A Comparative Study of Sentiment-Aware Collaborative Filtering Algorithms for Arabic Recommendation Systems
The rapid proliferation of online information necessitates efficient Recommendation Systems (RSs) to assist users in discovering relevant content. While English-language RSs have received significant attention, research on Arabic RSs remains limited despite the increasing demand for Arabic digital c...
Saved in:
| Main Authors: | Sumaia Mohammed Al-Ghuribi, Shahrul Azman Mohd Noah, Mawal A. Mohammed, Neeraj Tiwary, Nur Izyan Yasmin Saat |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10741193/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Combining review elements for modelling various multi-criteria collaborative recommendation models
by: Sumaia Mohammed AL-Ghuribi, et al.
Published: (2025-07-01) -
Customers' sentiment on food delivery services: An Arabic text mining approach
by: Dheya Mustafa, et al.
Published: (2024-11-01) -
A Hybrid Method of Linguistic and Statistical Features for Arabic Sentiment Analysis
by: Ahmed Sabah AL-Jumaili
Published: (2020-03-01) -
ArabSis: Arabic Corpus Sentiment Analysis
by: Ziad Doughan, et al.
Published: (2025-01-01) -
A Survey of Deep Learning Techniques for Arabic Aspect-Based Sentiment Analysis
by: Dalal Alqusair, et al.
Published: (2025-01-01)