Improving collaborative filtering recommender system results and performance using genetic algorithms

This paper presents a metric to measure similarity between users, which is applicable in collaborative filtering processes carried out in recommender systems. The proposed metric is formulated via a simple linear combination of values and weights. Values are calculated for each pair of users between which the similarity is obtained, whilst weights are only calculated once, making use of a prior stage in which a genetic algorithm extracts weightings from the recommender system which depend on the specific nature of the data from each recommender system. The results obtained present significant improvements in prediction quality, recommendation quality and performance.

► Metric formulated via a simple linear combination of values and weights.
► Model-based approach using genetic algorithms to improve results.
► Collaborative filtering predictions accuracy and performance improvements.