On effective location-aware music recommendation

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 gener...

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Main Authors: CHENG, Zhiyong, SHEN, Jialie
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2016
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Online Access:https://ink.library.smu.edu.sg/sis_research/3178
https://ink.library.smu.edu.sg/context/sis_research/article/4179/viewcontent/EffectiveLocation_AwareMusic_Recommendation_2016.pdf
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spelling sg-smu-ink.sis_research-41792020-04-01T07:49:08Z On effective location-aware music recommendation CHENG, Zhiyong SHEN, Jialie 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. 2016-04-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3178 info:doi/10.1145/2846092 https://ink.library.smu.edu.sg/context/sis_research/article/4179/viewcontent/EffectiveLocation_AwareMusic_Recommendation_2016.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Algorithms Design Experimentation Human Factors Venue-aware music recommendation music concept topic model Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Algorithms
Design
Experimentation
Human Factors
Venue-aware
music recommendation
music concept
topic model
Databases and Information Systems
spellingShingle Algorithms
Design
Experimentation
Human Factors
Venue-aware
music recommendation
music concept
topic model
Databases and Information Systems
CHENG, Zhiyong
SHEN, Jialie
On effective location-aware music recommendation
description 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.
format text
author CHENG, Zhiyong
SHEN, Jialie
author_facet CHENG, Zhiyong
SHEN, Jialie
author_sort CHENG, Zhiyong
title On effective location-aware music recommendation
title_short On effective location-aware music recommendation
title_full On effective location-aware music recommendation
title_fullStr On effective location-aware music recommendation
title_full_unstemmed On effective location-aware music recommendation
title_sort on effective location-aware music recommendation
publisher Institutional Knowledge at Singapore Management University
publishDate 2016
url https://ink.library.smu.edu.sg/sis_research/3178
https://ink.library.smu.edu.sg/context/sis_research/article/4179/viewcontent/EffectiveLocation_AwareMusic_Recommendation_2016.pdf
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