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Recommending music for places of interest in a mobile travel guide

Authors:

Matthias Braunhofer,

Marius Kaminskas,

Francesco RicciAuthors Info & Claims

RecSys '11: Proceedings of the fifth ACM conference on Recommender systems

Pages 253 - 256

https://doi.org/10.1145/2043932.2043977

Published: 23 October 2011 Publication History

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Abstract

Context-aware music recommender systems suggest music items taking into consideration contextual conditions, such as the user mood or location, that may influence the user preferences at a particular moment. In this paper we consider a particular kind of context-aware recommendation task: selecting music suited for a place of interest (POI), which the user is visiting, and that is illustrated in a mobile travel guide. We have designed an approach for this novel recommendation task by matching music to POIs using emotional tags. In order to test our approach, we have developed a mobile application that suggests an itinerary and plays recommended music for each visited POI. The results of the study show that users judge the recommended music suited for the POIs, and the music is rated higher when it is played in this usage scenario.

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Kaviani F(2023)Recommender System in Social Networks using Fuzzy Logic2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)10.1109/ICECCME57830.2023.10253120(1-7)Online publication date: 19-Jul-2023
https://doi.org/10.1109/ICECCME57830.2023.10253120
Yohe KMoradizeyveh SBeheshti A(2023)Towards Socio-Economic and Culturally-Aware Recommender Systems2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)10.1109/CSDE59766.2023.10487736(1-8)Online publication date: 4-Dec-2023
https://doi.org/10.1109/CSDE59766.2023.10487736
Lozano Murciego ÁJiménez-Bravo DValera Román ADe Paz Santana JMoreno-García M(2021)Context-Aware Recommender Systems in the Music Domain: A Systematic Literature ReviewElectronics10.3390/electronics1013155510:13(1555)Online publication date: 27-Jun-2021
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Index Terms

Recommending music for places of interest in a mobile travel guide
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Information

Published In

RecSys '11: Proceedings of the fifth ACM conference on Recommender systems

October 2011

414 pages

ISBN:9781450306836

DOI:10.1145/2043932

General Chairs:
Bamshad Mobasher
DePaul University, USA
,
Robin Burke
DePaul University, USA
,
Program Chairs:
Dietmar Jannach
TU Dortmund, Germany
,
Gediminas Adomavicius
University of Minnesota, USA

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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Publication History

Published: 23 October 2011

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RecSys '11

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SIGCHI

RecSys '11: Fifth ACM Conference on Recommender Systems

October 23 - 27, 2011

Illinois, Chicago, USA

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

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Cited By

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https://doi.org/10.1109/ICECCME57830.2023.10253120
Yohe KMoradizeyveh SBeheshti A(2023)Towards Socio-Economic and Culturally-Aware Recommender Systems2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)10.1109/CSDE59766.2023.10487736(1-8)Online publication date: 4-Dec-2023
https://doi.org/10.1109/CSDE59766.2023.10487736
Lozano Murciego ÁJiménez-Bravo DValera Román ADe Paz Santana JMoreno-García M(2021)Context-Aware Recommender Systems in the Music Domain: A Systematic Literature ReviewElectronics10.3390/electronics1013155510:13(1555)Online publication date: 27-Jun-2021
https://doi.org/10.3390/electronics10131555
Adomavicius GBauman KTuzhilin AUnger M(2021)Context-Aware Recommender Systems: From Foundations to Recent DevelopmentsRecommender Systems Handbook10.1007/978-1-0716-2197-4_6(211-250)Online publication date: 22-Nov-2021
https://doi.org/10.1007/978-1-0716-2197-4_6
Shen JRafferty KJia J(2020)Online Intelligent Music Recommendation: The Opportunity and Challenge for People Well-Being Improvement2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)10.1109/CogMI50398.2020.00014(27-31)Online publication date: Oct-2020
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Bauer CSchedl M(2019)Global and country-specific mainstreaminess measures: Definitions, analysis, and usage for improving personalized music recommendation systemsPLOS ONE10.1371/journal.pone.021738914:6(e0217389)Online publication date: 7-Jun-2019
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Missaoui SKassem FViviani MAgostini AFaiz RPasi G(2019)LOOKERPersonal and Ubiquitous Computing10.1007/s00779-018-01194-w23:2(181-197)Online publication date: 1-Apr-2019
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Lin QNiu YZhu YLu HMushonga KNiu Z(2018)Heterogeneous Knowledge-Based Attentive Neural Networks for Short-Term Music RecommendationsIEEE Access10.1109/ACCESS.2018.28749596(58990-59000)Online publication date: 2018
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Pichl MZangerle ESpecht GIonescu BSebe NFeng JLarson MLienhart RSnoek C(2017)Improving Context-Aware Music Recommender SystemsProceedings of the 2017 ACM on International Conference on Multimedia Retrieval10.1145/3078971.3078980(201-208)Online publication date: 6-Jun-2017
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Pichl MZangerle ESpecht GSchedl M(2017)Mining Culture-Specific Music Listening Behavior from Social Media Data2017 IEEE International Symposium on Multimedia (ISM)10.1109/ISM.2017.35(208-215)Online publication date: Dec-2017
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