Considering the cultural background of users is known to improve recommender systems for multimedia items. In this work, we focus on music and analyze user demographics and music listening events in a large corpus (120,000 users, 109 events) from Last.fm to investigate whether similarity between countries in terms of cultural and socio-economic factors is reflected in music taste. To this end, we propose a tag-based model to describe the music taste of a country and correlate the resulting music profiles to Hofstede’s cultural dimensions and the Quality of Government data. Spearman’s rank-order correlation and Quadratic Assignment Procedure indeed indicate statistically significant weak to medium correlations of music taste and several cult...
RELEVANCE: Popularity-based approaches are widely adopted in music recommendation systems, both in i...
In this paper, we describe the LFM-1b User Genre Profile dataset. It provides detailed information o...
While music information retrieval (MIR) has made substantial progress in automatic analysis of audio...
Considering the cultural background of users is known to improve recommender systems for multimedia ...
Music listening is an inherently cultural behavior, which may be shaped by users’ backgrounds and co...
Music listening is an inherently cultural behavior, which may be shaped by users' backgrounds and co...
Music preferences are strongly shaped by the cultural and socio-economic background of the listener,...
We investigate the complex relationship between the factors (i) preference for music mainstream, (ii...
In the field of music recommender systems, country-specific aspects have received little attention, ...
Social connections and cultural aspects play important roles in shaping an individual's preferences....
Music streaming platforms allow users to enjoy music from all over the globe.Such opportunity speeds...
Background Preferences for music can be represented through music features. The widespread prevalenc...
RelevancePopularity-based approaches are widely adopted in music recommendation systems, both in ind...
Abstract. We investigate a range of music recommendation algorithm combinations, score aggregation f...
LFM-1b dataset extended by acoustic track features and cultural cues describing users This datas...
RELEVANCE: Popularity-based approaches are widely adopted in music recommendation systems, both in i...
In this paper, we describe the LFM-1b User Genre Profile dataset. It provides detailed information o...
While music information retrieval (MIR) has made substantial progress in automatic analysis of audio...
Considering the cultural background of users is known to improve recommender systems for multimedia ...
Music listening is an inherently cultural behavior, which may be shaped by users’ backgrounds and co...
Music listening is an inherently cultural behavior, which may be shaped by users' backgrounds and co...
Music preferences are strongly shaped by the cultural and socio-economic background of the listener,...
We investigate the complex relationship between the factors (i) preference for music mainstream, (ii...
In the field of music recommender systems, country-specific aspects have received little attention, ...
Social connections and cultural aspects play important roles in shaping an individual's preferences....
Music streaming platforms allow users to enjoy music from all over the globe.Such opportunity speeds...
Background Preferences for music can be represented through music features. The widespread prevalenc...
RelevancePopularity-based approaches are widely adopted in music recommendation systems, both in ind...
Abstract. We investigate a range of music recommendation algorithm combinations, score aggregation f...
LFM-1b dataset extended by acoustic track features and cultural cues describing users This datas...
RELEVANCE: Popularity-based approaches are widely adopted in music recommendation systems, both in i...
In this paper, we describe the LFM-1b User Genre Profile dataset. It provides detailed information o...
While music information retrieval (MIR) has made substantial progress in automatic analysis of audio...