Abstract
Recommender systems generate recommendations based on user profiles, which consist of past interactions of users with items. When user profiles are not available, session-based recommendation can be used instead to make predictions based on sequences of user clicks within short sessions. Although each approach can be used separately, it is desired to utilize both user profiles and session information, and other information such as context, when those are available. In this paper, we propose a Recurrent Neural Networks (RNNs) based method that combines different types of information to generate recommendations. Specifically, we learn user and item representations from user-item interaction data and explore a new type of RNN cells to combine global user embeddings with sequential behavior within each session to generate next item recommendations. The proposed model uses an attention mechanism to adaptively regulate the contributions of different input components based on specific situations. The model can be extended to incorporate other input, such as contextual information. Experimental results on two real-world datasets show that our method outperforms state-of-the-art baselines that use only user or session information.
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References
Bach, N.X., Hai, N.D., Phuong, T.M.: Personalized recommendation of stories for commenting in forum-based social media. Inf. Sci. 352–353, 48–60 (2016)
Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl.-Based Syst. 46, 109–132 (2013)
Celma, O.: Music Recommendation and Discovery: The Long Tail, Long Fail, and Long Play in the Digital Music Space. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13287-2
Cho, K., et al.: Learning phrase representations using RNN encoder–decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1724–1734 (2014)
Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. (TOIS) 22(1), 143–177 (2004)
Donkers, T., Loepp, B., Ziegler, J.: Sequential user-based recurrent neural network recommendations. In: Proceedings of the Eleventh ACM Conference on Recommender Systems (RecSys), pp. 152–160 (2017)
Figueiredo, F., Ribeiro, B., Almeida, J.M., Faloutsos, C.: TribeFlow: mining & predicting user trajectories. In: Proceedings of the 25th International Conference on World Wide Web (WWW), pp. 695–706 (2016)
Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)
Hidasi, B., et al.: Parallel recurrent neural network architectures for feature-rich session-based recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems (RecSys), pp. 241–248 (2016)
Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. In: Proceedings of the International Conference on Learning Representations (ICLR) (2016)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., Ma, J.: Neural attentive session-based recommendation. In: Proceedings of the 2017 ACM Conference on Information and Knowledge Management (CIKM), pp. 1419–1428 (2017)
Phuong, N.D., Thang, L.Q., Phuong, T.M.: A graph-based method for combining collaborative and content-based filtering. In: Ho, T.-B., Zhou, Z.-H. (eds.) PRICAI 2008. LNCS (LNAI), vol. 5351, pp. 859–869. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-89197-0_80
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 (RecSys), pp. 130–137 (2017)
Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized markov chains for next-basket recommendation. In: Proceedings of the 19th International Conference on World Wide Web (WWW), pp. 811–820 (2010)
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 (WWW), pp. 285–295 (2001)
Tan, Y.K., Xu, X., Liu, Y.: Improved recurrent neural networks for session-based recommendations. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp. 17–22 (2016)
Tuan, T.X., Phuong, T.M.: 3D convolutional networks for session-based recommendation with content features. In: Proceedings of the Eleventh ACM Conference on Recommender Systems (RecSys), pp. 138–146 (2017)
Vasile, F., Smirnova, E., Conneau, A.: Meta-Prod2Vec: product embeddings using side-information for recommendation. In: Proceedings of the 10th ACM Conference on Recommender Systems (RecSys), pp. 225–232 (2016)
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We would like to thank FPT for financial support, which made this work possible.
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Phuong, T.M., Thanh, T.C., Bach, N.X. (2018). Combining User-Based and Session-Based Recommendations with Recurrent Neural Networks. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11301. Springer, Cham. https://doi.org/10.1007/978-3-030-04167-0_44
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DOI: https://doi.org/10.1007/978-3-030-04167-0_44
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