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short-paper

Attentive neural architecture incorporating song features for music recommendation

Authors:

Noveen Sachdeva,

Vikram PudiAuthors Info & Claims

RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems

Pages 417 - 421

https://doi.org/10.1145/3240323.3240397

Published: 27 September 2018 Publication History

Abstract

Recommender Systems are an integral part of music sharing platforms. Often the aim of these systems is to increase the time, the user spends on the platform and hence having a high commercial value. The systems which aim at increasing the average time a user spends on the platform often need to recommend songs which the user might want to listen to next at each point in time. This is different from recommendation systems which try to predict the item which might be of interest to the user at some point in the user lifetime but not necessarily in the very near future. Prediction of next song the user might like requires some kind of modeling of the user interests at the given point of time. Attentive neural networks have been exploiting the sequence in which the items were selected by the user to model the implicit short-term interests of the user for the task of next item prediction, however we feel that features of the songs occurring in the sequence could also convey some important information about the short-term user interest which only the items cannot. In this direction we propose a novel attentive neural architecture which in addition to the sequence of items selected by the user, uses the features of these items to better learn the user short-term preferences and recommend next song to the user.

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Cited By

Kostrzewa DSzwajnoch PBrzeski RMrozek D(2024)Detecting Selected Instruments in the Sound SignalApplied Sciences10.3390/app1414633014:14(6330)Online publication date: 20-Jul-2024
https://doi.org/10.3390/app14146330
Shashaani S(2024)Explainability in Music Recommender SystemProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688028(1395-1401)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688028
Awotunde JKazeem Abiodun MAdeniyi AHameed Abiodun BAdeniyi JRueben Aremu DAdebiyi AAtanda O(2024)Personalized Music Recommendation System Based on Machine Learning and Collaborative Filtering2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG)10.1109/SEB4SDG60871.2024.10630080(1-8)Online publication date: 2-Apr-2024
https://doi.org/10.1109/SEB4SDG60871.2024.10630080
Show More Cited By

Index Terms

Attentive neural architecture incorporating song features for music recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. World Wide Web
    1. Web searching and information discovery
      1. Content ranking

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Information & Contributors

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Published In

cover image ACM Conferences

RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems

September 2018

600 pages

ISBN:9781450359016

DOI:10.1145/3240323

General Chairs:
Sole Pera
Boise State University
,
Michael Ekstrand
Boise State University
,
Program Chairs:
Xavier Amatriain
Curai, USA
,
John O'Donovan
University of California, Santa Barbara

Copyright © 2018 ACM.

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. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 September 2018

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RecSys '18

Sponsor:

SIGCHI

RecSys '18: Twelfth ACM Conference on Recommender Systems

October 2, 2018

British Columbia, Vancouver, Canada

Acceptance Rates

RecSys '18 Paper Acceptance Rate 32 of 181 submissions, 18%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Kostrzewa DSzwajnoch PBrzeski RMrozek D(2024)Detecting Selected Instruments in the Sound SignalApplied Sciences10.3390/app1414633014:14(6330)Online publication date: 20-Jul-2024
https://doi.org/10.3390/app14146330
Shashaani S(2024)Explainability in Music Recommender SystemProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688028(1395-1401)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688028
Awotunde JKazeem Abiodun MAdeniyi AHameed Abiodun BAdeniyi JRueben Aremu DAdebiyi AAtanda O(2024)Personalized Music Recommendation System Based on Machine Learning and Collaborative Filtering2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG)10.1109/SEB4SDG60871.2024.10630080(1-8)Online publication date: 2-Apr-2024
https://doi.org/10.1109/SEB4SDG60871.2024.10630080
Ranjan SPandey TBhusan Dash BMishra MChandra De UPatra S(2024)Performance Assessment of Various Machine Learning Algorithms in Recommendation2024 Second International Conference on Inventive Computing and Informatics (ICICI)10.1109/ICICI62254.2024.00055(292-297)Online publication date: 11-Jun-2024
https://doi.org/10.1109/ICICI62254.2024.00055
Kaushik PSingh Rathore SChahal KSaraf SSingh Chauhan GKumar P(2024)RhythmQuest: Unifying Indian Music Classification and Prediction with Hybrid Deep Learning Techniques2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)10.1109/IATMSI60426.2024.10503056(1-6)Online publication date: 14-Mar-2024
https://doi.org/10.1109/IATMSI60426.2024.10503056
Amiri BShahverdi NHaddadi AGhahremani Y(2024)Beyond the Trends: Evolution and Future Directions in Music Recommender Systems ResearchIEEE Access10.1109/ACCESS.2024.338668412(51500-51522)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3386684
Kostrzewa DBerezka PBrzeski R(2024)Music Genre Classification Using Hybrid Committees and Voting MechanismsAdvances in Computational Collective Intelligence10.1007/978-3-031-70248-8_2(16-28)Online publication date: 8-Sep-2024
https://doi.org/10.1007/978-3-031-70248-8_2
Lv SPan L(2023)BTSAMAInternational Journal of Ambient Computing and Intelligence10.4018/IJACI.32735114:1(1-23)Online publication date: 31-Jul-2023
https://dl.acm.org/doi/10.4018/IJACI.327351
Jin XZhou WWang JXU DZheng YEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)An Order-Complexity Aesthetic Assessment Model for Aesthetic-aware Music RecommendationProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612140(6938-6947)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3612140
Li ZLi KYang Q(2023)The Algorithm of Sequential Music Recommendation Based on BERT2023 International Conference on Culture-Oriented Science and Technology (CoST)10.1109/CoST60524.2023.00054(231-235)Online publication date: 11-Oct-2023
https://doi.org/10.1109/CoST60524.2023.00054
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