short-paper

A General Architecture for an Emotion-aware Content-based Recommender System

Authors:

Fedelucio Narducci,

Marco de Gemmis,

Pasquale LopsAuthors Info & Claims

EMPIRE '15: Proceedings of the 3rd Workshop on Emotions and Personality in Personalized Systems 2015

Pages 3 - 6

https://doi.org/10.1145/2809643.2809648

Published: 16 September 2015 Publication History

Abstract

Emotions play a crucial role in the decision making process. Frequently, choices are strongly influenced by the mood of the moment, and the same person could take different decisions at different time on the same topic. Recommender systems, that are definitively recognized as tools for supporting the decision making process, demonstrated to be more accurate exploiting emotive labels in several work. For this reason a large number of researchers are focusing their attention on the analysis of the emotions by exploiting data that users daily disseminate on the Web (e.g.: Social Networks, Blogs, Forums, etc.). In this paper we propose a general architecture for implementing an emotion-aware content-based recommender system. Furthermore, we developed a web service that researchers can freely exploit for their own implementations. We carried out a user study on the domain of music recommendation, particularly influenced by the user emotion, and results are very promising.

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Cited By

Oyebode OOgubuike RDaniel DOrji R(2024)Emotion Detection from Real-Life Situations Based on Journal Entries Using Machine Learning and Deep Learning TechniquesIntelligent Systems and Applications10.1007/978-3-031-47724-9_32(477-502)Online publication date: 19-Apr-2024
https://doi.org/10.1007/978-3-031-47724-9_32
Milton APera M(2023)Into the Unknown: Exploration of Search Engines’ Responses to Users with Depression and AnxietyACM Transactions on the Web10.1145/358028317:4(1-29)Online publication date: 11-Jul-2023
https://dl.acm.org/doi/10.1145/3580283
Luganga A(2023)Research Proposal: Using Computational Design to Enhance Emotion-Driven Recommendations in Multimedia Experiences2023 11th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)10.1109/ACIIW59127.2023.10388193(1-5)Online publication date: 10-Sep-2023
https://doi.org/10.1109/ACIIW59127.2023.10388193
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Index Terms

A General Architecture for an Emotion-aware Content-based Recommender System

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cover image ACM Other conferences

EMPIRE '15: Proceedings of the 3rd Workshop on Emotions and Personality in Personalized Systems 2015

September 2015

45 pages

ISBN:9781450336154

DOI:10.1145/2809643

Copyright © 2015 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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University of Ljubljana: University of Ljubljana
Johannes Kepler University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 September 2015

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EMPIRE '15

EMPIRE '15: 3rd Workshop on Emotions and Personality in Personalized Systems 2015

September 16 - 20, 2015

Vienna, Austria

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EMPIRE '15 Paper Acceptance Rate 6 of 9 submissions, 67%;

Overall Acceptance Rate 6 of 9 submissions, 67%

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Cited By

Oyebode OOgubuike RDaniel DOrji R(2024)Emotion Detection from Real-Life Situations Based on Journal Entries Using Machine Learning and Deep Learning TechniquesIntelligent Systems and Applications10.1007/978-3-031-47724-9_32(477-502)Online publication date: 19-Apr-2024
https://doi.org/10.1007/978-3-031-47724-9_32
Milton APera M(2023)Into the Unknown: Exploration of Search Engines’ Responses to Users with Depression and AnxietyACM Transactions on the Web10.1145/358028317:4(1-29)Online publication date: 11-Jul-2023
https://dl.acm.org/doi/10.1145/3580283
Luganga A(2023)Research Proposal: Using Computational Design to Enhance Emotion-Driven Recommendations in Multimedia Experiences2023 11th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)10.1109/ACIIW59127.2023.10388193(1-5)Online publication date: 10-Sep-2023
https://doi.org/10.1109/ACIIW59127.2023.10388193
Salazar JAguilar JMonsalve-Pulido JMontoya E(2023)A generic architecture of an affective recommender system for e-learning environmentsUniversal Access in the Information Society10.1007/s10209-023-01024-8Online publication date: 17-Aug-2023
https://doi.org/10.1007/s10209-023-01024-8
Assuncao WPiccolo LZaina L(2022)Considering emotions and contextual factors in music recommendation: a systematic literature reviewMultimedia Tools and Applications10.1007/s11042-022-12110-z81:6(8367-8407)Online publication date: 2-Feb-2022
https://dl.acm.org/doi/10.1007/s11042-022-12110-z
Chandra AAhmed AKumar SChand PBorah MHussain Z(2022)Content-Based Recommender System for Similar Products in E-CommerceEdge Analytics10.1007/978-981-19-0019-8_46(617-628)Online publication date: 4-Apr-2022
https://doi.org/10.1007/978-981-19-0019-8_46
Santamaria-Granados LMendoza-Moreno JRamirez-Gonzalez G(2020)Tourist Recommender Systems Based on Emotion Recognition—A Scientometric ReviewFuture Internet10.3390/fi1301000213:1(2)Online publication date: 24-Dec-2020
https://doi.org/10.3390/fi13010002
Li W(2018)A Rating Prediction Method for Combining Social Network and Context InformationProceedings of the 2018 International Conference on Computing and Data Engineering10.1145/3219788.3219789(53-56)Online publication date: 4-May-2018
https://dl.acm.org/doi/10.1145/3219788.3219789

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