Enhanced E-commerce Recommender System Based on Deep Learning and Ensemble Approaches-Reference-Cited by-同舟云学术

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Enhanced E-commerce Recommender System Based on Deep Learning and Ensemble Approaches

Published:2024-04-18 Issue: Volume:5 Page:1-8
ISSN:
Container-title:Proceedings of the 7th International Conference on Networking, Intelligent Systems and Security
language:
Short-container-title:

Author:

Karabila Ikram¹^ORCID,Darraz Nossayba¹^ORCID,El-Ansari Anas²^ORCID,Alami Nabil³^ORCID,El mallahi Mostafa¹^ORCID

Affiliation:

1. IPI laboratory mathematics and computer science department, Sidi mohamed ben abdellah university High Normal School, Morocco

2. Masi laboratory computer science department, Mohammed first university Polydisciplinary Faculty, Morocco

3. Masi laboratory, Mohammed first university Higher School of Technology, Morocco

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3659677.3659747

Reference20 articles.

1. A Survey of State-of-the-art: Deep Learning Methods on Recommender System

2. An attention-based deep learning method for solving the cold-start and sparsity issues of recommender systems

3. BERT-enhanced sentiment analysis for personalized e-commerce recommendations;Karabila I.;Multimedia Tools and Applications,2023

4. [4] Koren, Y., Rendle, S., & Bell, R. (2021). Advances in collaborative filtering. Recommender systems handbook, 91-142.

5. [5] Lops, P., De Gemmis, M., & Semeraro, G. (2011). Content-based recommender systems: State of the art and trends. Recommender systems handbook, 73-105.

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