Zobrazeno 1 - 10
of 81
pro vyhledávání: '"Bobadilla, Jesus"'
Autor:
Bobadilla, Jesús, Gutiérrez, Abraham
Publikováno v:
International Journal of Interactive Multimedia and Artificial Intelligence, 2023
The published method Generative Adversarial Networks for Recommender Systems (GANRS) allows generating data sets for collaborative filtering recommendation systems. The GANRS source code is available along with a representative set of generated datas
Externí odkaz:
http://arxiv.org/abs/2410.17651
Publikováno v:
International Journal of Interactive Multimedia and Artificial Intelligence, Volume 8, number 6, Pages 15-23, 2024
Matrix factorization models are the core of current commercial collaborative filtering Recommender Systems. This paper tested six representative matrix factorization models, using four collaborative filtering datasets. Experiments have tested a varie
Externí odkaz:
http://arxiv.org/abs/2410.17644
Publikováno v:
International Journal of Interactive Multimedia and Artificial Intelligence, Volume 7, number 4, Pages 18-26, 2022
Neural collaborative filtering is the state of art field in the recommender systems area; it provides some models that obtain accurate predictions and recommendations. These models are regression-based, and they just return rating predictions. This p
Externí odkaz:
http://arxiv.org/abs/2410.16838
Publikováno v:
Information Sciences 442-443, 145-157 (2018)
Users want to know the reliability of the recommendations; they do not accept high predictions if there is no reliability evidence. Recommender systems should provide reliability values associated with the predictions. Research into reliability measu
Externí odkaz:
http://arxiv.org/abs/2402.04457
Publikováno v:
Knowledge-Based Systems, 152, 94-99 (2018)
Recommender Systems (RS) provide a relevant tool to mitigate the information overload problem. A large number of researchers have published hundreds of papers to improve different RS features. It is advisable to use RS frameworks that simplify RS res
Externí odkaz:
http://arxiv.org/abs/2402.01008
Modern society devotes a significant amount of time to digital interaction. Many of our daily actions are carried out through digital means. This has led to the emergence of numerous Artificial Intelligence tools that assist us in various aspects of
Externí odkaz:
http://arxiv.org/abs/2307.09447
Recommendation to groups of users is a challenging subfield of recommendation systems. Its key concept is how and where to make the aggregation of each set of user information into an individual entity, such as a ranked recommendation list, a virtual
Externí odkaz:
http://arxiv.org/abs/2303.07001
Research and education in machine learning needs diverse, representative, and open datasets that contain sufficient samples to handle the necessary training, validation, and testing tasks. Currently, the Recommender Systems area includes a large numb
Externí odkaz:
http://arxiv.org/abs/2303.01297
Deep learning provides accurate collaborative filtering models to improve recommender system results. Deep matrix factorization and their related collaborative neural networks are the state-of-art in the field; nevertheless, both models lack the nece
Externí odkaz:
http://arxiv.org/abs/2107.12677
Publikováno v:
Neural Computing and Applications, 1-18, 2020
Extracting demographic features from hidden factors is an innovative concept that provides multiple and relevant applications. The matrix factorization model generates factors which do not incorporate semantic knowledge. This paper provides a deep le
Externí odkaz:
http://arxiv.org/abs/2006.12379