Neural Network Based Next-Song Recommendation

TL;DR

This paper introduces CNN-rec, a neural network-based next-song recommender inspired by NLP techniques, which effectively models sequential listening patterns and outperforms traditional methods.

Contribution

The paper presents a novel neural network architecture for next-song recommendation, leveraging sequential data and NLP-inspired methods, with competitive performance against existing systems.

Findings

CNN-rec outperforms classic recommendation systems

Achieves comparable results to state-of-the-art methods

Effectively models sequential listening patterns

Abstract

Recently, the next-item/basket recommendation system, which considers the sequential relation between bought items, has drawn attention of researchers. The utilization of sequential patterns has boosted performance on several kinds of recommendation tasks. Inspired by natural language processing (NLP) techniques, we propose a novel neural network (NN) based next-song recommender, CNN-rec, in this paper. Then, we compare the proposed system with several NN based and classic recommendation systems on the next-song recommendation task. Verification results indicate the proposed system outperforms classic systems and has comparable performance with the state-of-the-art system.