no code implementations • 27 Aug 2022 • Ziyang Wang, Huoyu Liu, Wei Wei, Yue Hu, Xian-Ling Mao, Shaojian He, Rui Fang, Dangyang Chen
Different from the previous contrastive learning-based methods for SR, MCLSR learns the representations of users and items through a cross-view contrastive learning paradigm from four specific views at two different levels (i. e., interest- and feature-level).
3 code implementations • 11 Nov 2020 • Shuai Zhang, Huoyu Liu, Aston Zhang, Yue Hu, Ce Zhang, Yumeng Li, Tanchao Zhu, Shaojian He, Wenwu Ou
Furthermore, we present two variants of hypercuboids to enhance the capability in capturing the diversities of user interests.
no code implementations • 29 Feb 2020 • Xiao Xu, Fang Dong, Yanghua Li, Shaojian He, Xin Li
A contextual bandit problem is studied in a highly non-stationary environment, which is ubiquitous in various recommender systems due to the time-varying interests of users.