Adaptive Genetic Algorithm for Improving Prediction Accuracy of a Multi-Criteria Recommender System

Abstract

Recommender systems are software tools used to make valuable recommendations to users. Traditionally, recommender systems use information obtained from ratings of an item by users with similar opinions to make recommendations. A user uses a single rating to represent the degree of likeness of an item in traditional recommender systems. Though this approach has reasonably shown a good prediction accuracy, however, the performance of traditional recommender systems is considered inadequate, as users could have different opinions based on some specific features of an item. Multi-criteria recommendation extends the traditional techniques by incorporating ratings for various attributes of the items. It provides better recommendations for users as the system allows the opportunity for users to specify their preferences based on different attributes of user item, which improves prediction accuracy. In this paper, we proposed an aggregation function based method that uses an adaptive genetic algorithm to efficiently incorporate the criteria ratings for improving the accuracy of the multi-criteria recommender system. We carried out an experiment using a dataset for multi-criteria recommendations of movies to users. The experimental result shows that our proposed approach provides better accuracy than the corresponding traditional technique.