Special Issue "Multi-Criteria/Multi-Objective Decision Making and Recommender Systems"
Deadline for manuscript submissions: 30 November 2023 | Viewed by 441
Interests: recommender systems; user modeling; technology-enhanced learning; fintech
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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):
- 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
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