JDRec: Practical Actor-Critic Framework for Online Combinatorial Recommender System
release_eaarntabvvg7nmcotebtrjmrmy
by
Xin Zhao
2022
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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