Markus Schedl, E. Zangerle, M. Pichl,
"User Models for Culture-Aware Music Recommendation: Fusing Acoustic and Cultural Cues"
, in Schedl, in Transactions of the International Society of Music Information Retrieval, Vol. 3, Nummer 1, 2020
User Models for Culture-Aware Music Recommendation: Fusing Acoustic and Cultural Cues
Sprache des Titels:
Integrating information about the listener?s cultural background when building music recommender systems has recently been identified as a means to improve recommendation quality. In this article, we, therefore, propose a novel approach to jointly model users by their musical preferences and cultural backgrounds. We describe the musical preferences of users by the acoustic features of the songs the users have listened to and characterize the cultural background of users by culture-related socio-economic features that we infer from the user?s country. To evaluate the impact of the proposed user model on recommendation quality, we integrate the model into a culture-aware recommender system. By analyzing a dataset comprising approximately 400 million listening events of about 55,000 users from 36 countries, we show that incorporating both acoustic information of the tracks a user has listened to as well as the cultural background of users in the form of a music-cultural user model contributes to improved recommendation performance. Furthermore, we provide a systematic analysis of the influence of different features on the quality of the provided culture-aware track recommendations. We find that considering acoustic features that model the characteristics of tracks and a user?s musical preferences have the highest impact on recommendation performance. However, adding socio-economic features allows further improving the recommendation quality. In addition, we identify interesting correlations between acoustic characteristics of music preferences and cultural features of populations at the country level.
Sprache der Kurzfassung:
Transactions of the International Society of Music Information Retrieval