Mitigation of Popularity Bias in Recommendation Systems
Conference paper
Authors | Karboua, S., Harrag, F., Meziane, F. and Boutadjine, A. |
---|---|
Type | Conference paper |
Abstract | In response to the quantity of information available on the Internet, many online service providers are attempting to customize their services and make content access more simple via recommender systems (RSs) to support users in discovering the products they are most likely interested in. However, these recommendation systems are prone to popularity bias, which is a tendency to promote popular items even if they do not satisfy a user’s preferences and then provide customers with recommendations of poor quality. Such a bias has a negative influence on both users and item providers. It is then essential to mitigate such bias in order to guarantee that less popular but pertinent items show up on the user’s |
Keywords | Popularity bias; Recommender System; Fairness; Mitigation |
Year | 2022 |
Conference | Tunisian-Algerian Joint Conference on Applied Computing |
Web address (URL) | https://ceur-ws.org/Vol-3333/Paper7.pdf |
Accepted author manuscript | License File Access Level Open |
Publisher's version | License File Access Level Open |
Web address (URL) of conference proceedings | https://ceur-ws.org/Vol-3333/ |
Output status | Published |
Publication dates | |
Online | 13 Dec 2022 |
Publication process dates | |
Accepted | 13 Dec 2022 |
Deposited | 21 Feb 2023 |
https://repository.derby.ac.uk/item/9x10q/mitigation-of-popularity-bias-in-recommendation-systems
Download files
Accepted author manuscript
TACC2022.pdf | ||
License: CC BY 4.0 | ||
File access level: Open |
Publisher's version
TACC2022.pdf | ||
License: CC BY 4.0 | ||
File access level: Open |
50
total views30
total downloads4
views this month2
downloads this month
Export as
Related outputs
Diagnosis of Breast Cancer Based on Hybrid Features Extraction in Dynamic Contrast Enhanced Magnetic Resonance Imaging
Hasan, A.M., Aljobouri, H.K., Al-Waely, K.N.A., Ibrahim, W.I., Jalab, H.A. and Meziane, F. 2023. Diagnosis of Breast Cancer Based on Hybrid Features Extraction in Dynamic Contrast Enhanced Magnetic Resonance Imaging. Neural Computing and Applications. pp. 1-14. https://doi.org/10.1007/s00521-023-08909-yClassification Model of Breast Masses in DCE-MRI Using Kinetic Curves Features with Quantum-Raina’s Polynomial Based Fusion
Hasan, A.M., Al-Waely, N.K.N., Ajobouri, H.K., Ibrahim, R.W., Jalab, H.A. and Meziane, F. 2023. Classification Model of Breast Masses in DCE-MRI Using Kinetic Curves Features with Quantum-Raina’s Polynomial Based Fusion. Biomedical Signal Processing and Control. 84, pp. 1-12. https://doi.org/10.1016/j.bspc.2023.105002The Impact of Arabic Diacritization on Word Embeddings
Abbache, M., Abbache, A., Xu, J.W., Meziane, F. and Wen, X.B. 2023. The Impact of Arabic Diacritization on Word Embeddings. ACM Transactions on Asian and Low-Resource Language Information Processing . pp. 1-32. https://doi.org/10.1145/3592603
A review of the generation of requirements specification in natural language using objects UML models and domain ontology
Abdalazeima, Alaa and Meziane, Farid 2021. A review of the generation of requirements specification in natural language using objects UML models and domain ontology. Procedia Computer Science. 189, pp. 328-334. https://doi.org/10.1016/j.procs.2021.05.102Describing Pulmonary Nodules Using 3D Clustering
Al-Funjan, A., Farid Meziane and Aspin, R. 2022. Describing Pulmonary Nodules Using 3D Clustering. Advanced Engineering Research. 22 (3), pp. 261-271. https://doi.org/10.23947/2687-1653-2022-22-3-261-271Credit Risk Prediction for Peer-To-Peer Lending Platforms: An Explainable Machine Learning Approach
Swee, C.P., Labadin, J. and Meziane, F. 2022. Credit Risk Prediction for Peer-To-Peer Lending Platforms: An Explainable Machine Learning Approach. Journal of Computing and Social Informatics. 1 (2), pp. 1-16. https://doi.org/10.33736/jcsi.4761.2022DCOPA: a distributed clustering based on objects performances aggregation for hierarchical communications in IoT applications
Mir, F. and Meziane, F. 2022. DCOPA: a distributed clustering based on objects performances aggregation for hierarchical communications in IoT applications. Cluster Computing. 26, p. 1077–1098. https://doi.org/10.1007/s10586-022-03741-w
Botnet detection used fast-flux technique, based on adaptive dynamic evolving spiking neural network algorithm
Almomani, Ammar, Nawasrah, Ahmad Al, Alauthman, Mohammad, Betar, Mohammed Azmi Al and Meziane, Farid 2021. Botnet detection used fast-flux technique, based on adaptive dynamic evolving spiking neural network algorithm. International Journal of Ad Hoc and Ubiquitous Computing. 36 (1), p. 50. https://doi.org/10.1016/j.cosrev.2020.100305
MRI brain classification using the quantum entropy LBP and deep-learning-based features
Hasan, Ali M., Jalab, Hamid A., Ibrahim, Rabha W., Meziane, Farid, AL-Shamasneh, Ala’a R. and Obaiys, Suzan J. 2020. MRI brain classification using the quantum entropy LBP and deep-learning-based features. Entropy. 22 (9), p. 1033. https://doi.org/10.3390/e22091033