Time-Aware Music Recommender Systems: Modeling the Evolution of Implicit User Preferences and User Listening Habits in A Collaborative Filtering Approach

TL;DR

This paper introduces a time-aware collaborative filtering approach for music recommendation systems that models evolving user preferences and listening habits to improve recommendation accuracy.

Contribution

It presents a novel method that incorporates temporal information to better capture user behavior and preferences in music recommendation systems.

Findings

Outperforms existing methods in accuracy

Effectively models user preference evolution

Enhances both context-aware and context-free recommendations

Abstract

Online streaming services have become the most popular way of listening to music. The majority of these services are endowed with recommendation mechanisms that help users to discover songs and artists that may interest them from the vast amount of music available. However, many are not reliable as they may not take into account contextual aspects or the ever-evolving user behavior. Therefore, it is necessary to develop systems that consider these aspects. In the field of music, time is one of the most important factors influencing user preferences and managing its effects, and is the motivation behind the work presented in this paper. Here, the temporal information regarding when songs are played is examined. The purpose is to model both the evolution of user preferences in the form of evolving implicit ratings and user listening behavior. In the collaborative filtering method proposed in this work, daily listening habits are captured in order to characterize users and provide them with more reliable recommendations. The results of the validation prove that this approach outperforms other methods in generating both context-aware and context-free recommendations