Abstract
Neighbor-based recommender systems are highly valued for their interpretability. These systems focus on determining the similarity between users to find neighbor sets. In this paper, we combine observed ratings and reviews from users to calculate their similarity, named RaReSi. This combination is reflected not only in the calculation of user similarity but also in the transformation of the user representation space. Thus, our method effectively addresses the issue of sparse data, a typical challenge in the field of recommender systems. Experimental results on datasets Baby, Tools-Home Improvement, and Beauty show that our proposed method yields better RMSE results than rating-only, review-only, and other combined methods.
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References
Valcarce, D., Landin, A., Parapar, J., Barreiro, Á.: Collaborative filtering embeddings for memory-based recommender systems. Eng. Appl. Artif. Intell. 85, 347–356 (2019)
Khan, Z.Y., Niu, Z., Yousif, A.: Joint deep recommendation model exploiting reviews and metadata information. Neurocomputing 402, 256–265 (2020)
Lima, G.R., Mello, C.E., Lyra, A., Zimbrao, G.: Applying landmarks to enhance memory-based collaborative filtering. Inf. Sci. 513, 412–428 (2020)
Cai, W., Pan, W., Liu, J., Chen, Z., Ming, Z.: k-Reciprocal nearest neighbors algorithm for one-class collaborative filtering. Neurocomputing 381, 207–216 (2020)
Valcarce, D., Parapar, J., Barreiro, Á.: Language models for collaborative filtering neighborhoods. In: European Conference on Information Retrieval, pp. 614–625. Springer, Cham (2016)
Nam, L.N.H.: Incorporating textual reviews in the learning of latent factors for recommender systems. Electron. Commer. Res. Appl. 52, 101133 (2022)
Chehal, D., Gupta, P., Gulati, P.: Implementation and comparison of topic modeling techniques based on user reviews in e-commerce recommendations. J. Ambient. Intell. Humaniz. Comput. 12(5), 5055–5070 (2021)
Hernández-Rubio, M., Cantador, I., Bellogín, A.: A comparative analysis of recommender systems based on item aspect opinions extracted from user reviews. User Model. User-Adap. Inter. 29(2), 381–441 (2019)
Hoang, B.N.M., Vy, H.T.H., Hong, T.G., Hang, V.T.M., Nhung, H.L.T.K.: Using bert embedding to improve memory-based collaborative filtering recommender systems. In: 2021 RIVF International Conference on Computing and Communication Technologies (RIVF), pp. 1–6. IEEE (2021)
Aggarwal, C.C.: Recommender Systems, vol. 1. Springer International Publishing, Cham (2016)
Gazdar, A., Hidri, L.: A new similarity measure for collaborative filtering based recommender systems. Knowl.-Based Syst. 188, 105058 (2020)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 1–19 (2009)
Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: ACM SIGIR Forum, vol. 51, No. 2, pp. 227–234. ACM, New York, NY, USA (2017)
Koutrika, G., Bercovitz, B., Garcia-Molina, H.F.: Expressing and combining flexible recommendations. In: Proceedings of the 35th SIGMOD International Conference on Management of Data (SIGMOD’09), Providence, RI, USA, vol. 29
Musto, C., de Gemmis, M., Semeraro, G., Lops, P.: A multi-criteria recommender system exploiting aspect-based sentiment analysis of users’ reviews. In: Proceedings of the eleventh ACM conference on recommender systems, pp. 321–325 (2017)
Ghasemi, N., Momtazi, S.: Neural text similarity of user reviews for improving collaborative filtering recommender systems. Electron. Commer. Res. Appl. 45, 101019 (2021)
Nam, L.N.H.: Towards comprehensive profile aggregation methods for group recommendation based on the latent factor model. Expert Syst. Appl. 185, 115585 (2022)
Wang, W., Zhang, G., Lu, J.: Member contribution-based group recommender system. Decis. Support Syst. 87, 80–93 (2016)
Ortega, F., Hernando, A., Bobadilla, J., Kang, J.H.: Recommending items to group of users using matrix factorization based collaborative filtering. Inf. Sci. 345, 313–324 (2016)
Shen, R.P., Zhang, H.R., Yu, H., Min, F.: Sentiment based matrix factorization with reliability for recommendation. Expert Syst. Appl. 135, 249–258 (2019)
Sanh, V., Debut, L., Chaumond, J., Wolf, T.: DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108 (2019)
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This research is funded by the University of Science, VNU-HCM under grant number CNTT 2022-04.
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Vy, H.T.H., Ha, D.T.T., Hong, T.G., Vu, T.M.H., Pham-Nguyen, C., Nam, L.N.H. (2023). RaReSi: An Approach Combining Ratings and Reviews to Measure User Similarity in Neighbor-Based Recommender Systems. In: Nguyen, N.T., et al. Advances in Computational Collective Intelligence. ICCCI 2023. Communications in Computer and Information Science, vol 1864. Springer, Cham. https://doi.org/10.1007/978-3-031-41774-0_2
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DOI: https://doi.org/10.1007/978-3-031-41774-0_2
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