University of Calgary | recommendation system based on Reinforcement Learning

【 title 】Reinforcement Learning based Recommender Systems: A Survey

【 The author team 】M. Mehdi Afsar, Trafford Crump, Behrouz Far

【 Date of publication 】2022.6.15

【 Thesis link 】https://dl.acm.org/doi/pdf/10.1145/3543846

【 Recommended reasons 】 Recommendation system (RS) Has become an integral part of daily life . Traditionally , A recommendation question is considered a classification or prediction question , But now it is generally believed that , Expressing it as a sequential decision problem can better reflect the users - System interaction . therefore , It can be expressed as a Markov decision process （MDP） And through reinforcement learning （RL） Algorithm to solve . And traditional recommendation methods （ Including collaborative filtering and content-based filtering ） Different ,RL Able to process sequentially 、 Dynamic user system interaction , And take into account the long-term user participation . This paper introduces a recommendation system based on reinforcement learning (RLRS) The study of . First recognize and explain RLRS Usually it can be divided into based on RL and DRL Methods . then , A four part RLRS frame , I.e. status representation 、 Strategy optimization 、 Reward formulation and environmental construction , And summarize accordingly RLRS Algorithm . This article uses a variety of charts to highlight emerging themes and depict important trends . Last , Important aspects and challenges that can be solved in the future were discussed .