Abstrakt
In this paper, we present Fuzzy D'Hondt's algorithm suitable to aggregate lists of recommended objects originating from various base recommending methods. The algorithm is inspired by D'Hondt's election method used to a proportional conversion of votes to mandates in public elections.
We enhance the original approach to enable fuzzy candidate-party membership, propose a gradient learning of per-party votes assignments and utilize it for iterative on-line aggregation of recommendations. Main features of the proposed algorithm are ability to iteratively learn relevance of individual base recommenders (parties), ability to account for multiple item's memberships and capability to provide proportional representation of base recommenders w.r.t. their results as well as fair ordering of the final list of recommended items.
Fuzzy D'Hondt's aggregation method was evaluated in on-line A/B testing against state-of-the-art approach based on multi-armed bandits with Thompson sampling and achieved competitive results.