Personalized Implicit Negative Feedback Enhancements for Fuzzy D'Hondt's Recommendation Aggregations
Abstract
In this paper, we focus on the problems of fair aggregation of recommender systems (RS) and over-exposure of users with insignificant recommendations. While fair aggregation of diverse RS may contribute to both calibration and diversity challenges, some recently proposed methods suffer from repeating the same set of recommendations to the user over and over again.
However, it may be difficult to distinguish between situations when users ignore recommendations because they are irrelevant or because they did not notice them. In order to cope with these challenges, we propose an innovative off-line RS evaluation methodology based on the noticeability of recommended items.
We further propose a Fuzzy D'Hondt's algorithm with personalized implicit negative feedback attribution (FDHondtINF). The algorithm is designed to provide a fair ordering of candidate items coming from multiple individual RS, while considering also the objects previously ignored by the current user.
FDHondtINF was evaluated off-line along with other aggregation methods and individual RS on MovieLens 1M dataset. The algorithm performs especially well in situations when the recommended items are less noticeable, or when a sequence of multiple recommendations for the same user model is given.