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Personalized next-song recommendation in online karaokes

Authors:

Guoping HuAuthors Info & Claims

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

Pages 137 - 140

https://doi.org/10.1145/2507157.2507215

Published: 12 October 2013 Publication History

Abstract

In this paper, we propose Personalized Markov Embedding (PME), a next-song recommendation strategy for online karaoke users. By modeling the sequential singing behavior, we first embed songs and users into a Euclidean space in which distances between songs and users reflect the strength of their relationships. Then, given each user's last song, we can generate personalized recommendations by ranking the candidate songs according to the embedding. Moreover, PME can be trained without any requirement of content information. Finally, we perform an experimental evaluation on a real world data set provided by ihou.com which is an online karaoke website launched by iFLYTEK, and the results clearly demonstrate the effectiveness of PME.

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Cited By

Toma SAriga THiguchil YHayasaka IShigyo ROgawa T(2024)Exploring Impact of Prioritizing Intra-Singer Acoustic Variations on Singer Embedding Extractor Construction for Singer Verification2024 27th Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardisation of Speech Databases and Assessment Techniques (O-COCOSDA)10.1109/O-COCOSDA64382.2024.10800601(1-6)Online publication date: 17-Oct-2024
https://doi.org/10.1109/O-COCOSDA64382.2024.10800601
Wu HGeng CFang H(2023)Causality and Correlation Graph Modeling for Effective and Explainable Session-Based RecommendationACM Transactions on the Web10.1145/359331318:1(1-25)Online publication date: 11-Oct-2023
https://dl.acm.org/doi/10.1145/3593313
Jia BCao JQian SZhu NDong XZhang LCheng LMo L(2023)SMONE: A Session-based Recommendation Model Based on Neighbor Sessions with Similar Probabilistic IntentionsACM Transactions on Knowledge Discovery from Data10.1145/358709917:8(1-22)Online publication date: 12-May-2023
https://dl.acm.org/doi/10.1145/3587099
Show More Cited By

Index Terms

Personalized next-song recommendation in online karaokes
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Information & Contributors

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Published In

cover image ACM Conferences

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

October 2013

516 pages

ISBN:9781450324090

DOI:10.1145/2507157

General Chairs:
Qiang Yang
Huawei Noah Ark Research Lab, China
,
Irwin King
Chinese University of Hong Kong, China
,
Qing Li
City University of Hong Kong, China
,
Program Chairs:
Pearl Pu
Ecole Polytechnique Fédérale de Lausanne, Switzerland
,
George Karypis
University of Minnesota, USA

Copyright © 2013 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: 12 October 2013

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RecSys '13

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SIGCHI

RecSys '13: Seventh ACM Conference on Recommender Systems

October 12 - 16, 2013

Hong Kong, China

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RecSys '13 Paper Acceptance Rate 32 of 136 submissions, 24%;

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

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Cited By

Toma SAriga THiguchil YHayasaka IShigyo ROgawa T(2024)Exploring Impact of Prioritizing Intra-Singer Acoustic Variations on Singer Embedding Extractor Construction for Singer Verification2024 27th Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardisation of Speech Databases and Assessment Techniques (O-COCOSDA)10.1109/O-COCOSDA64382.2024.10800601(1-6)Online publication date: 17-Oct-2024
https://doi.org/10.1109/O-COCOSDA64382.2024.10800601
Wu HGeng CFang H(2023)Causality and Correlation Graph Modeling for Effective and Explainable Session-Based RecommendationACM Transactions on the Web10.1145/359331318:1(1-25)Online publication date: 11-Oct-2023
https://dl.acm.org/doi/10.1145/3593313
Jia BCao JQian SZhu NDong XZhang LCheng LMo L(2023)SMONE: A Session-based Recommendation Model Based on Neighbor Sessions with Similar Probabilistic IntentionsACM Transactions on Knowledge Discovery from Data10.1145/358709917:8(1-22)Online publication date: 12-May-2023
https://dl.acm.org/doi/10.1145/3587099
Wei WZhao SZou D(2023)Recommendation System: A Survey and New PerspectivesWorld Scientific Annual Review of Artificial Intelligence10.1142/S281103232330001301Online publication date: 4-May-2023
https://doi.org/10.1142/S2811032323300013
Tsao CYeh CJang JChen YWang C(2023)Multi-behavior Recommendation with Action Pattern-aware Networks2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT59888.2023.00009(16-23)Online publication date: 26-Oct-2023
https://doi.org/10.1109/WI-IAT59888.2023.00009
Dong LZhu GWang YLi YDuan JSun M(2023)A Graph Positional Attention Network for Session-Based RecommendationIEEE Access10.1109/ACCESS.2023.323535311(7564-7573)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3235353
Ding CZhao ZLi CYu YZeng Q(2023)Session-based recommendation with hypergraph convolutional networks and sequential information embeddingsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.119875223:COnline publication date: 10-May-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.119875
Wu HGeng CFang H(2023)Session-based recommendation by exploiting substitutable and complementary relationships from multi-behavior dataData Mining and Knowledge Discovery10.1007/s10618-023-00994-w38:3(1193-1221)Online publication date: 26-Dec-2023
https://doi.org/10.1007/s10618-023-00994-w
Liu QTian FZheng QWang Q(2023)Disentangling interest and conformity for eliminating popularity bias in session-based recommendationKnowledge and Information Systems10.1007/s10115-023-01839-065:6(2645-2664)Online publication date: 8-Mar-2023
https://doi.org/10.1007/s10115-023-01839-0
Ma MRen PChen ZRen ZZhao LLiu PMa Jde Rijke M(2022)Mixed Information Flow for Cross-Domain Sequential RecommendationsACM Transactions on Knowledge Discovery from Data10.1145/348733116:4(1-32)Online publication date: 8-Jan-2022
https://dl.acm.org/doi/10.1145/3487331
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