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Based recommender systems: a survey of approaches, challenges and future perspectives
To cluster or not to cluster: the impact of clustering on the performance of aspect-based collaborative filtering
SM Al-Ghuribi, SAM Noah, MA Mohammed… - IEEE …, 2023 - ieeexplore.ieee.org
Collaborative filtering (CF) is one of the most widely utilised approaches in recommendation
techniques. It suggests items to users based on the ratings of other users who share their …
techniques. It suggests items to users based on the ratings of other users who share their …
Cross-modal contrastive learning for aspect-based recommendation
Abstract Knowledge-enhanced recommender systems with aspects have improved
recommendation performance by better profiling user preferences. Existing models can be …
recommendation performance by better profiling user preferences. Existing models can be …
Aspect-level item recommendation based on user reviews with variational autoencoders
W Ou, VN Huynh - Information Sciences, 2024 - Elsevier
In this paper we propose an aspect-based recommendation model based on variational
autoencoders, that provides not only coarse predictions about what items users may like, but …
autoencoders, that provides not only coarse predictions about what items users may like, but …
Sentiment-enhanced Neural Collaborative Filtering Models Using Explicit User Preferences
C Dursun, A Ozcan - 2023 5th International Congress on …, 2023 - ieeexplore.ieee.org
The integration of recommender systems contributes to the tourism industry as it provides
tailored recommendations to users, assisting them in discovering and selecting the most …
tailored recommendations to users, assisting them in discovering and selecting the most …
[PDF][PDF] To Cluster or Not to Cluster: The Impact of Clustering on the Performance of Aspect-Based Collaborative Filtering
MA MOHAMMED, SN QASEM, BAH MURSHED - academia.edu
Collaborative filtering (CF) is one of the most widely utilised approaches in recommendation
techniques. It suggests items to users based on the ratings of other users who share their …
techniques. It suggests items to users based on the ratings of other users who share their …