Evolutionary Approach to Recommender Systems Improvement by Directory of Products Optimization

Abstract

This paper concerns improving recommender systems using evolutionary algorithms that optimize the composition of the directory of products in the recommender system. It focuses on merging some infrequent products into certain groups of similar products and replacing the regular products by some aggregates of products, such as a category of products or a cluster of products in the product embedding space, and preparing recommendations with such aggregates. Evolutionary algorithms endeavor to decide which products should be processed as single products and which ones should be merged with others into smaller or larger groups of products (merging products into groups is done by a given auxiliary merging strategy that cluster similar products into groups) with the aim to maximize the accuracy of the recommender system. Computational experiments on benchmark datasets confirmed the efficiency of the approach.