Self-Supervised Interest Transfer Network via Prototypical Contrastive Learning for Recommendation

no code implementations • 28 Feb 2023 • Guoqiang Sun, Yibin Shen, Sijin Zhou, Xiang Chen, Hongyan Liu, Chunming Wu, Chenyi Lei, Xianhui Wei, Fei Fang

In this paper, we propose a cross-domain recommendation method: Self-supervised Interest Transfer Network (SITN), which can effectively transfer invariant knowledge between domains via prototypical contrastive learning.

Contrastive Learning

Graph-Enhanced Exploration for Goal-oriented Reinforcement Learning

no code implementations • ICLR 2022 • Jiarui Jin, Sijin Zhou, Weinan Zhang, Tong He, Yong Yu, Rasool Fakoor

Goal-oriented Reinforcement Learning (GoRL) is a promising approach for scaling up RL techniques on sparse reward environments requiring long horizon planning.

Continuous Control graph construction +2

Explore with Dynamic Map: Graph Structured Reinforcement Learning

no code implementations • 1 Jan 2021 • Jiarui Jin, Sijin Zhou, Weinan Zhang, Rasool Fakoor, David Wipf, Tong He, Yong Yu, Zheng Zhang, Alex Smola

In reinforcement learning, a map with states and transitions built based on historical trajectories is often helpful in exploration and exploitation.

reinforcement-learning Reinforcement Learning (RL)

Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning

no code implementations • 18 Jun 2020 • Sijin Zhou, Xinyi Dai, Haokun Chen, Wei-Nan Zhang, Kan Ren, Ruiming Tang, Xiuqiang He, Yong Yu

Interactive recommender system (IRS) has drawn huge attention because of its flexible recommendation strategy and the consideration of optimal long-term user experiences.

Decision Making Recommendation Systems +3