Temporal Contrastive Pre-Training for Sequential Recommendation
Changxin Tian, Zihan Lin, Shuqing Bian, Jinpeng Wang, Wayne Xin Zhao · Proceedings of the 31st ACM International Conference on Information & Knowledge Management · 2022
Recently, pre-training based approaches are proposed to leverage self-supervised signals for improving the performance of sequential recommendation. However, most of existing pre-training recommender systems simply model the historical behavior of a user as a sequence, while lack of sufficient consideration on temporal interaction patterns that are useful for modeling user behavior.