Personalisation of d'Hondt's algorithm and its use in recommender ecosystems

Autor:	Balcar, Stepan, Peska, Ladislav, Vojtas, Peter
Rok vydání:	2024
Předmět:	Computer Science - Human-Computer Interaction
Druh dokumentu:	Working Paper
Popis:	In the area of recommender systems, we are dealing with aggregations and potential of personalisation in ecosystems. Personalisation is based on separate aggregation models for each user. This approach reveals differences in user preferences, especially when they are in strict disagreement with global preferences. Hybrid models are based on combination of global and personalised model of weights for d'Hondt's voting algorithm. This paper shows that personalisation combined with hybridisation on case-by-case basis outperforms non-personalised d'Hondt's algorithm on datasets RetailRocket and SLANTour. By taking into account voices of minorities we achieved better click through rate. Comment: The 2022 World Congress in Computer Sience Engineering & Applied Computing (CSCE) July 25-28 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.07264 Zobrazit plný text záznamu View this record from Arxiv