A Hybrid Recommendation for Music Based on Reinforcement Learning

Abstract

The key to personalized recommendation system is the prediction of users’ preferences. However, almost all existing music recommendation approaches only learn listeners’ preferences based on their historical records or explicit feedback, without considering the simulation of interaction process which can capture the minor changes of listeners’ preferences sensitively. In this paper, we propose a personalized hybrid recommendation algorithm for music based on reinforcement learning (PHRR) to recommend song sequences that match listeners’ preferences better. We firstly use weighted matrix factorization (WMF) and convolutional neural network (CNN) to learn and extract the song feature vectors. In order to capture the changes of listeners’ preferences sensitively, we innovatively enhance simulating interaction process of listeners and update the model continuously based on their preferences both for songs and song transitions. The extensive experiments on real-world datasets validate the effectiveness of the proposed PHRR on song sequence recommendation compared with the state-of-the-art recommendation approaches.