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.

翻译：在推荐系统领域，我们处理生态系统中的聚合与个性化潜力。个性化基于为每个用户建立的独立聚合模型。当用户偏好与全局偏好存在严重分歧时，该方法能有效揭示用户偏好的差异。混合模型结合了全局权重模型与个性化权重模型，应用于d'Hondt投票算法。本文通过RetailRocket和SLANTour数据集上的实验表明，基于具体案例的个性化混合策略优于非个性化的d'Hondt算法。通过考虑少数群体的意见，我们实现了更高的点击通过率。