Introducing CSP Dataset: A Dataset Optimized for the Study of the Cold Start Problem in Recommender Systems
Metadatos
Afficher la notice complèteAuteur
Herce Zelaya, Julio; Porcel Gallego, Carlos Gustavo; Tejeda Lorente, Álvaro; Bernabé Moreno, Juan; Herrera Viedma, EnriqueEditorial
MDPI
Materia
Recommender systems Datasets Cold start problem New user problem
Date
2022-12-29Referencia bibliográfica
Herce-Zelaya, J... [et al.]. Introducing CSP Dataset: A Dataset Optimized for the Study of the Cold Start Problem in Recommender Systems. Information 2023, 14, 19. [https://doi.org/10.3390/info14010019]
Patrocinador
Spanish Government PID2019-103880RB-I00; Andalusian Agency project P20_00673Résumé
Recommender systems are tools that help users in the decision-making process of choosing
items that may be relevant for them among a vast amount of other items. One of the main problems
of recommender systems is the cold start problem, which occurs when either new items or new
users are added to the system and, therefore, there is no previous information about them. This
article presents a multi-source dataset optimized for the study and the alleviation of the cold start
problem. This dataset contains info about the users, the items (movies), and ratings with some
contextual information. The article also presents an example user behavior-driven algorithm using
the introduced dataset for creating recommendations under the cold start situation. In order to create
these recommendations, a mixed method using collaborative filtering and user-item classification has
been proposed. The results show recommendations with high accuracy and prove the dataset to be
a very good asset for future research in the field of recommender systems in general and with the
cold start problem in particular.