Abstract
Traditional recommender systems use a single rating for each item. However, this approach is limited because a single overall rating does not provide sufficient information about the reasons that led to a user's overall rating. Therefore, multicriteria recommender systems have been developed to benefit from user preferences expressed across variety of criteria. Recurrent neural networks (RNNs) have been also used in recommendation systems; indeed, they have proven their efficiency in other fields as speech recognition and machine translation. Nevertheless, the use of various RNN structures was restricted to reviews and session-based single-criteria recommender systems. These methods require useful metadata, such as the history of user activity during a session. In this paper, we propose a sequence-aware Long-Short Term Memory (LSTM) RNN with a custom attention mechanism to predict the overall ratings of users. The user's multicriteria ratings are the only needed data for the proposed approach. Thus, we consider every user's multicriteria rating given for an item as a sequence of data for that user. Extensive experiments conducted on real-world data show that the proposed method outperforms baseline approaches.
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References
Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks (2015). arXiv:1511.06939
Mobasher, B., Dai, H., Luo, T., Nakagawa, M.: Using sequential and non-sequential patterns in predictive web usage mining tasks. In: 2002 IEEE International Conference on Data Mining, 2002. Proceedings, pp. 669–672. IEEE (2002)
Ragno, R., Burges, C.J., Herley, C.: Inferring similarity between music objects with application to playlist generation. In: Proceedings of the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval, pp. 73–80 (2005)
Shani, G., Heckerman, D., Brafman, R.I., Boutilier, C.: An MDP-based recommender system. J. Mach. Learn. Res. 6(9) v
Moore, J.L., Chen, S., Turnbull, D., Joachims, T.: Taste over time: the temporal dynamics of user preferences. In: ISMIR, vol. 13, pp. 401–406 (2013)
Quadrana, M., Cremonesi, P., Jannach, D.: Sequence-aware recommender systems. ACM Comput. Surv. (CSUR) 51(4), 1–36 (2018)
Phuong, T.M., Thanh, T.C., Bach, N.X.: Neural session-aware recommendation. IEEE. Access 7, 86884–86896 (2019)
Quadrana, M., Karatzoglou, A., Hidasi, B., Cremonesi, P.: Personalizing session-based recommendations with hierarchical recurrent neural networks. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 130–137 (2017)
Liu, D.Z., Singh, G.: A recurrent neural network based recommendation system. In: International Conference on Recent Trends in Engineering, Science & Technology (2016)
Choe, B., Kang, T., Jung, K.: Recommendation system with hierarchical recurrent neural network for long-term time series. IEEE Access 9, 72033–72039 (2021)
Al-Ghuribi, S.M., Noah, S.A.M.: Multi-criteria review-based recommender system–the state of the art. IEEE Access 7, 169446–169468 (2019)
Adomavicius, G., Manouselis, N., Kwon, Y.: Multi-criteria recommender systems. In: Recommender Systems Handbook, pp. 769–803. Springer, Boston (2011)
Bougteb, Y., Ouhbi, B., Frikh, B., Zemmouri, E.: A deep autoencoder-based hybrid recommender system. Int. J. Mob. Comput. Multimed. Commun. (IJMCMC) 13(1), 1–19 (2022)
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)
Li, P., Tuzhilin, A.: Learning latent multi-criteria ratings from user reviews for recommendations. IEEE Trans. Knowl. Data Eng. (2020)
Kwon, Y.: Improving neighborhood-based CF systems: towards more accurate and diverse recommendations. J. Intell. Inf. Syst. 18(3), 119–135 (2012)
Bougteb, Y., Ouhbi, B., Frikh, B., Zemmouri, E.M.: A deep autoencoder based multi-criteria recommender system. In: The International Conference on Artificial Intelligence and Computer Vision, pp. 56–65. Springer, Cham (2021)
Jannach, D., Lerche, L., Jugovac, M.: Adaptation and evaluation of recommendations for short-term shopping goals. In: Proceedings of the 9th ACM Conference on Recommender Systems, pp. 211–218 (2015)
Zhang, H., Ni, W., Li, X., Yang, Y.: Modeling the heterogeneous duration of user interest in time-dependent recommendation: a hidden semi-Markov approach. IEEE Trans. Syst. Man Cybern.: Syst. 48(2), 177–194 (2016)
Zhu, Q., Zhou, X., Song, Z., Tan, J., Guo, L.: Dan: deep attention neural network for news recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, No. 01, pp. 5973–5980 (2019)
Yuan, W., Wang, H., Yu, X., Liu, N., Li, Z.: Attention-based context-aware sequential recommendation model. Inf. Sci. 510, 122–134 (2020)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Zhao, P., Zhu, H., Liu, Y., Li, Z., Xu, J., Sheng, V.S.: Where to go next: a spatio-temporal LSTM model for next POI recommendation (2018). arXiv:1806.06671
Huang, L., Ma, Y., Wang, S., Liu, Y.: An attention-based spatiotemporal lstm network for next poi recommendation. IEEE Trans. Serv. Comput. 14(6), 1585–1597 (2019)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT press (2016)
Staudemeyer, R.C., Morris, E.R.: Understanding LSTM—a tutorial into long short-term memory recurrent neural networks (2019). arXiv:1909.09586
Luong, M.T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation (2015). arXiv:1508.04025
Liu, G., Guo, J.: Bidirectional LSTM with attention mechanism and convolutional layer for text classification. Neurocomputing 337, 325–338 (2019)
Chan, W., Jaitly, N., Le, Q., Vinyals, O.: (2016) Listen, attend and spell: a neural network for large vocabulary conversational speech recognition. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4960–4964. IEEE
Ying, H., Zhuang, F., Zhang, F., Liu, Y., Xu, G., Xie, X., Wu, J.: Sequential recommender system based on hierarchical attention network. In: IJCAI International Joint Conference on Artificial Intelligence (2018)
Wang, R., Wu, Z., Lou, J., Jiang, Y.: Attention-based dynamic user modeling and deep collaborative filtering recommendation. Expert Syst. Appl. 188, 116036 (2022)
Xia, H., Luo, Y., Liu, Y.: Attention neural collaboration filtering based on GRU for recommender systems. Complex Intell. Syst. 7(3), 1367–1379 (2021)
Yücebaş, S.C.: MovieANN: a hybrid approach to movie recommender systems using multi layer artificial neural networks. Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi 5(2), 214–232 (2019)
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Bougteb, Y., Frikh, B., Ouhbi, B., Zemmouri, E.M. (2024). Attention-Based Recurrent Neural Network for Multicriteria Recommendations. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2023. Lecture Notes in Networks and Systems, vol 823. Springer, Cham. https://doi.org/10.1007/978-3-031-47724-9_18
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DOI: https://doi.org/10.1007/978-3-031-47724-9_18
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