research-article

Towards a knowledge based Explainable Recommender Systems

Authors:

Mohammed BerradaAuthors Info & Claims

BDIoT '19: Proceedings of the 4th International Conference on Big Data and Internet of Things

Article No.: 21, Pages 1 - 5

https://doi.org/10.1145/3372938.3372959

Published: 07 January 2020 Publication History

Abstract

Most current Machine Learning based recommender systems act like black boxes, not offering the user any insight into the system logic or justification for the recommendations. Thus, risking losing trust with users and failing to achieve acceptance. The goal of this work is to improve the explainability of recommender systems by using a knowledge extraction method.

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Cited By

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
Afreen N(2024)Explainable and Faithful Educational Recommendations through Causal Language Modelling via Knowledge GraphsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688022(1358-1360)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688022
Deconcini B(2024)Personal Values and Community-Centric Environmental Recommender Systems: Enhancing Sustainability Through User EngagementProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688018(1342-1347)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688018
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Information

Published In

cover image ACM Other conferences

BDIoT '19: Proceedings of the 4th International Conference on Big Data and Internet of Things

October 2019

476 pages

ISBN:9781450372404

DOI:10.1145/3372938

Conference Chairs:
Mohamed Lazaar
ENSIAS, Mohammed V University in Rabat, Morocco
,
Claude Duvallet
Le Havre University in Le Havre, France
,
Mohammed Al Achhab
ENSA, Abdelmalek Essaadi University in Tetuan, Morocco
,
Oussama Mahboub
ENSA, Abdelmalek Essaadi University in Tetuan, Morocco
,
Hassan Silkan
FS, Chouaib Doukkali University in El Jadida, Morocco

Copyright © 2019 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 January 2020

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BDIoT'19

BDIoT'19: The 4th International Conference On Big Data and Internet of Things

October 23 - 24, 2019

Rabat, Morocco

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BDIoT '19 Paper Acceptance Rate 75 of 136 submissions, 55%;

Overall Acceptance Rate 75 of 136 submissions, 55%

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Cited By

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
Afreen N(2024)Explainable and Faithful Educational Recommendations through Causal Language Modelling via Knowledge GraphsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688022(1358-1360)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688022
Deconcini B(2024)Personal Values and Community-Centric Environmental Recommender Systems: Enhancing Sustainability Through User EngagementProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688018(1342-1347)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688018
Sharma CSharma SSharma KSethi GChen H(2024)Exploring explainable AI: a bibliometric analysisDiscover Applied Sciences10.1007/s42452-024-06324-z6:11Online publication date: 14-Nov-2024
https://doi.org/10.1007/s42452-024-06324-z
Jangid MKumar R(2024)Deep learning approaches to address cold start and long tail challenges in recommendation systems: a systematic reviewMultimedia Tools and Applications10.1007/s11042-024-20262-3Online publication date: 16-Oct-2024
https://doi.org/10.1007/s11042-024-20262-3
Buzcu BTessa MTchappi INajjar AHulstijn JCalvaresi DAydoğan R(2024)Towards interactive explanation-based nutrition virtual coaching systemsAutonomous Agents and Multi-Agent Systems10.1007/s10458-023-09634-538:1Online publication date: 20-Jan-2024
https://doi.org/10.1007/s10458-023-09634-5
Samih AHamane ZGhadi AFennan A(2024)Health Recommender SystemsInternational Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD’2023)10.1007/978-3-031-52388-5_25(261-272)Online publication date: 9-Feb-2024
https://doi.org/10.1007/978-3-031-52388-5_25
RUIZ CQUARESMA M(2023)A transparência de sistemas de recomendação de plataformas de Vídeo sob Demanda (VoD): categorias de conteúdosBlucher Design Proceedings10.5151/ped2022-1591479(1609-1624)Online publication date: Mar-2023
https://doi.org/10.5151/ped2022-1591479
Krook JBlockx J(2023)Recommender Systems, Autonomy and User EngagementProceedings of the First International Symposium on Trustworthy Autonomous Systems10.1145/3597512.3599712(1-9)Online publication date: 11-Jul-2023
https://dl.acm.org/doi/10.1145/3597512.3599712
Mehrbod NCosta RMehrbod AAhmed MLampathaki F(2023)Matchmaking Engine for Energy Data Marketplace Using Word Embedding Techniques2023 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC)10.1109/ICE/ITMC58018.2023.10332327(1-7)Online publication date: 19-Jun-2023
https://doi.org/10.1109/ICE/ITMC58018.2023.10332327
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