Temporal Contrastive Pre-Training for Sequential Recommendation
Changxin Tian, Zihan Lin, Shuqing Bian, Jinpeng Wang, Wayne Xin Zhao
2022Proceedings of the 31st ACM International Conference on Information & Knowledge Management22 citationsDOI
Abstract
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.
Topics & Concepts
Leverage (statistics)Computer scienceRecommender systemSequential Pattern MiningTraining (meteorology)Artificial intelligenceTraining setSequence (biology)Machine learningGeneticsPhysicsBiologyMeteorologyRecommender Systems and TechniquesAdvanced Bandit Algorithms ResearchTopic Modeling