Music recommendations with temporal context awareness

Cebrián, Toni; Planaguma, Marc; Villegas, Paulo; Amatriain, Xavier

doi:10.1145/1864708.1864786

Cited by 25 publications

(14 citation statements)

References 3 publications

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“…A summary and categorization of corresponding scientific works can be found in Table 1. The majority of approaches rely solely on one or a few aspects (temporal features in Cebrián et al 2010, listening history and weather conditions in Lee and Lee 2007, for instance), whereas more comprehensive user models are rare in MIR. One of the few exceptions is Cunningham et al's study (Cunningham et al 2008) that investigates if and how various factors relate to music taste (e.g., human movement, emotional status, and external factors such as temperature and lightning conditions).…”

Section: What About the User In Mir?mentioning

confidence: 99%

“…For instance, Cebrián et al (2010), Pohle et al (2007), and Nürnberger and Detyniecki (2003) seemingly do not perform any kind of evaluation involving real users, or at least do not report it. Some approaches are evaluated on user-generated data, but do not request feedback from real users during the evaluation experiments.…”

Section: What About the User In Mir?mentioning

confidence: 99%

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The neglected user in music information retrieval research

2013

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Personalization and context-awareness are highly important topics in research on Intelligent Information Systems. In the fields of Music Information Retrieval (MIR) and Music Recommendation in particular, user-centric algorithms should ideally provide music that perfectly fits each individual listener in each imaginable situation and for each of her information or entertainment needs. Even though preliminary steps towards such systems have recently been presented at the "International Society for Music Information Retrieval Conference" (ISMIR) and at similar venues, this vision is still far away from becoming a reality. In this article, we investigate and discuss literature on the topic of user-centric music retrieval and reflect on why the breakthrough in this field has not been achieved yet. Given the different expertises of the authors, we shed light on why this topic is a particularly challenging one, taking computer science and psychology points of view. Whereas the computer science aspect centers on the problems of user modeling, machine learning,

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Section: What About the User In Mir?mentioning

confidence: 99%

Section: What About the User In Mir?mentioning

confidence: 99%

The neglected user in music information retrieval research

2013

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“…Some approaches consider only temporal features [5] or weather Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.…”

Section: Motivation and Related Workmentioning

confidence: 99%

Mobile Music Genius

Schedl

Breitschopf

Ionescu

2014

Proceedings of International Conference on Multimedia Retrieval

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The amount of music consumed while on the move has been spiraling during the past couple of years, which requests for intelligent music recommendation techniques. In this demo paper, we introduce a context-aware mobile music player named "Mobile Music Genius" (MMG), which seamlessly adapts the music playlist on the fly, according to the user context. It makes use of a comprehensive set of features derived from sensor data, spatiotemporal information, and user interaction to learn which kind of music a listeners prefers in which context. We describe the automatic creation and adaptation of playlists and present results of a study that investigates the capabilities of the gathered user context features to predict the listener's music preference.

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“…Music tastes vary heavily along with time, an aspect that has been studied in [22] with a special focus on the user situation. The notion of time is captured in our system via the time profile of each track over the year, which we encode as a feature vector for computing our time similarity (cf.…”

Section: Related Workmentioning

confidence: 99%

Music recommenders based on hybrid techniques and serendipity

Ziegler¹,

Hornung²,

Przyjaciel-Zablocki

et al. 2014

Web Intelligence and Agent Systems: An International Journal

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Taste in music is of highly subjective nature, making the recommending of music tracks a challenging research task. With TRECS, our live prototype system, we present a weighted hybrid recommender approach that amalgamates three diverse recommender techniques into one comprehensive score. Moreover, our prototype system peppers the generated result list by some simple serendipity heuristic. This way, users can benefit from recommendations aligned with their current taste in music while gaining some exploratory diversification. Empirical evaluations of the live TRECS system, based on an online evaluation, assess the overall recommendation quality as well as the impact of each of the three sub-recommenders. In addition, to better understand the nature and impact of serendipity in isolation, we conducted another study with another recommender prototype of ours, named SONG STUMBLER. The latter assesses three different serendipity metrics in an online evaluation.

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Music recommendations with temporal context awareness

Abstract: We present a system capable of recommending music playlists that take into account the temporal context of the user, i.e. they select user preferences as learned for the concrete time situation of the request.

Cited by 25 publications

References 3 publications

The neglected user in music information retrieval research

The neglected user in music information retrieval research

Mobile Music Genius

Music recommenders based on hybrid techniques and serendipity

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