Investigating country-specific music preferences and music recommendation algorithms with the LFM-1b dataset

Knees

et al. 2018

Multimed Tools Appl

Self Cite

Online activities such as social networking, online shopping, and consuming multi-media create digital traces, which are often analyzed and used to improve user experience and increase revenue, e. g., through better-fitting recommendations and more targeted marketing. Analyses of digital traces typically aim to find user traits such as age, gender, and nationality to derive common preferences. We investigate to which extent the music listening habits of users of the social music platform Last.fm can be used to predict their age, gender, and nationality. We propose a feature modeling approach building on Term Frequency-Inverse Document Frequency (TF-IDF) for artist listening information and artist tags combined with additionally extracted features. We show that we can substantially outperform a baseline majority voting approach and can compete with existing approaches. Further, regarding prediction accuracy vs. available listening data we show that even one single listening event per user is enough to outperform the baseline in all prediction tasks. We also compare the performance of our algorithm for different user groups and discuss possible prediction errors and how to mitigate them. We conclude that personal information can be derived from music listening information, which indeed can help better tailoring recommendations, as we illustrate with the use case of a music recommender system that can directly utilize the user attributes predicted by our algorithm to increase the quality of it's recommendations.

Section: Dataset Selection and Characteristicssupporting

confidence: 68%

“…These groups contain the users in the age intervals [6][7][8][9][10][11][12][13][14][15][16][17], [18][19][20][21], [22][23][24][25], [26][27][28][29][30], [31][32][33][34][35][36][37][38][39][40] Figure 1 shows the distribution of users in these age groups.…”

Section: Age Groupsmentioning

confidence: 99%

Predicting user demographics from music listening information

Krismayer

Knees

et al. 2018

Multimed Tools Appl

Self Cite

“…Please note that artist IDs (on the x-axis) are sorted with respect to their global popularity in regards to the respective measure (AF, LF, or AF-ILF). As can be seen, the AF and even more the LF measures are not suited well to distill the essential mainstream of a country, except maybe for countries such as Finland that show a very specific music taste far away from the global taste [31]. In contrast, AF-ILF is capable of identifying those artists that are popular in a specific country, but not worldwide.…”

Section: Formalizing Mainstreaminessmentioning

confidence: 95%

Introducing Global and Regional Mainstreaminess for Improving Personalized Music Recommendation

Proceedings of the 15th International Conference on Advances in Mobile Computing &Amp; Multimedia - MoMM2017

Bauer

2017

Self Cite

The music mainstreaminess of a user reflects how strong a user's listening preferences correspond to those of the larger population. Considering that music mainstream may be defined from different perspectives and on various levels, e.g., geographical (charts of a country), genre ("Indie charts"), or distribution channel (radio charts vs. download charts), we study how the user's music mainstreaminess influences the quality of music recommendations. The paper's contribution is three-fold. First, we propose 11 novel mainstreaminess measures characterizing music listeners, considering both a global and a country-specific basis. To this end, we model preference profiles (as a vector over artists) for users, countries, and globally, incorporating artist frequency, listener frequency, and a newly proposed TF-IDF-inspired weighting function, which we call artist frequency-inverse listener frequency (AF-ILF). The resulting preference profile for each user u is then related to the respective country-specific and global preference profile using fractionbased approaches, symmetrized Kullback-Leibler divergence, and Kendall's τ rank correlation, in order to quantify u's mainstreaminess. Second, we demonstrate country-specific peculiarities of these mainstreaminess definitions. Third, we show that incorporating the proposed global and country-specific mainstreaminess measures into the music recommendation process can notably improve accuracy of rating prediction.

“…Skowron et al used the same dimensions to predict genre preferences of listeners with di erent cultural backgrounds [171]. Schedl analyzed a large corpus of listening histories created by Last.fm users in 47 countries and identi ed distinct preference pa erns [156]. Further analyses revealed countries closest to what can be considered the global mainstream (e.g., the Netherlands, UK, and Belgium) and countries farthest from it (e.g., China, Iran, and Slovakia).…”

Section: Culture-aware Music Recommendationmentioning

confidence: 99%

Current challenges and visions in music recommender systems research

Int J Multimed Info Retr

Zamani

Chen

et al. 2018

Self Cite

225

154

Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's ngertip. While today's MRS considerably help users to nd interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user-item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. e purpose of this trends and survey article is twofold. We rst identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the eld. e article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the eld.