Investigating the Scalability of Algorithms, the Role of Similarity Metric and the List of Suggested Items Construction Scheme in Recommender Systems

Abstract

The continuous increase in demand for new products and services on the market brought the need for systematic improvement of recommendation technologies. Recommender systems proved to be the answer to the data overload problem and an advantage for e-business. Nevertheless, challenges that recommender systems face, like sparsity and scalability, affect their performance in real-world situations where both the number of users and items are high and item rating is infrequent. In this article we propose a cluster based recommendation approach using genetic algorithms. Users are grouped into clusters based on their past choices and preferences and receive recommendations from the other cluster members with the aid of an innovative recommendation scheme called Top-Nvoted items. Similarity between users is computed using the max_norm Pearson coefficient. This is a modified form of the widely used Pearson coefficient and it is used to prevent very active users dominating recommendations. We compare our approach with five well established recommendation methods with the aid of three different datasets. These datasets vary in terms of the number of users, the number of items, and the sparsity of ratings. As a result important conclusions are drawn about the efficiency of each method with respect to scalability and dataset's sparsity