Contrastive Self-supervised Learning in Recommender Systems: A Survey

no code implementations • 17 Mar 2023 • Mengyuan Jing, Yanmin Zhu, Tianzi Zang, Ke Wang

We then introduce a taxonomy based on the key components of the framework, including view generation strategy, contrastive task, and contrastive objective.

Recommendation Systems Self-Supervised Learning

Deep Meta-learning in Recommendation Systems: A Survey

no code implementations • 9 Jun 2022 • Chunyang Wang, Yanmin Zhu, Haobing Liu, Tianzi Zang, Jiadi Yu, Feilong Tang

For each recommendation scenario, we further discuss technical details about how existing methods apply meta-learning to improve the generalization ability of recommendation models.

Meta-Learning Recommendation Systems

A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions

no code implementations • 7 Aug 2021 • Tianzi Zang, Yanmin Zhu, Haobing Liu, Ruohan Zhang, Jiadi Yu

In this survey paper, we first proposed a two-level taxonomy of cross-domain recommendation which classifies different recommendation scenarios and recommendation tasks.

Recommendation Systems

Jointly Modeling Heterogeneous Student Behaviors and Interactions Among Multiple Prediction Tasks

no code implementations • 25 Mar 2021 • Haobing Liu, Yanmin Zhu, Tianzi Zang, Yanan Xu, Jiadi Yu, Feilong Tang

In this paper, we focus on modeling heterogeneous behaviors and making multiple predictions together, since some prediction tasks are related and learning the model for a specific task may have the data sparsity problem.

Transfer Learning