Genetic Algorithms for Feature Weighting in Multi-criteria Recommender Systems

Abstract

Recommender systems have been emerging as a powerful technique of e-commerce. The majority of existing recommender systems uses an overall rating value on items for evaluating user’s preference opinions. Because users might express their opinions based on some specific features of the item, recommender systems solely based on a single criterion could produce recommendations that do not meet user needs. In this paper, we propose a mechanism for integrating multiple criteria into the Collaborative Filtering (CF) algorithm. Specifically, we present the implementation of Genetic Algorithms (GA) for optimal feature weighting. The proposed system consists of two main parts. First, the weight of each user toward each feature is computed by using GAs. The feature weights are then incorporated into the collaborative filtering process to provide recommendations. Empirical studies have shown that our weighting scheme can be incorporated to improve the performance of multicriteria CF.