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User geospatial context for music recommendation in microblogs

User geospatial context for music recommendation in microblogs
User geospatial context for music recommendation in microblogs
Music information retrieval and music recommendation are seeing a paradigm shift towards methods that incorporate user context aspects. However, structured experiments on a standardized music dataset to investigate the effects of doing so are scarce. In this paper, we compare performance of various combinations of collaborative filtering and geospatial as well as cultural user models for the task of music recommendation. To this end, we propose a geospatial model that uses GPS coordinates and a cultural model that uses semantic locations (continent, country, and state of the user). We conduct experiments on a novel standardized music collection, the "Million Musical Tweets Dataset" of listening events extracted from microblogs. Overall, we find that modeling listeners' location via Gaussian mixture models and computing similarities from these outperforms both cultural user models and collaborative filtering.
ACM
Schedl, Markus
e98fac9e-e446-4b27-9cac-815480749500
Vall, Andreu
68a9a2b4-9d6d-4c99-a85d-7846bca0431e
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Schedl, Markus
e98fac9e-e446-4b27-9cac-815480749500
Vall, Andreu
68a9a2b4-9d6d-4c99-a85d-7846bca0431e
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb

Schedl, Markus, Vall, Andreu and Farrahi, Katayoun (2014) User geospatial context for music recommendation in microblogs. In SIGIR '14 Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval: ACM Special Interest Group On Information Retrieval. ACM. 4 pp . (doi:10.1145/2600428.2609491).

Record type: Conference or Workshop Item (Paper)

Abstract

Music information retrieval and music recommendation are seeing a paradigm shift towards methods that incorporate user context aspects. However, structured experiments on a standardized music dataset to investigate the effects of doing so are scarce. In this paper, we compare performance of various combinations of collaborative filtering and geospatial as well as cultural user models for the task of music recommendation. To this end, we propose a geospatial model that uses GPS coordinates and a cultural model that uses semantic locations (continent, country, and state of the user). We conduct experiments on a novel standardized music collection, the "Million Musical Tweets Dataset" of listening events extracted from microblogs. Overall, we find that modeling listeners' location via Gaussian mixture models and computing similarities from these outperforms both cultural user models and collaborative filtering.

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Published date: July 2014

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Local EPrints ID: 420663
URI: https://eprints.soton.ac.uk/id/eprint/420663
PURE UUID: e1df96e4-ae21-47a7-b5b7-6ffab75209a8

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Date deposited: 11 May 2018 16:30
Last modified: 18 Feb 2019 17:30

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Contributors

Author: Markus Schedl
Author: Andreu Vall
Author: Katayoun Farrahi

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