New Trends in Artificial Intelligence for Recommender Systems and Collaborative Filtering-Reference-Cited by-同舟云学术

New Trends in Artificial Intelligence for Recommender Systems and Collaborative Filtering

Published:2023-07-31 Issue:15 Volume:13 Page:8845
ISSN:2076-3417
Container-title:Applied Sciences
language:en
Short-container-title:Applied Sciences

Author:

Pérez-López Diego¹,Dueñas-Lerín Jorge¹^ORCID,Ortega Fernando¹²^ORCID,González-Prieto Ángel¹³⁴^ORCID

Affiliation:

1. Knowledge Discovery and Information Systems (KNODIS) Research Group, Universidad Politénica de Madrid, 28031 Madrid, Spain

2. Departmento de Sistemas Informáticos, Universidad Politécnica de Madrid, 28040 Madrid, Spain

3. Departamento de Álgebra, Geometría y Topología, Universidad Complutense de Madrid, 28040 Madrid, Spain

4. Instituto de Ciencias Matemáticas (CSIC-UAM-UCM-UC3M), 28049 Madrid, Spain

Abstract

In recent times, recommender systems (RSs) have been attracting a lot of attention from the research community because of their groundbreaking applications [...]

Funder

Comunidad de Madrid

Madrid Government

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Link

https://www.mdpi.com/2076-3417/13/15/8845/pdf

Reference13 articles.

1. Margaris, D., Spiliotopoulos, D., and Vassilakis, C. (2021). Augmenting Black Sheep Neighbour Importance for Enhancing Rating Prediction Accuracy in Collaborative Filtering. Appl. Sci., 11.

2. Rizkallah, S., Atiya, A.F., and Shaheen, S. (2021). New Vector-Space Embeddings for Recommender Systems. Appl. Sci., 11.

3. Aciar, S.V., Fabregat, R., Jové, T., and Aciar, G. (2021). Enhancing Recommender System with Collaborative Filtering and User Experiences Filtering. Appl. Sci., 11.

4. Shafqat, W., and Byun, Y.C. (2021). Incorporating similarity measures to optimize graph convolutional neural networks for product recommendation. Appl. Sci., 11.

5. Chen, X., Chen, J., Lian, X., and Mai, W. (2022). Resolving Data Sparsity via Aggregating Graph-Based User–App–Location Association for Location Recommendations. Appl. Sci., 12.