Memetic Collaborative Filtering Based Recommender System

Abstract

Web based Decision Support systems like recommendation systems have become effective tools for decision making in the recent past. However the recommender systems employing conventional clustering techniques (KRS) like K-Means for collaborative filtering, suffer from the limitation of getting local optimum results. This paper presents Memetic Recommender System (MRS) based on the collaborative behavior of memes. Memetic Algorithms (MAs) are considered as one of the most successful approaches for combinatorial optimization. MAs are the genetic algorithms which incorporate local search in the evolutionary scheme. We propose a distinctive strategy to perform local search in memetic algorithms. MRS works in 2 phases-In the first phase a model is developed based on Memetic Clustering algorithm and in the second phase trained model is used to predict recommendations for the active user. Rigorous experiments were conducted to prove the decision support and statistical efficacy of MRS visa vis KRS. Results confirmed that the proposed approach yields much better performance as compared to the conventional collaborative filtering recommender system.