Výsledky projektu Adaptabilita doporučovacích systémů a heterogenní doporučovací ekosystémy

UMUAI - Special Issue on Dynamic Recommender Systems and User Models

In this paper, we focus on the problem of rank-sensitive proportionality preservation when aggregating outputs of multiple recommender systems in dynamic recommendation scenarios. We believe that individual recommenders may provide complementary views on the user’s preferences or needs and therefore their proportional (i.e., unbiased) aggregation may be beneficial for the long-term user satisfaction. We propose an aggregation framework (FuzzDA) based on a modified D’Hondt’s algorithm (DA) for proportional mandates allocation. Specifically, we adjusted DA to register fuzzy membership of items and modified the selection procedure to balance both relevance and proportionality criteria. Furthermore, we propose several iterative votes assignment strategies and negative implicit feedback incorporation strategies to make FuzzDA framework applicable in dynamic recommendation scenarios. Overall, the framework should provide benefits w.r.t. long-term novelty of recommendations, diversity of recommended items as well as overall relevance.
We evaluated FuzzDA framework thoroughly both in offline simulations and in on-line A/B testing. Framework variants outperformed baselines w.r.t. click-through rate (CTR) in most of the evaluated scenarios. Some variants of FuzzDA also provided the best or close-to-best iterative novelty (while main taining very high CTR). While the impact of the framework variants on userwise diversity was not so extensive, the tradeoff between CTR and diversity
seems reasonable.

In this paper we describe a general framework for parallel optimization based on the island model of evolutionary algorithms. The framework runs a number of optimization methods in parallel with periodic communication, in this way, it essentially creates a parallel ensemble of optimization method. At the same time, the system contains a planner that decides which of the available optimization methods should be used to solve the given optimization problem and changes the distribution of such methods during the run of the optimization. Thus, the system effectively solves the problem online parallel portfolio selection. The proposed system is evaluated in a number of common benchmarks with various problem encodings as well as in two real-life problems -- the optimization in recommender systems and the training of neural networks for the control of electric vehicle charging.