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

TL;DR

This paper introduces a personalized hybrid d'Hondt's algorithm for recommender systems that outperforms traditional methods by better capturing minority preferences, leading to improved click-through rates.

Contribution

It proposes a novel hybrid and personalized approach to d'Hondt's algorithm, enhancing recommendation quality by considering individual user preferences.

Findings

Personalized hybrid d'Hondt's algorithm outperforms non-personalized versions.

Better click-through rates achieved by considering minority voices.

Effective on datasets RetailRocket and SLANTour.

Abstract

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.