Experimenting switching hybrid recommender systems

Abstract

Recommender systems employ machine learning and data mining techniques for filtering unseen information and can predict whether a user would like a given resource. To date a number of recommendation algorithms have been proposed, where collaborative filtering and content-based filtering are the two most famous and adopted recommendation techniques. Moreover, machine learning classifiers can be used for recommendation by training them on items' content information. These systems suffer from scalability, data sparsity, over specialisation, and cold-start problems resulting in poor quality recommendations and reduced coverage. Hybrid recommender systems combine individual systems to avoid certain aforementioned limitations of these systems. In this paper, we proposed unique generalised switching hybrid recommendation algorithms that combine machine learning classifiers with the collaborative filtering recommender systems. We also provide various variants of the proposed algorithm by using Singular Value Decomposition (SVD) based recommendations, utilising SVD over collaborative filtering, and utilising SVD combined with Expected Maximisation (EM) algorithm. Experimental results on two different datasets, show that the proposed algorithms are scalable and provide better performance - in terms of accuracy and coverage - than other algorithms while at the same time eliminate some recorded problems with the recommender systems.