recommend items based on the number of occurrences
Bayesian Personalized Ranking
use Markov Chain、personalized recommendation
use max-pooling、mean-pooling to generate session representation
use GRU for every session. There is no connection between sessions.
use global encoder and local encoder for single session.
use user embedding、GRU for every session. There is a connection between each person's session.
use pooling and attention to model all users' record
use pooling、attention、MLP to model single session
build graph、use GNN
1、Rendle, Steffen , et al. "BPR: Bayesian Personalized Ranking from Implicit Feedback." Conference on Uncertainty in Artificial Intelligence AUAI Press, 2009.
2、Rendle, S., Freudenthaler, C., & Schmidt-Thieme, L. (2010). Factorizing personalized Markov chains for next-basket recommendation. Proceedings of the 19th International Conference on World Wide Web.
3、Wang, Pengfei, et al. "Learning Hierarchical Representation Model for NextBasket Recommendation." (2015):403-412.
4、Session-based Recommendations with Recurrent Neural Networks,2016,ICLR.
5、Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian and Jun Ma (2017). Neural Attentive Session-based Recommendation. In Proceedings of CIKM'17, Singapore, Singapore, Nov 06-10, 2017.
6、HGRU2Rec: Personalizing Session-based Recommendation with Hierarchical Recurrent Neural Networks,2017,RecSys.
7、Sequential Recommender System based on Hierarchical Attention Network,2018,IJCAI.
8、STAMP: Short-Term Attention/Memory Priority Model for Session-based Recommendation,2018,KDD.
9、SR-GNN: Session-based Recommendation with Graph Neural Networks,2019,AAAI.
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