poster

Enriching user profiling with affective features for the improvement of a multimodal recommender system

Authors:

Ioannis Arapakis,

Yashar Moshfeghi,

Joemon M. JoseAuthors Info & Claims

CIVR '09: Proceedings of the ACM International Conference on Image and Video Retrieval

Article No.: 29, Pages 1 - 8

https://doi.org/10.1145/1646396.1646433

Published: 08 July 2009 Publication History

Abstract

Recommender systems have been systematically applied in industry and academia to help users cope with information uncertainty. However, given the multiplicity of the preferences and needs it has been shown that no approach is suitable for all users in all situations. Thus, it is believed that an effective recommender system should incorporate a variety of techniques and features to offer valuable recommendations and enhance the search experience. In this paper we propose a novel video search interface that employs a multimodal recommender system, which can predict topical relevance. The multimodal recommender accounts for interaction data, contextual information, as well as users' affective responses, and exploits these information channels to provide meaningful recommendations of unseen videos. Our experiment shows that the multimodal interaction feature is a promising way to improve the performance of recommendation.

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Cited By

Davis KSpape MRuotsalo T(2023)Contradicted by the Brain: Predicting Individual and Group Preferences via Brain-Computer InterfacingIEEE Transactions on Affective Computing10.1109/TAFFC.2022.322588514:4(3094-3105)Online publication date: 1-Oct-2023
https://doi.org/10.1109/TAFFC.2022.3225885
Wani ZBhat A(2022)Exploring the Augmented Intelligence and Augmented RealityHandbook of Research on Technological Advances of Library and Information Science in Industry 5.010.4018/978-1-6684-4755-0.ch007(125-141)Online publication date: 14-Oct-2022
https://doi.org/10.4018/978-1-6684-4755-0.ch007
Wang DZhao X(2022)Affective video recommender systems: A surveyFrontiers in Neuroscience10.3389/fnins.2022.98440416Online publication date: 26-Aug-2022
https://doi.org/10.3389/fnins.2022.984404
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Index Terms

Enriching user profiling with affective features for the improvement of a multimodal recommender system

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Published In

cover image ACM Conferences

CIVR '09: Proceedings of the ACM International Conference on Image and Video Retrieval

July 2009

383 pages

ISBN:9781605584805

DOI:10.1145/1646396

Conference Chairs:
Yiannis Kompatsiaris
CERTH-ITI, Greece
,
Stephane Marchand-Maillet
Univ. of Geneva, Switzerland
,
Program Chairs:
Yannis Avrithis
NTUA, Greece
,
Noel O Connor
DCU, Ireland
,
Daniel Gatica-Perez
Idiap Research Institute, Switzerland
,
Tat-Seng Chua
National University of Singapore, Singapore

Copyright © 2009 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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SIGMM: ACM Special Interest Group on Multimedia

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 July 2009

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Sixth Framework Programme

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CIVR '09

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SIGMM

CIVR '09: CIVR '09 - International Conference on Image and Video Retrieval

July 8 - 10, 2009

Santorini, Fira, Greece

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Cited By

Davis KSpape MRuotsalo T(2023)Contradicted by the Brain: Predicting Individual and Group Preferences via Brain-Computer InterfacingIEEE Transactions on Affective Computing10.1109/TAFFC.2022.322588514:4(3094-3105)Online publication date: 1-Oct-2023
https://doi.org/10.1109/TAFFC.2022.3225885
Wani ZBhat A(2022)Exploring the Augmented Intelligence and Augmented RealityHandbook of Research on Technological Advances of Library and Information Science in Industry 5.010.4018/978-1-6684-4755-0.ch007(125-141)Online publication date: 14-Oct-2022
https://doi.org/10.4018/978-1-6684-4755-0.ch007
Wang DZhao X(2022)Affective video recommender systems: A surveyFrontiers in Neuroscience10.3389/fnins.2022.98440416Online publication date: 26-Aug-2022
https://doi.org/10.3389/fnins.2022.984404
Li XZhang YTiwari PSong DHu BYang MZhao ZKumar NMarttinen P(2022)EEG Based Emotion Recognition: A Tutorial and ReviewACM Computing Surveys10.1145/352449955:4(1-57)Online publication date: 21-Nov-2022
https://dl.acm.org/doi/10.1145/3524499
Graef RKlier MObermeier AZolitschka J(2022)What to buy, Pepper? – Bridging the Physical and the Digital World with Recommendations from Humanoid RobotsJournal of Decision Systems10.1080/12460125.2022.202904932:2(439-465)Online publication date: 23-Jan-2022
https://doi.org/10.1080/12460125.2022.2029049
Shaw RPatra BPradhan AMishra S(2022)Attention Classification and Lecture Video Recommendation Based on Captured EEG Signal in Flipped Learning PedagogyInternational Journal of Human–Computer Interaction10.1080/10447318.2022.209156139:15(3057-3070)Online publication date: 5-Aug-2022
https://doi.org/10.1080/10447318.2022.2091561
Kiesel JMeyer LPotthast MStein B(2021)Meta-Information in Conversational SearchACM Transactions on Information Systems10.1145/346886839:4(1-44)Online publication date: 16-Aug-2021
https://dl.acm.org/doi/10.1145/3468868
Davis III KSpapé MRuotsalo T(2021)Collaborative Filtering with Preferences Inferred from Brain SignalsProceedings of the Web Conference 202110.1145/3442381.3450031(602-611)Online publication date: 19-Apr-2021
https://dl.acm.org/doi/10.1145/3442381.3450031
De Pessemier TCoppens IMartens L(2020)Evaluating facial recognition services as interaction technique for recommender systemsMultimedia Tools and Applications10.1007/s11042-020-09061-8Online publication date: 10-Jun-2020
https://doi.org/10.1007/s11042-020-09061-8
Moshfeghi YTriantafillou PPollick F(2019)Towards Predicting a Realisation of an Information Need based on Brain SignalsThe World Wide Web Conference10.1145/3308558.3313671(1300-1309)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313671
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