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Article
Affiliation(s)

School of Science and Technology, The Open University of Hong Kong, Hong Kong, China

ABSTRACT

Session-based recommendation predicts the user’s next action by exploring the item dependencies in an anonymous session. Most of the existing methods are based on the assumption that each session has a single intention, items irrelevant to the single intention will be regarded as noises. However, in real-life scenarios, sessions often contain multiple intentions. This paper designs a multi-channel Intention-aware Recurrent Unit (TARU) network to further mining these noises. The multi-channel TARU explicitly group items into the different channels by filtering items irrelevant to the current intention with the intention control unit. Furthermore, we propose to use the attention mechanism to adaptively generate an effective representation of the session’s final preference for the recommendation. The experimental results on two real-world datasets denote that our method performs well in session recommendation tasks and achieves improvement against several baselines on the general metrics. 

KEYWORDS

Intention-aware network; Session-based recommendation; Recommendation

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References
Cen, Y., Zhang, J., Zou, X., Zhou, C., Yang, H., & Tang, J. (2020). Controllable multi-interest framework for recommendation. Paper presented at the Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.
Guo, C., Zhang, M., Fang, J., Jin, J., & Pan, M. (2020). Session-based recommendation with hierarchical leaping networks. Paper presented at the Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval.
Hidasi, B., Karatzoglou, A., Baltrunas, L., & Tikk, D. J. A. P. A. (2015). Session-based recommendations with recurrent neural networks. 
Kang, W.-C., & McAuley, J. (2018). Self-attentive sequential recommendation. Paper presented at the 2018 IEEE International Conference on Data Mining (ICDM).
Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., & Ma, J. (2017). Neural attentive session-based recommendation. Paper presented at the Proceedings of the 2017 ACM on Conference on Information and Knowledge Management.
Liu, Q., Wu, S., Wang, L., & Tan, T. (2016). Predicting the next location: A recurrent model with spatial and temporal contexts. Paper presented at the Proceedings of the AAAI Conference on Artificial Intelligence.
Liu, Q., Zeng, Y., Mokhosi, R., & Zhang, H. (2018). STAMP: short-term attention/memory priority model for session-based recommendation. Paper presented at the Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.
Pan, Z., Chen, W., & Chen, H. J. I. A. (2020). A hybrid-Preference neural model for basket-sensitive item recommendation. 8, 226131-226141. 
Qu, S., Yuan, F., Guo, G., Zhang, L., & Wei, W. J. A. P. A. (2020). CmnRec: Sequential recommendations with Chunk-accelerated memory network. 
Rendle, S., Freudenthaler, C., & Schmidt-Thieme, L. (2010). Factorizing personalized markov chains for next-basket recommendation. Paper presented at the Proceedings of the 19th international conference on World wide web.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., & Polosukhin, I. (2017). Attention is all you need. arXiv preprint arXiv:1706.03762.
Wang, S., Hu, L., Wang, Y., Sheng, Q. Z., Orgun, M. A., & Cao, L. (2019). Modeling multi-purpose sessions for next-item recommendations via mixture-channel purpose routing networks. Paper presented at the IJCAI.
Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., & Tan, T. (2019). Session-based recommendation with graph neural networks. Paper presented at the Proceedings of the AAAI Conference on Artificial Intelligence.
Yu, F., Liu, Q., Wu, S., Wang, L., & Tan, T. (2016). A dynamic recurrent model for next basket recommendation. Paper presented at the Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval.
Zhang, J., Hao, B., Chen, B., Li, C., Chen, H., & Sun, J. (2019). Hierarchical reinforcement learning for course recommendation in MOOCs. Paper presented at the Proceedings of the AAAI Conference on Artificial Intelligence.

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