RT Journal Article
T1 BinRec: addressing data sparsity and cold-start challenges in recommender systems with biclustering
A1 Rodríguez Baena, Domingo Savio
A1 Gómez-Vela, Francisco Antonio
A1 López Fernández, Aurelio
A1 García Torres, Miguel
A1 Divina, Federico
K1 Recommender System
K1 Collaborative Filtering
K1 Biclustering
AB Recommender Systems help users in making decision in different fields such as purchases or what movies to watch. User-Based Collaborative Filtering (UBCF) approach is one of the most commonly used techniques for developing these software tools. It is based on the idea that users who have previously shared similar tastes will almost certainly share similar tastes in the future. As a result, determining the nearest users to the one for whom recommendations are sought (active user) is critical. However, the massive growth of online commercial data has made this task especially difficult. As a result, Biclustering techniques have been used in recent years to perform a local search for the nearest users in subgroups of users with similar rating behaviour under a subgroup of items (biclusters), rather than searching the entire rating database. Nevertheless, due to the large size of these databases, the number of biclusters generated can be extremely high, making their processing very complex. In this paper we propose BinRec, a novel UBCF approach based on Biclustering. BinRec simplifies the search for neighbouring users by determining which ones are nearest to the active user based on the number of biclusters shared by the users. Experimental results show that BinRec outperforms other state-of-the-art recommender systems, with a remarkable improvement in environments with high data sparsity. The flexibility and scalability of the method position it as an efficient alternative for common collaborative filtering problems such as sparsity or cold-start.
PB Springer Nature
YR 2025
FD 2025-07-01
LK https://hdl.handle.net/10433/24296
UL https://hdl.handle.net/10433/24296
LA en
NO Rodríguez-Baena, D., Gómez-Vela, F., Lopez-Fernandez, A. et al. BinRec: addressing data sparsity and cold-start challenges in recommender systems with biclustering. Appl Intell 55, 830 (2025). https://doi.org/10.1007/s10489-025-06725-6
NO Universidad Pablo de Olavide
NO Departamento de Deporte e Informática
NO Grupo PAID TIC - 239
DS RIO
RD May 21, 2026