Integrating Sentiment Features in Factorization Machines: Experiments on Music Recommender Systems

Music recommender systems play a pivotal role in catering to diverse user preferences and fostering personalized listening experiences. At the same time, sentiments can profoundly influence music by shaping its emotional expression and evoking specific moods onto listeners. Expressed in textual content, these sentiments may be analyzed through natural language processing techniques to gauge emotions or opinions, hopefully increasing their relevance when exploited for recommendation. This work aims to investigate how to better integrate such information and understand its potential impact on personalized music suggestions, attempting to enhance recommendation models by incorporating sentiment features into factorization machines. For this purpose, a dataset was collected from Last.fm and enhanced with sentiment information extracted from Wikipedia. Empirical results evidence that not all sentiment-related features are equally useful, showing that each tested factorization machine approach varies in sensitivity to these features. Source code and data are available at https://github.com/abellogin/SentiFMRecSys.