  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.
