Author(s): Hema Banati | Shikha Mehta
Journal: International Journal of Computer Science & Information Technology
ISSN 0975-4660
Volume: 2;
Issue: 5;
Start page: 103;
Date: 2010;
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Keywords: Memetic collaborative filtering | Genetic collaborative filtering | Memetic recommender systems | Genetic recommender system | Evolutionary collaborative filtering.
ABSTRACT
The rising popularity of evolutionary algorithms to solve complex problems has inspired researchers toexplore their utility in recommender systems. Recommender systems are intelligent web applications whichgenerate recommendations keeping in view the user’s stated and unstated requirements. Evolutionaryapproaches like Genetic and memetic algorithms have been considered as one of the most successfulapproaches for combinatorial optimization. Memetic Algorithms (MAs) are enhanced genetic algorithmswhich incorporate local search in the evolutionary scheme. Local Search process on each solution afterevery generation helps in improving the convergence time of MA. This paper presents multi-perspectivecomparative evaluation of memetic and genetic evolutionary algorithms for model based collaborativefiltering recommender system. Experimental study was conducted on MovieLens dataset to investigate thedecision support and statistical efficiency of Memetic and genetic algorithms. Algorithms were analyzedfrom 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 ratingsof the unrated items. Results obtained demonstrated that from all perspectives memetic collaborativefiltering algorithm has better predictive accuracy as compared genetic collaborative filtering algorithm.
Journal: International Journal of Computer Science & Information Technology
ISSN 0975-4660
Volume: 2;
Issue: 5;
Start page: 103;
Date: 2010;
VIEW PDF
DOWNLOAD PDF Original pageKeywords: Memetic collaborative filtering | Genetic collaborative filtering | Memetic recommender systems | Genetic recommender system | Evolutionary collaborative filtering.
ABSTRACT
The rising popularity of evolutionary algorithms to solve complex problems has inspired researchers toexplore their utility in recommender systems. Recommender systems are intelligent web applications whichgenerate recommendations keeping in view the user’s stated and unstated requirements. Evolutionaryapproaches like Genetic and memetic algorithms have been considered as one of the most successfulapproaches for combinatorial optimization. Memetic Algorithms (MAs) are enhanced genetic algorithmswhich incorporate local search in the evolutionary scheme. Local Search process on each solution afterevery generation helps in improving the convergence time of MA. This paper presents multi-perspectivecomparative evaluation of memetic and genetic evolutionary algorithms for model based collaborativefiltering recommender system. Experimental study was conducted on MovieLens dataset to investigate thedecision support and statistical efficiency of Memetic and genetic algorithms. Algorithms were analyzedfrom 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 ratingsof the unrated items. Results obtained demonstrated that from all perspectives memetic collaborativefiltering algorithm has better predictive accuracy as compared genetic collaborative filtering algorithm.
