Building scalable and accurate hybrid kernel mapping recommender systems

Recommender systems are intelligent systems that can overcome information overload problem. Kernel Mapping Recommender (KMR) systems have been proposed, which give state-of-the-art performance. However, the performance of the KMR algorithm suffers under cold-start and sparse problems. From this line of research, this paper proposes a hybrid framework that can efficiently integrate different versions, namely, item-based and user-based KMR-of KMR algorithm. We have proposed various heuristic algorithms that integrate different versions of KMR (into a unified framework) resulting in improved accuracy and elimination of problems associated with conventional recommender system. We have tested our system on publically available movies dataset and benchmark with KMR. The results reveal that the proposed algorithm yields robust results under cold-start and sparse scenarios.