IUAutoTimeSVD++: A Hybrid Temporal Recommender System Integrating Item and User Features Using a Contractive Autoencoder

Collaborative filtering (CF), a fundamental technique in personalized Recommender Systems, operates by leveraging user–item preference interactions. Matrix factorization remains one of the most prevalent CF-based methods. However, recent advancements in deep learning have spurred the development of...

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Bibliographic Details
Published in:Information (Basel) 2024-04, Vol.15 (4), p.204
Main Authors: Azri, Abdelghani, Haddi, Adil, Allali, Hakim
Format: Article
Language:English
Subjects:
Accuracy
Collaboration
collaborative filtering
contractive autoencoder
Deep learning
Factorization
feature extraction
Graph representations
Hybrid systems
matrix factorization
Motion pictures
Movie reviews
Neural networks
Random variables
recommendation
Recommender systems
Sparsity
User behavior
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Summary:Collaborative filtering (CF), a fundamental technique in personalized Recommender Systems, operates by leveraging user–item preference interactions. Matrix factorization remains one of the most prevalent CF-based methods. However, recent advancements in deep learning have spurred the development of hybrid models, which extend matrix factorization, particularly with autoencoders, to capture nonlinear item relationships. Despite these advancements, many proposed models often neglect dynamic changes in the rating process and overlook user features. This paper introduces IUAutoTimeSVD++, a novel hybrid model that builds upon autoTimeSVD++. By incorporating item–user features into the timeSVD++ framework, the proposed model aims to address the static nature and sparsity issues inherent in existing models. Our model utilizes a contractive autoencoder (CAE) to enhance the capacity to capture a robust and stable representation of user-specific and item-specific features, accommodating temporal variations in user preferences and leveraging item characteristics. Experimental results on two public datasets demonstrate IUAutoTimeSVD++’s superiority over baseline models, affirming its effectiveness in capturing and utilizing user and item features for temporally adaptive recommendations.
ISSN:2078-2489
2078-2489
DOI:10.3390/info15040204