JDRec: Practical Actor-Critic Framework for Online Combinatorial Recommender System

• URL: http://arxiv.org/abs/2207.13311v1
• Date: Wed, 27 Jul 2022 05:47:12 GMT
• Title: JDRec: Practical Actor-Critic Framework for Online Combinatorial Recommender System
• Authors: Xin Zhao (1), Zhiwei Fang (1), Yuchen Guo (2), Jie He (1), Wenlong Chen (1), Changping Peng (1) ((1) JD.com, (2) Tsinghua University)
• Abstract summary: A recommender (CR) system feeds a list of items to a user at a time in the result page, in which the user behavior is affected by both contextual information and items. Despite its importance, it is still a challenge to build a practical CR system, due to the efficiency, dynamics, personalization requirement in online environment.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A combinatorial recommender (CR) system feeds a list of items to a user at a time in the result page, in which the user behavior is affected by both contextual information and items. The CR is formulated as a combinatorial optimization problem with the objective of maximizing the recommendation reward of the whole list. Despite its importance, it is still a challenge to build a practical CR system, due to the efficiency, dynamics, personalization requirement in online environment. In particular, we tear the problem into two sub-problems, list generation and list evaluation. Novel and practical model architectures are designed for these sub-problems aiming at jointly optimizing effectiveness and efficiency. In order to adapt to online case, a bootstrap algorithm forming an actor-critic reinforcement framework is given to explore better recommendation mode in long-term user interaction. Offline and online experiment results demonstrate the efficacy of proposed JDRec framework. JDRec has been applied in online JD recommendation, improving click through rate by 2.6% and synthetical value for the platform by 5.03%. We will publish the large-scale dataset used in this study to contribute to the research community.

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