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Temporal Community-Based Collaborative Filtering to Relieve from Cold-Start and Sparsity Problems
2018
International Journal of Intelligent Systems and Applications
Recommender systems inherently dynamic in nature and exponentially grow with time, in terms of interests and behaviour patterns. Traditional recommender systems rely on similarity of users or items in static networks where the user/item neighbourhood is almost same and they generate the same recommendations since the network is constant. This paper proposes a novel architecture, called Temporal Community-based Collaborative filtering, which is an association of recommendation and the dynamic
doi:10.5815/ijisa.2018.10.06
fatcat:ciabnek3zffxbagvyefjhp2rn4