A deep reinforcement learning based long-term recommender system

Recommender systems aim to maximize the overall accuracy for long-term recommendations. However, most of the existing recommendation models adopt a static view, and ignore the fact that recommendation is a dynamic sequential decision-making process. As a result, they fail to adapt to new situations and suffer from the cold-start problem. Although sequential recommendation methods have been gaining attention recently, the objective of long-term recommendation still has not been explicitly addressed since these methods are developed for short-term prediction situations. To overcome these problems, we propose a novel top-N interactive recommender system based on deep reinforcement learning. In our model, the processes of recommendation are viewed as Markov decision processes (MDP), wherein the interactions between agent (recommender system) and environment (user) are simulated by the recurrent neural network (RNN). In addition, reinforcement learning is employed to optimize the proposed model for the purpose of maximizing long-term recommendation accuracy. Experimental results based on several benchmarks show that our model significantly outperforms previous top-N methods in terms of Hit-Rate and NDCG for the long-term recommendation, and can be applied to both cold-start and warm-start scenarios. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

The study introduces a novel top-N interactive recommender system based on deep reinforcement learning, where recommendation processes are viewed as Markov decision processes and optimized through reinforcement learning to maximize long-term recommendation accuracy. Experimental results show that the model outperforms previous top-N methods in long-term recommendation accuracy and can be applied in both cold-start and warm-start scenarios.