Context Aware Recommender Systems: A Novel Approach Based on Matrix Factorization and Contextual Bias
Mario Casillo, Brij B. Gupta, Marco Lombardi, Angelo Lorusso, Domenico Santaniello, Carmine Valentino
Abstract
In the world of Big Data, a tool capable of filtering data and providing choice support is crucial. Recommender Systems have this aim. These have evolved further through the use of information that would improve the ability to suggest. Among the possible exploited information, the context is widely used in literature and leads to the definition of the Context-Aware Recommender System. This paper proposes a Context-Aware Recommender System based on the concept of embedded context. This technique has been tested on different datasets to evaluate its accuracy. In particular, the use of multiple datasets allows a deep analysis of the advantages and disadvantages of the proposed approach. The numerical results obtained are promising.