Context-aware based evolutionary collaborative filtering algorithm

Abstract

Recommender systems are tools that provide personalized suggestions of items for users. They must be able to adapt constantly to user preferences and behavior in order to generate relevant recommendations. However, initial works in this field do not consider the context in which the users are. In recent years, a new recommendation technique, called Context-Aware Recommender System (CARS), has emerged. This approach integrates contextual information about users and/or items in the recommendation process to satisfy even more users’ needs. Therefore, accurate prediction depends upon the degree to which a recommendation method has incorporated the relevant contextual data. To address this issue, we propose to combine user based collaborative filtering with the Genetic Algorithm based meta-heuristic in order to provide better predictions for users. The proposed model uses a weighting function which incorporates the contextual factors that influence the users’ decisions. It is based on the Genetic Algorithm based meta-heuristic to estimate, for each contextual parameter, a degree of importance that would reduce the mean absolute error and increase the F-measure. Experimental results from Movielens dataset validate that our proposed algorithm improves recommendations accuracy.