Publication Type

Journal Article

Version

publishedVersion

Publication Date

4-2016

Abstract

Rapid advances in mobile devices and cloud-based music service now allow consumers to enjoy music any-time and anywhere. Consequently, there has been an increasing demand in studying intelligent techniques to facilitate context-aware music recommendation. However, one important context that is generally overlooked is user's venue, which often includes surrounding atmosphere, correlates with activities, and greatly influences the user's music preferences. In this article, we present a novel venue-aware music recommender system called VenueMusic to effectively identify suitable songs for various types of popular venues in our daily lives. Toward this goal, a Location-aware Topic Model (LTM) is proposed to (i) mine the common features of songs that are suitable for a venue type in a latent semantic space and (ii) represent songs and venue types in the shared latent space, in which songs and venue types can be directly matched. It is worth mentioning that to discover meaningful latent topics with the LTM, a Music Concept Sequence Generation (MCSG) scheme is designed to extract effective semantic representations for songs. An extensive experimental study based on two large music test collections demonstrates the effectiveness of the proposed topic model and MCSG scheme. The comparisons with state-of-the-art music recommender systems demonstrate the superior performance of VenueMusic system on recommendation accuracy by associating venue and music contents using a latent semantic space. This work is a pioneering study on the development of a venue-aware music recommender system. The results show the importance of considering the influence of venue types in the development of context-aware music recommender systems.

Keywords

Algorithms, Design, Experimentation, Human Factors, Venue-aware, music recommendation, music concept, topic model

Discipline

Databases and Information Systems

Publication

ACM Transactions on Information Systems

Volume

34

Issue

2

First Page

1

Last Page

32

ISSN

1046-8188

Identifier

10.1145/2846092

Publisher

ACM

Copyright Owner and License

Authors

Additional URL

http://doi.org/10.1145/2846092

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