TransRec++: Translation-based sequential recommendation with heterogeneous feedback

Graphical abstract

References

[1] He R N, Kang W C, McAuley J. Translation-based recommendation. In: Proceedings of the 17th ACM Conference on Recommender Systems. 2017, 161– 169

[2] Peng X G, Chen Y F, Duan Y C, Pan W K, Ming Z. RBPR: role-based Bayesian personalized ranking for heterogeneous one-class collaborative filtering. In: Proceedings of the 24th ACM Conference on User Modeling, Adaptation and Personalisation (Extended Proceedings). 2016

[3] Pan W K , Yang Q , Cai W L , Chen Y F , Zhang Q , Peng X G , Ming Z . Transfer to rank for heterogeneous one-class collaborative filtering. ACM Transactions on Information Systems, 2019, 37( 1): 10–

[4] 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. 2010, 811– 820

[5] He R N, McAuley J. Fusing similarity models with Markov chains for sparse sequential recommendation. In: Proceedings of 16th International Conference on Data Mining. 2016, 191– 200

[6] Zhou M Z, Ding Z Y, Tang J L, Yin D W. Micro behaviors: a new perspective in e-commerce recommender systems. In: Proceedings of the 11th ACM International Conference on Web Search and Data Mining. 2018, 727– 735

[7] Li Z, Zhao H K, Liu Q, Huang Z Y, Mei T, Chen E H. Learning from history and present: Next-item recommendation via discriminatively exploiting user behaviors. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018, 1734−1743

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Higher Education Press 2021