Social Recommendation System Based on Multi-agent Deep Reinforcement Learning

Abstract

Currently, deep reinforcement learning is widely used to transform the recommendation process into a sequential task. Despite this, the recommendation system continues to be plagued by data sparsity and cold start issues as a result of high online costs and a sparse user-item rating matrix. To address the aforementioned issues, this article proposes a social multi-agent reinforcement learning framework (MATR). Two agents collaborate within the framework to enhance the performance of conventional solutions. Specifically, one agent is responsible for capturing the dynamic preferences of users based on their interaction with the system. The other agent leverages a ubiquitous social network to minimise cold starts and data sparsity for some users. In addition, we designed a module for learning state representations from social networks and user rating matrices. To optimise the use of social networks in the state representation module, in addition to first-order social neighbour features, we used trust inference to discover high-level social neighbour features, and we integrated Feature aggregation modelling state representation with graph convolutional neural networks and attention mechanisms. Our method has been shown to be effective and advantageous using actual data sets.