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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Published in:	Information (Basel) Vol. 15; no. 4; p. 204
Main Authors:	Azri, Abdelghani, Haddi, Adil, Allali, Hakim
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.04.2024
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
ISSN:	2078-2489, 2078-2489
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Abstract	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.
AbstractList	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.
Audience	Academic
Author	Allali, Hakim Azri, Abdelghani Haddi, Adil
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SubjectTerms	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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Title	IUAutoTimeSVD++: A Hybrid Temporal Recommender System Integrating Item and User Features Using a Contractive Autoencoder
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