Overcoming the cold-start challenge in recommender systems: A novel two-stage framework

Cold-start recommendation helps personalize user experiences and improve the relevance of the recommendation system. Despite its importance, cold-start solutions are difficult to develop because new users and items lack interaction data, making user preferences and item relevance prediction difficul...

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Hlavní autoři:	Matam Vinay Kumar, Reddy Nooka Madhusudhana
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	De Gruyter 2025-08-01
Edice:	Open Computer Science
Témata:	cold-start recommendation hierarchical density-based clustering transfer learning bi-gru matrix factorization recommendation systems data sparsity user/item cold-start
On-line přístup:	https://doi.org/10.1515/comp-2025-0038
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