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Fuzzy D'Hondt's Algorithm for On-line Recommendations Aggregation
2019
ACM Conference on Recommender Systems
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
dblp:conf/recsys/PeskaB19
fatcat:judkp64q6zb25ayqhdl2vlytmu