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A Comprehensive Study of Application of Evolutionary Computing in Multi-stakeholder Recommendation Systems

Manas Kumar Yogi, CH.E.N. Sai Priya

Abstract


Recommender systems are customized data get to applications; they are universal in the present online condition, and compelling at finding things that address client issues and tastes. As the range of recommender systems has broadened, it has become clear that the resolute spotlight on the client regular to scholastic research has clouded other significant parts of suggestion results. Properties, for example, reasonableness, equalization, profitability, and correspondence are most certainly not caught by run of the mill measurements for recommender framework assessment. The idea of multi-stakeholder suggestion has risen as a binding together structure for portraying and understanding suggestion settings where the end user isn’t the sole core interest. This article portrays the usage of evolutionary computing methods for multi-stakeholder suggestions, and the scene of framework plans. Recommender systems are ordinarily intended to streamline the utility of the end client. In numerous settings, in any case, the end client isn’t the main partner and this select center might deliver unsuitable outcomes for different partners. One such setting is found in multisided stages, which unite purchasers and venders. In such stages, it very well might be important to improve the incentive for the two purchasers and merchants mutually.


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References


Abdollahpouri H, Burke R, Mobasher B. Controlling popularity bias in learning to rank recommendation. In: Proceedings of the 11th ACM conference on recommender systems; 2017: Chicago, IL, USA: DePaul University; 2017. [Online] Available at https://scds.cdm.depaul.edu/wp-content/uploads/2017/05/SOCRS_2017_paper_5.pdf [Accessed on August 2023]

Abdollahpouri H, Burke R, Mobasher B. Recommender systems as multistakeholder environments. In: Proceedings of the 25th conference on user modeling, adaptation and personalization (UMAP’17), page to appear, Bratislava, Slovakia: ACM; 2017: 347–348. doi: 10.1145/3079628.3079657.

Adamopoulos P, Tuzhilin A. The business value of recommendations: a privacy-preserving econometric analysis. In: Proceedings of the Thirty Sixth International Conference on Information Systems; Atlanta, GA, USA: Association for Information Systems; 2015.

Alanazi A, Bain M. A scalable people-to-people hybrid reciprocal recommender using hidden Markov models. In: 2nd International Work on Machine Learning Methods in Recommendation Systems. 2016. [Online] Available at https://doogkong.github.io/2016/papers/alanazi2016scalable.pdf [Accessed on August 2023]

Burke R, Abdollahpouri H. Educational recommendation with multiple stakeholders. In: Third International Workshop on Educational Recommender Systems; 13-16 October 2016; Omaha, NE, USA: IEEE; 2016. doi: 10.1109/WIW.2016.028.

Burke R, Sonboli N, Ordonez-Gauger A. Balanced neighborhoods for multi-sided fairness in recommendation. In: Conference on Fairness, Accountability and Transparency, FAT; 2018; pp. 202–214.

Ekstrand MD, Tian Mucun, Mohammed R, Kazi I, Mehrpouyan Hoda, Kluver D. Exploring author gender in book rating and recommendation. Acad Med. 2018: 242–250. doi: 10.1145/3240323.3240373.

Jannach D, Adomavicius G. Price and profit awareness in recommender systems. In: Proceedings of the ACM RecSys 2017 Workshop on Value-aware and Multi-stakeholder Recommendation, Como, Italy; 2017.

Eiben AE, Schoenauer M. Evolutionary computing. Inf Process Lett. 2002; 82 (1): 1–6. doi: 10.1016/S0020-0190(02)00204-1.

Arulkumaran K, Cully A, Togelius J. Alphastar: an evolutionary computation perspective. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion; 13–17 Jul 2019; Prague, Czech Republic: ACM; 2019. pp. 314–315.


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