An Effective Hybrid Algorithm in Recommender Systems Based on Fast Genetic k-means and Information Gain

摘要

Personalization in a recommender system is to customize contents for users based on their preferences and interests. For a new user such systems face cold start problem. This is because system knows nothing about this user and is unable to present recommendations. For the above said problem an existing technique, Information Gain through Clustered Neighbors (IGCN), has proved to be productive but this technique uses k-means algorithm for making user clusters. The problem with k-means algorithm is it might get stuck at local optima and has initial value dependency. Genetic k-means Algorithm (GKA), a hybrid clustering technique, converges to global optima faster than traditional Genetic Algorithms (GAs). The performance of this technique was improved by Fast Genetic K-means algorithm (FGKA). As the above mentioned GAs has proved to overcome disadvantages of k-means, the paper intends to use a GA viz. FGKA for clustering instead of k-means due to its better performance. This is why the proposed algorithm is named Information Gain Clustering through Fast Genetic k-means Algorithm (IGCFGKA). We show through our results that IGCFGKA not only overcomes k-means disadvantages but it also provides high quality recommendations and an optimal or near optimal solution. Our paper is first to compare IGCFGKA with various strategies of Information gain in recommender systems.