ListarUNIR REVISTAS por tema "collaborative filtering"
Mostrando ítems 1-8 de 8
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A Collaborative Filtering Probabilistic Approach for Recommendation to Large Homogeneous and Automatically Detected Groups
(International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 06/2020)In the collaborative filtering recommender systems (CFRS) field, recommendation to group of users is mainly focused on stablished, occasional or random groups. These groups have a little number of users: relatives, friends, ... -
An Adapted Approach for User Profiling in a Recommendation System: Application to Industrial Diagnosis
(International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 12/2018)In this paper, we propose a global architecture of a recommender tool, which represents a part of an existing collaborative platform. This tool provides diagnostic documents for industrial operators. The recommendation ... -
Classification-based Deep Neural Network Architecture for Collaborative Filtering Recommender Systems
(International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 03/2020)This paper proposes a scalable and original classification-based deep neural architecture. Its collaborative filtering approach can be generalized to most of the existing recommender systems, since it just operates on the ... -
Comprehensive Evaluation of Matrix Factorization Models for Collaborative Filtering Recommender Systems
(International Journal of Interactive Multimedia and Artificial Intelligence, 04/2023)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. ... -
DeepFair: Deep Learning for Improving Fairness in Recommender Systems
(International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 06/2021)The lack of bias management in Recommender Systems leads to minority groups receiving unfair recommendations. Moreover, the trade-off between equity and precision makes it difficult to obtain recommendations that meet both ... -
Empirical Comparison of Graph-based Recommendation Engines for an Apps Ecosystem
(International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 03/2015)Recommendation engines (RE) are becoming highly popular, e.g., in the area of e-commerce. A RE offers new items (products or content) to users based on their profile and historical data. The most popular algorithms used ... -
Social Relations and Methods in Recommender Systems: A Systematic Review
(International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 06/2022)With the constant growth of information, data sparsity problems, and cold start have become a complex problem in obtaining accurate recommendations. Currently, authors consider the user's historical behavior and find ... -
Testing Deep Learning Recommender Systems Models on Synthetic GAN-Generated Datasets
(International Journal of Interactive Multimedia and Artificial Intelligence, 10/2023)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 ...
