To address the sparsity and cold start problem of collaborative filtering, researchers usually
make use of side information, such as social networks or item attributes, to improve …

To address the sparsity and cold-start problem of collaborative filtering, researchers usually
make use of side information, such as social networks or item attributes, to improve the …

To alleviate sparsity and cold start problem of collaborative filtering based recommender
systems, researchers and engineers usually collect attributes of users and items, and design …

Collaborative filtering often suffers from sparsity and cold start problems in real
recommendation scenarios, therefore, researchers and engineers usually use side …

Abstract Knowledge graphs have shown to be highly beneficial to recommender systems,
providing an ideal data structure to generate hybrid recommendations using both content …

Since it can effectively address the problem of sparsity and cold start of collaborative
filtering, knowledge graph (KG) is widely studied and employed as side information in the …

Among different recommendation techniques, collaborative filtering usually suffer from
limited performance due to the sparsity of user-item interactions. To address the issues …

Deep Learning and factorization-based collaborative filtering recommendation models have
undoubtedly dominated the scene of recommender systems in recent years. However …

Recently, much attention has been paid to the usage of knowledge graph within the context
of recommender systems to alleviate the data sparsity and cold-start problems. However …

To solve the information explosion problem and enhance user experience in various online
applications, recommender systems have been developed to model users' preferences …