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Shaoliang Zhang
Researcher at Tencent
Publications - 11
Citations - 67
Shaoliang Zhang is an academic researcher from Tencent. The author has contributed to research in topics: Computer science & Recommender system. The author has an hindex of 2, co-authored 6 publications receiving 13 citations.
Papers
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Proceedings Article
Hierarchical Reinforcement Learning for Integrated Recommendation.
TL;DR: Zhang et al. as mentioned in this paper propose a Hierarchical reinforcement learning framework for integrated recommendation (HRL-Rec), which divides the integrated recommendation into two tasks to recommend channels and items sequentially.
Proceedings ArticleDOI
Personalized Approximate Pareto-Efficient Recommendation
TL;DR: Wang et al. as mentioned in this paper proposed a Personalized Approximate Pareto-Efficient Recommendation (PAPERec) framework for multi-objective recommendation, where users have personalized weights on different objectives.
Proceedings ArticleDOI
Real-time Relevant Recommendation Suggestion
TL;DR: Zhang et al. as mentioned in this paper proposed a real-time relevant recommendation suggestion (R3S) framework, which consists of an item recommender and a box trigger, and extracted features from multiple aspects including feature interaction, semantic similarity and information gain as different experts, and proposed a new Multi-critic multi-gate mixture of experts (M3oE) strategy to jointly consider different experts with multi-head critics.
Journal ArticleDOI
Negative Can Be Positive: Signed Graph Neural Networks for Recommendation
Proceedings ArticleDOI
Multi-granularity Fatigue in Recommendation
TL;DR: A novel multi-granularity fatigue is proposed, modeling user fatigue from coarse to fine on the recommendation feed scenario, where the underexplored global session fatigue and coarse-grained taxonomy fatigue have large impacts.