FS-CARS: fast and scalable context-aware news recommender system using tensor factorisation

Abstract

Matrix factorisation is a widely adopted approach of collaborative filtering technique which factorises user-item rating matrix to generate recommendations. User-item rating matrix can be extended to incorporate user's context, resulting in rating tensor which can be factorised to generate better quality context-aware recommendations. Tensor factorisation is computationally intensive task; computational time can be significantly reduced using a distributed and scalable framework. This paper proposes a context-aware news recommender system which classifies news items into different categories and incorporates user's context resulting in rating tensor which is then factorised to generate recommendations. The news items are highly dynamic and are generated in large numbers which can further increase the computational time many fold. To fix the computation time of the process, the proposed system is implemented on distributed and scalable framework of Apache Spark using MLlib library. The proposed recommender system is evaluated for performance and computational time.