight to each user before modeling latent relations and predictions. In rating conversion, ratings that involve explicit feedback are important. Instead of the traditional approach to user embedding, we propose a novel approach that uses explicit feedback. This can express user features better than traditional methods and can then be used to convert ratings to match the target user’s perspective. For better representation and recommendation, we also learn latent relations between each user and item by adopting knowledge graph ideas, which leads to more accurate results. The FilmTrust and MovieLens datasets are used in experiments comparing the proposed method with existing methods. This evaluation showed that our model is more accurate than existing methods.
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Bordes, A., Usunier, N., Garcia-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, NIPS 2013, vol. 2, pp. 2787–2795. Curran Associates Inc., USA (2013). http://dl.acm.org/citation.cfm?id=2999792.2999923
Chalermpornpong, W., Maneeroj, S., Atsuhiro, T.: Rating pattern formation for better recommendation. In: 2013 24th International Workshop on Database and Expert Systems Applications, pp. 146–151 (August 2013). https://doi.org/10.1109/DEXA.2013.23
Chen, C., Zhang, M., Liu, Y., Ma, S.: Neural attentional rating regression with review-level explanations. In: Proceedings of the 2018 World Wide Web Conference, WWW 2018, pp. 1583–1592. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE (2018). https://doi.org/10.1145/3178876.3186070
Chen, C., Zhang, M., Liu, Y., Ma, S.: Social attentional memory network: modeling aspect- and friend-level differences in recommendation. In: Proceedings of the 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, pp. 177–185. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3289600.3290982
Desrosiers, C., Karypis, G.: A comprehensive survey of neighborhood-based recommendation methods. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 107–144. Springer, Boston, MA (2011). https://doi.org/10.1007/978-0-387-85820-3_4
Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)
Dziugaite, G.K., Roy, D.M.: Neural network matrix factorization. CoRR abs/1511.06443 (2015). http://arxiv.org/abs/1511.06443
Guo, Q., et al.: A survey on knowledge graph-based recommender systems. arXiv abs/2003.00911 (2020)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, WWW 2017, pp. 173–182. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE (2017). https://doi.org/10.1145/3038912.3052569
Hsieh, C.K., Yang, L., Cui, Y., Lin, T.Y., Belongie, S., Estrin, D.: Collaborative metric learning. In: Proceedings of the 26th International Conference on World Wide Web, WWW 2017, pp. 193–201. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE (2017). https://doi.org/10.1145/3038912.3052639
Jin, R., Si, L.: A study of methods for normalizing user ratings in collaborative filtering. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2004, pp. 568–569. ACM, New York (2004). https://doi.org/10.1145/1008992.1009124
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009). https://doi.org/10.1109/MC.2009.263
Lathia, N., Hailes, S., Capra, L.: Trust-based collaborative filtering. In: Karabulut, Y., Mitchell, J., Herrmann, P., Jensen, C.D. (eds.) Trust Management II, pp. 119–134. Springer, Boston (2008). https://doi.org/10.1007/978-0-387-09428-1_8
Li, M., Tei, K., Fukazawa, Y.: An efficient co-attention neural network for social recommendation. In: IEEE/WIC/ACM International Conference on Web Intelligence, WI 2019, pp. 34–42. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3350546.3352498
Qiao, C., et al.: A new method of region embedding for text classification. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=BkSDMA36Z
Ricci, F., Rokach, L., Shapira, B., Kantor, P.B.: Recommender Systems Handbook, 1st edn. Springer, Heidelberg (2010). https://doi.org/10.1007/978-0-387-85820-3
Sun, Z., Deng, Z., Nie, J., Tang, J.: RotatE: Knowledge graph embedding by relational rotation in complex space. CoRR abs/1902.10197 (2019). http://arxiv.org/abs/1902.10197
Tay, Y., Anh Tuan, L., Hui, S.C.: Latent relational metric learning via memory-based attention for collaborative ranking. In: Proceedings of the 2018 World Wide Web Conference, WWW 2018, pp. 729–739. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE (2018). https://doi.org/10.1145/3178876.3186154
Tengkiattrakul, P., Maneeroj, S., Takasu, A.: Translation-based embedding model for rating conversion in recommender systems. In: IEEE/WIC/ACM International Conference on Web Intelligence, WI 2019, pp. 217–224. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3350546.3352521
Vaswani, A., et al.: Attention is all you need. CoRR abs/1706.03762 (2017). http://arxiv.org/abs/1706.03762
Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: a survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29(12), 2724–2743 (2017). https://doi.org/10.1109/TKDE.2017.2754499
Zhang, S., Tay, Y., Yao, L., Wu, B., Sun, A.: DeepRec: an open-source toolkit for deep learning based recommendation. CoRR abs/1905.10536 (2019). http://arxiv.org/abs/1905.10536
Zhang, S., Yao, L., Sun, A., Tay, Y.: Deep learning based recommender system: a survey and new perspectives. ACM Comput. Surv. 52(1), 38 (2019). https://doi.org/10.1145/3285029
Zhang, Y., Wang, J., Luo, J.: Knowledge graph embedding based collaborative filtering. IEEE Access 8, 134553–134562 (2020). https://doi.org/10.1109/ACCESS.2020.3011105
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Tengkiattrakul, P., Maneeroj, S., Takasu, A. (2021). Attentive Hybrid Collaborative Filtering for Rating Conversion in Recommender Systems. In: Brambilla, M., Chbeir, R., Frasincar, F., Manolescu, I. (eds) Web Engineering. ICWE 2021. Lecture Notes in Computer Science(), vol 12706. Springer, Cham. https://doi.org/10.1007/978-3-030-74296-6_12
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