Music Recommender Systems

Int J Multimed Info Retr

2017

Self Cite

Recently, the LFM-1b dataset has been proposed to foster research and evaluation in music retrieval and music recommender systems, Schedl (Proceedings of the ACM International Conference on Multimedia Retrieval (ICMR). New York, 2016). It contains more than one billion music listening events created by more than 120,000 users of Last.fm. Each listening event is characterized by artist, album, and track name, and further includes a timestamp. Basic demographic information and a selection of more elaborate listener-specific descriptors are included as well, for anonymized users. In this article, we reveal information about LFM-1b’s acquisition and content and we compare it to existing datasets. We furthermore provide an extensive statistical analysis of the dataset, including basic properties of the item sets, demographic coverage, distribution of listening events (e.g., over artists and users), and aspects related to music preference and consumption behavior (e.g., temporal features and mainstreaminess of listeners). Exploiting country information of users and genre tags of artists, we also create taste profiles for populations and determine similar and dissimilar countries in terms of their populations’ music preferences. Finally, we illustrate the dataset’s usage in a simple artist recommendation task, whose results are intended to serve as baseline against which more elaborate techniques can be assessed.

“…A more comprehensive discussion of datasets for music recommendation and related tasks can be found in [14]. …”

Section: Related Datasetsmentioning

confidence: 99%

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

Int J Multimed Info Retr

2017

Self Cite

“…Thereby, most MRS rely mainly on collaborative filtering [20] or on information about music items (i.e., content-based filtering [4]) [35]. For instance, content-based MRS may consider acoustic similarity information on the song level [38], or genre or artist similarity [21].…”

Section: Introductionmentioning

confidence: 99%

“…Various automatic approaches to music recommendation have been proposed [35]. Thereby, most MRS rely mainly on collaborative filtering [20] or on information about music items (i.e., content-based filtering [4]) [35].…”

Section: Introductionmentioning

confidence: 99%

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.

“…The user-item matching algorithms in recommender systems are typically based on one of the following approaches: content-based filtering (CBF), collaborative filtering (CF), or hybrid approaches that combine techniques from CBF and CF [65]. CBF approaches to MRS exploit item content descriptors, for instance, rhythm, tempo, instrumentation, lyrics, genre, or style of a music piece [15,[40][41][42][43], to build a user profile. Such descriptors are calculated or inferred from either the audio signal (using audio analysis techniques) [10,66,67], editorial metadata (e.g., genre or release year) [40,68], usergenerated content (e.g., tags or reviews) [69,70], or annotations gathered via web content mining [71,72].…”

Section: Music Recommendation Systemsmentioning

confidence: 99%

Global and country-specific mainstreaminess measures: Definitions, analysis, and usage for improving personalized music recommendation systems

Bauer

2019

PLoS ONE

Self Cite

Relevance Popularity-based approaches are widely adopted in music recommendation systems, both in industry and research. These approaches recommend to the target user what is currently popular among all users of the system. However, as the popularity distribution of music items typically is a long-tail distribution, popularity-based approaches to music recommendation fall short in satisfying listeners that have specialized music preferences far away from the global music mainstream. Addressing this gap, the contribution of this article is three-fold. Definition of mainstreaminess measures First, we provide several quantitative measures describing the proximity of a user’s music preference to the music mainstream. Assuming that there is a difference between the global music mainstream and a country-specific one, we define the measures at two levels: relating a listener’s music preferences to the global music preferences of all users, or relating them to music preferences of the user’s country. To quantify such music preferences, we define a music item’s popularity in terms of artist playcounts (APC) and artist listener counts (ALC). Moreover, we adopt a distribution-based and a rank-based approach as means to decrease bias towards the head of the long-tail distribution. This eventually results in a framework of 6 measures to quantify music mainstream. Differences between countries with respect to music mainstream Second, we perform in-depth quantitative and qualitative studies of music mainstream in that we (i) analyze differences between countries in terms of their level of mainstreaminess, (ii) uncover both positive and negative outliers (substantially higher and lower country-specific popularity, respectively, compared to the global mainstream), analyzing these with a mixed-methods approach, and (iii) investigate differences between countries in terms of listening preferences related to popular music artists. We conduct our studies and experiments using the standardized LFM-1b dataset, from which we analyze about 800,000,000 listening events shared by about 53,000 users (from 47 countries) of the music streaming platform Last.fm. We show that there are substantial country-specific differences in listeners’ music consumption behavior with respect to the most popular artists listened to. Rating prediction experiments Third, we demonstrate the applicability of our study results to improve music recommendation systems. To this end, we conduct rating prediction experiments in which we tailor recommendations to a user’s level of preference for the music mainstream using the proposed 6 mainstreaminess measures: defined by a distribution-based or rank-based approach, defined on a global level or on a country level (for the user’s country), and for APC or AL...