Abstract
Sequential recommendation is a significant task that predicts the next items given user historical transaction sequences. It is often reduced to a multi-classification task with the historical sequence as the input, and the next item as the output class label. Sequence representation learning in the multi-classification task is of our main concern. The item frequency usually follows the long tail distribution in recommendation systems, which will lead to the imbalanced classification problem. This item imbalance poses a great challenge for sequence representation learning. In this paper, we propose a Robust Sequence Embedding method for the recommendation, RoSE for short. RoSE improves the recommendation performance from two perspectives. We propose a balanced k-plet sampling strategy to make each training batch balanced at the data level and propose the triplet constraint for each training sequence to make sure of balance and robust distribution in feature space at the algorithmic level. Comprehensive experiments are conducted on three benchmark datasets and RoSE shows promising results in the face of item imbalance.
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References
Devooght, R., Bersini, H.: Collaborative filtering with recurrent neural networks. arXiv preprint arXiv:1608.07400 (2016)
Hoffer, E., Ailon, N.: Deep metric learning using triplet network. In: Feragen, A., Pelillo, M., Loog, M. (eds.) SIMBAD 2015. LNCS, vol. 9370, pp. 84–92. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24261-3_7
Huang, C., Li, Y., Change Loy, C., Tang, X.: Learning deep representation for imbalanced classification (2016)
Khan, S.H., Hayat, M., Bennamoun, M., Sohel, F.A., Togneri, R.: Cost-sensitive learning of deep feature representations from imbalanced data. IEEE Trans. Neural Netw. Learn. Syst. 29(8), 3573–3587 (2018)
Ko, Y.J., Maystre, L., Grossglauser, M.: Collaborative recurrent neural networks for dynamic recommender systems. In: JMLR: Workshop and Conference Proceedings, vol. 63 (2016)
Krawczyk, B.: Learning from imbalanced data: open challenges and future directions. Prog. Artif. Intell. 5(4), 221–232 (2016). https://doi.org/10.1007/s13748-016-0094-0
Lin, X., Niu, S., Wang, Y., Li, Y.: K-plet recurrent neural networks for sequential recommendation. In: SIGIR, ACM (2018)
Niu, S., Zhang, R.: Collaborative sequence prediction for sequential recommender. In: CIKM. ACM (2017)
Rendle, S., Freudenthaler, C.: Improving pairwise learning for item recommendation from implicit feedback. In: WSDM (2014)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: UAI, pp. 452–461 (2009)
Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized Markov chains for next-basket recommendation. In: WWW (2010)
Zimdars, A., Chickering, D.M., Meek, C.: Using temporal data for making recommendations. In: UAI (2001)
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Zhang, R., Niu, S., Li, Y. (2020). Robust Sequence Embedding for Recommendation. In: Li, G., Shen, H., Yuan, Y., Wang, X., Liu, H., Zhao, X. (eds) Knowledge Science, Engineering and Management. KSEM 2020. Lecture Notes in Computer Science(), vol 12275. Springer, Cham. https://doi.org/10.1007/978-3-030-55393-7_11
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DOI: https://doi.org/10.1007/978-3-030-55393-7_11
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