User Models for Culture-Aware Music Recommendation: Fusing Acoustic and Cultural Cues

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 socioeconomic 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 socioeconomic 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.

Support the underground: characteristics of beyond-mainstream music listeners

“…Additionally, since previous research has shown that the listener's cultural background impacts the quality of music recommendations [47], we plan to compare the cultural and socioeconomic aspects of beyond-mainstream and mainstream music listeners. We plan to employ these aspects by means of Hofstede's cultural dimensions [82] and the World Happiness Report [83].…”

“…We base our work on a dataset gathered from the Last.fm music platform, which we considerably enrich with the music tracks' acoustic features (see Sect. 3.1) [48]. Additionally, we combine this data with mainstreaminess information of Last.fm users (see Sect.…”

“…To study characteristics of beyond-mainstream users and their listening preferences, we create a novel dataset called LFM-BeyMS that contains the required information for such analyses. We base our work on a dataset gathered from the Last.fm music platform, which we considerably enrich with the music tracks' acoustic features (see Section 3.1) [47]. Additionally, we combine this data with mainstreaminess information of Last.fm users (see Section 3.3) as well as music genre information to identify beyond-mainstream listeners and music (see Section 3.4).…”

“…To enrich the LFM-1b dataset to suit our task, we utilize our previously created CultMRS music recommendation dataset [49]. This dataset contains 55,191 users, who have listened to a total of 26,022,625 distinct tracks, captured by a total of 807,890,921 listening events [47].…”

Neural content-aware collaborative filtering for cold-start music recommendation

“…We will also explore more refined sampling strategy [64,45] in order to reduce the computational time while keeping the performance high. Besides, our work can be extended to handle other modalities in order to fully exploit the available content such as artists biographies [65] or musical tags [16,66,28], but also contextual data such as culture [67] or location [5]. Finally, alternative architectures could be exploited, such as convolutional networks that directly extract content features from the raw audio data [36] in an end-to-end fashion.…”