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An emotional recommender system for music - 2020

An Emotional Recommender System For Music

Research Area:  Machine Learning


Nowadays, recommender systems have become essential to users for finding what they need within large collections of items. Meanwhile, recent studies have demonstrated as user personality can effectively provide a more valuable information to significantly improve recommenders performance, especially considering behavioral data captured from social network logs. In this work, we describe a novel music recommendation technique based on the identification of personality traits, moods, and emotions of a single user, starting from solid psychological observations recognized by the analysis of user behavior within a social environment. In particular, users personality and mood have been embedded within a content-based filtering approach to obtain more accurate and dynamic results. Several experiments are then reported to show effectiveness of user personality and mood recognition recommendation, thus, encouraging research in this direction.


Author(s) Name:  Vincenzo Moscato; Antonio Picariello; Giancarlo SperlĂ­

Journal name:  IEEE Intelligent Systems

Conferrence name:  

Publisher name:  IEEE

DOI:   10.1109/MIS.2020.3026000

Volume Information:  Volume: 36, Issue: 5, Sept.-Oct. 1 2021, Page(s): 57 - 68