Abstract
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.
Highlights
Recent advances in recommender systems and music information retrieval have shown that contextual infor mation is vital for highly personalized results (e.g., Wang et al (2012a); Braunhofer et al (2013); Pichl and Zangerle (2018))
We show that utilizing the cultural background of users together with their general musical preference contributes to improved recommendation quality
8 Conclusion and Future Work The contributions of this work are two-fold: (i) we introduced a novel music-cultural user model that jointly relies on acoustic song features and culture-related features to describe the user’s musical preferences and cultural background and (ii) we proposed a recommender system that leverages these features as contextual information
Summary
Recent advances in recommender systems and music information retrieval have shown that contextual infor mation is vital for highly personalized results (e.g., Wang et al (2012a); Braunhofer et al (2013); Pichl and Zangerle (2018)) In this scope, context can be defined as “conditions or circumstances which affect some thing” (Kaminskas and Ricci, 2012; Adomavicius and Tuzhilin, 2011), where, e.g., environment-related contextual information may include location, time or weather (Kaminskas et al, 2012). We argue that modeling users based on musical properties of the songs they listen to (approximating their musical preference) on the one hand and the user’s cultural background on the other contributes to capturing music-cultural listening patterns. We propose a novel music-cultural user modeling approach to exploit such listening patterns for recommender systems by integrating information about (i) the acoustic qualities of the music users have listened to and (ii) culture-specific information derived from the users’ location/country to describe the user’s likely cultural background
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