A Multi-Perspective Evaluation of MA and GA for Collaborative Filtering Recommender System

The rising popularity of evolutionary algorithms to solve complex problems has inspired researchers to explore their utility in recommender systems. Recommender systems are intelligent web applications which generate recommendations keeping in view the user's stated and unstated requirements. Evolutionary approaches like Genetic and memetic algorithms have been considered as one of the most successful approaches for combinatorial optimization. Memetic Algorithms (MAs) are enhanced genetic algorithms which incorporate local search in the evolutionary scheme. Local Search process on each solution after every generation helps in improving the convergence time of MA. This paper presents multi-perspective comparative evaluation of memetic and genetic evolutionary algorithms for model based collaborative filtering recommender system. Experimental study was conducted on MovieLens dataset to investigate the decision support and statistical efficiency of Memetic and genetic algorithms. Algorithms were analyzed from different perspectives like variation in number of clusters, effect of increasing the number of users, varying number of recommendations and using either one or more than one cluster for computing ratings of the unrated items. Results obtained demonstrated that from all perspectives memetic collaborative filtering algorithm has better predictive accuracy as compared genetic collaborative filtering algorithm.