Multi-criteria/Multi-objective Decision Making and Recommender Systems
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (20 July 2024) | Viewed by 2987
Special Issue Editor
Interests: recommender systems; user modeling; technology-enhanced learning; fintech
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recommender systems (RSs) have been widely applied to several domains and applications. Traditional RSs usually deal with a single objective, such as minimizing the prediction errors or maximizing the ranking of the recommendation list. There is an emerging demand for multi-objective/multi-criteria optimization so that the development of recommendation models can take multiple objectives/criteria into consideration. Multi-criteria decision making (MCDM) has been well established and developed for business intelligence, while multi-objective optimization (MOO) can be one of the solutions in MCDM. However, there is a gap between the MCDM/MOO theories and its applications in the area of recommender systems. This Special Issue encourages submissions related to multi-objective/multi-criteria recommender systems. Particularly, we are interested in technologies or solutions that can fill the gap between MCDM/MOO theories and its applications in RSs.
Below is a list of relevant articles for this Special Issue:
- Yong Zheng, David (Xuejun) Wang. "Multi-Criteria Decision Making and Recommender Systems", The 28th ACM Conference on Intelligent User Interfaces (ACM IUI), Sydney, Australia, March, 2023
- Yong Zheng, David (Xuejun) Wang. "A Survey of Recommender Systems with Multi-Objective Optimization", Neurocomputing, Vol. 474, p. 141-153, Elsevier, 2022
- Reinaldo Silva Fortes, Daniel Xavier de Sousa, Dayanne G. Coelho, Anisio M. Lacerda, and Marcos André Gonçalves. "Individualized extreme dominance (IndED): A new preference-based method for multi-objective recommender systems". Information Sciences, Volume 572, 2021
- Yong Zheng, David (Xuejun) Wang. "Multi-Objective Recommendations", Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), August, 2021
- Reinaldo Silva Fortes, Anisio Mendes Lacerda, Alan R.R. Freitas, Carlos Bruckner, Dayanne Coelho, and Marcos André Gonçalves. "User-oriented Objective Prioritization for Meta-Featured Multi-Objective Recommender Systems". In Proceedings of the User Modeling, Adaptation, and Personalization (UMAP). Singapore. July, 2018
- Reinaldo Silva Fortes, Alan R.R. de Freitas, and Marcos André Gonçalves. "A Multicriteria Evaluation of Hybrid Recommender Systems: On the Usefulness of Input Data Characteristics". In Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 2: ICEIS. Porto, Portugal. April, 2017
The topics of interests in this Special Issue include (but are not limited to):
Multi-Objective/Multi-Criteria Recommendations
- Novel multi-objective/multi-criteria recommendation applications;
- Promising multi-objective/multi-criteria recommendation models;
- Evaluation methods for multi-objective/multi-criteria recommendations;
- Human-centric considerations in multi-objective/multi-criteria recommendations.
Multi-Objective/Multi-criteria Optimizations (MOO) for Recommender Systems
- Effective multi-objective/multi-criteria optimization methods for RecSys;
- Comparison of different multi-objective/multi-criteria optimization methods;
- Novel scalarization or multi-objective evolutionary algorithms for RecSys.
Techniques from MOO/MCDM for Recommender Systems
- The recommendation task may not be formulated as a multi-objective/multi-criteria optimization problem, but techniques or components in MOO/MCDM can be reused in the recommendation models or process.
Dr. Yong Zheng
Guest Editor
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