A Stable Deep Reinforcement Learning Framework for Recommendation

Ruochen Liu

doi:10.1109/MIS.2022.3145503

Return

A Stable Deep Reinforcement Learning Framework for Recommendation

2022-05-01

PRE

Ruochen Liu *

Dawei Jiang

Xilong Zhang

DOI：10.1109/MIS.2022.3145503

Original

Original request for help

Save

Abstract

En 中文

Recommender system (RS) solves the problem of information overload, which is crucial in industrial fields. Recently, reinforcement learning (RL) combined with RS has attracted researchers' attention. These new methods model the interaction between RS and users as a process of serialization decision-making. However, these studies suffer from several disadvantages: 1) they fail to model the accumulated long-term interest tied to high reward, and 2) these algorithms need a lot of interactive data to learn a good strategy and are unstable in the scenario of recommendation. In this article, we propose a stable reinforcement learning framework for recommendation. We redefine the Markov decision process of RL-based recommendation, and add a stable module to model high feedback behavior of users. Second, an advanced RL algorithm is introduced to ensure stability and exploratory. The experiments verify the effectiveness of the proposed algorithm.

Keywords:

Reinforcement learning

Data models

Intelligent systems

Training data

Entropy

Stability analysis

Optimization