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PEIA: Personality and Emotion Integrated Attentive model for music recommendation on social media platforms

PEIA: Personality and Emotion Integrated Attentive model for music recommendation on social media platforms
PEIA: Personality and Emotion Integrated Attentive model for music recommendation on social media platforms
With the rapid expansion of digital music formats, it’s indispensable to recommend users with their favorite music. For music recommendation, users’ personality and emotion greatly affect their music preference, respectively in a longterm and short-term manner, while rich social media data provides effective feedback on these information. In this paper, aiming at music recommendation on social media platforms, we propose a Personality and Emotion Integrated Attentive model (PEIA), which fully utilizes social media data to comprehensively model users’ long-term taste (personality) and short-term preference (emotion). Specifically, it takes full advantage of personality-oriented user features, emotionoriented user features and music features of multi-faceted attributes. Hierarchical attention is employed to distinguish the important factors when incorporating the latent representations of users’ personality and emotion. Extensive experiments on a large real-world dataset of 171,254 users demonstrate the effectiveness of our PEIA model which achieves an NDCG of 0.5369, outperforming the state-of-the-art methods. We also perform detailed parameter analysis and feature contribution analysis, which further verify our scheme and demonstrate the significance of co-modeling of user personality and emotion in music recommendation.
social media, music
Shen, Tiancheng
c5ab6f9e-4c08-46fc-ad78-886263986178
Jia, Jia
e546293b-0245-49bd-9376-bdc1fed52aad
Chua, Tat-Seng
4803e955-b3b1-4fc6-803d-192cbb1c127a
Hall, Wendy
11f7f8db-854c-4481-b1ae-721a51d8790c
Li, Yan
d4b90b76-cde3-4166-9b65-36130e424d83
Ma, Yihui
95804cfb-e264-4b80-a860-6144b9c21441
Bu, Yaohua
4f1cb447-7ebc-480d-a121-b436b032d860
Wang, Hanjie
3232e04a-50a7-4371-9b54-ed9175b17d96
Chen, Bo
bd407ea9-4263-47dd-97ec-a7059f8f1d09
Shen, Tiancheng
c5ab6f9e-4c08-46fc-ad78-886263986178
Jia, Jia
e546293b-0245-49bd-9376-bdc1fed52aad
Chua, Tat-Seng
4803e955-b3b1-4fc6-803d-192cbb1c127a
Hall, Wendy
11f7f8db-854c-4481-b1ae-721a51d8790c
Li, Yan
d4b90b76-cde3-4166-9b65-36130e424d83
Ma, Yihui
95804cfb-e264-4b80-a860-6144b9c21441
Bu, Yaohua
4f1cb447-7ebc-480d-a121-b436b032d860
Wang, Hanjie
3232e04a-50a7-4371-9b54-ed9175b17d96
Chen, Bo
bd407ea9-4263-47dd-97ec-a7059f8f1d09

Shen, Tiancheng, Jia, Jia, Chua, Tat-Seng, Hall, Wendy, Li, Yan, Ma, Yihui, Bu, Yaohua, Wang, Hanjie and Chen, Bo (2020) PEIA: Personality and Emotion Integrated Attentive model for music recommendation on social media platforms. 34th AAAI Conference on Artificial Intelligence (AAAI'20), Hilton New York, United States. 07 - 12 Feb 2020. 8 pp . (Submitted)

Record type: Conference or Workshop Item (Paper)

Abstract

With the rapid expansion of digital music formats, it’s indispensable to recommend users with their favorite music. For music recommendation, users’ personality and emotion greatly affect their music preference, respectively in a longterm and short-term manner, while rich social media data provides effective feedback on these information. In this paper, aiming at music recommendation on social media platforms, we propose a Personality and Emotion Integrated Attentive model (PEIA), which fully utilizes social media data to comprehensively model users’ long-term taste (personality) and short-term preference (emotion). Specifically, it takes full advantage of personality-oriented user features, emotionoriented user features and music features of multi-faceted attributes. Hierarchical attention is employed to distinguish the important factors when incorporating the latent representations of users’ personality and emotion. Extensive experiments on a large real-world dataset of 171,254 users demonstrate the effectiveness of our PEIA model which achieves an NDCG of 0.5369, outperforming the state-of-the-art methods. We also perform detailed parameter analysis and feature contribution analysis, which further verify our scheme and demonstrate the significance of co-modeling of user personality and emotion in music recommendation.

Full text not available from this repository.

More information

Submitted date: 2020
Venue - Dates: 34th AAAI Conference on Artificial Intelligence (AAAI'20), Hilton New York, United States, 2020-02-07 - 2020-02-12
Keywords: social media, music

Identifiers

Local EPrints ID: 438221
URI: http://eprints.soton.ac.uk/id/eprint/438221
PURE UUID: b5d0f9bb-7327-4679-9f1f-680758f6463e
ORCID for Wendy Hall: ORCID iD orcid.org/0000-0003-4327-7811

Catalogue record

Date deposited: 04 Mar 2020 17:31
Last modified: 16 Jul 2020 00:23

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