Context Clustering-based Recommender Systems

Abstract

Recommender systems have gained lots of attention due to the rapid increase in the amount of data on the internet. Therefore, the demand for finding more advanced techniques to generate more useful recommendations becomes an urgent. The increasing need for generating more relevant recommendations led to the emergence of many novel recommendation systems, such as Context-aware Recommender System (CARS), which is based on incorporating the contextual information in recommendation systems. The goal of this paper is to propose new recommender systems that utilize the contextual information to find more relevant recommendations. In this paper, we propose CoC1, a novel Context Clustering-based recommender system. We introduce two approaches which utilize the contextual information and KMeans clustering algorithm to generate new forms of user-item matrices. We show that the accuracy of CoC1 which uses the new user-item matrices has been improved comparing with the accuracy of classical recommender system which uses the original user-item matrix.