Combining Aspects of Genetic Algorithms with Weighted Recommender Hybridization

TL;DR

This paper introduces a genetic algorithm-inspired method for combining unscored recommendations into a single ensemble, outperforming weighted voting methods in movie and name recommendation scenarios.

Contribution

It presents a novel hybridization approach using genetic algorithms for recommender systems, improving accuracy and efficiency over traditional weighted voting methods.

Findings

Outperformed weighted voting by 20.3% and 31.1% in two datasets.

Reduced execution time by up to 19.9%.

Introduced genetic algorithms to the field of recommender hybridization.

Abstract

Recommender systems are established means to inspire users to watch interesting movies, discover baby names, or read books. The recommendation quality further improves by combining the results of multiple recommendation algorithms using hybridization methods. In this paper, we focus on the task of combining unscored recommendations into a single ensemble. Our proposed method is inspired by genetic algorithms. It repeatedly selects items from the recommendations to create a population of items that will be used for the final ensemble. We compare our method with a weighted voting method and test the performance of both in a movie- and name-recommendation scenario. We were able to outperform the weighted method on both datasets by 20.3 % and 31.1 % and decreased the overall execution time by up to 19.9 %. Our results do not only propose a new kind of hybridization method, but introduce the field of recommender hybridization to further work with genetic algorithms.