Improved Collaborative Filtering using Evolutionary Algorithm based Feature Extraction

Abstract

ubiquity of Collaborative Filtering systems is evident in the wide variety of domains to which they have been applied successfully. However a major challenge to such systems is the high dimensionality and sparsity of the expressed preferences. Dealing effectively with large user profiles would improve the scalability of the system whereas reducing sparsity would increase the quality of recommendations. Several approaches in this direction have focused on feature selection and feature extraction in order to reduce the data dimension and thus make the recommendation process more scalable. Some of the features extraction techniques are based on extracting content based features. However many such solutions have been handcrafted and thus not guaranteed to work optimally under all data environments. This work explores Evolutionary algorithms based feature extraction techniques where the extracted features may describe user or item profiles. The features constructed/extracted thus are compact, dense and are discriminative. Moreover they have the advantage of requiring no extra information (such as content description) and are adaptive, delivering the optimal feature extraction scheme for the particular dataset. We have performed experiments with the popular MovieLens dataset and have compared the user-based and item-based evolutionary feature extraction schemes with respect to their accuracy. The experiments establish that the evolutionary feature extraction schemes score over traditional algorithms as well as content-based feature extraction schemes. Moreover we find that the item-based evolutionary feature extraction schemes outperform their user-based counterparts under varying parameter values.