An Ensemble Model for Combining Deep Matrix Factorization and Image-Based Recommendation Systems

Recommender systems are widely used in many domains, especially in E-commerce. It can be used for attracting users by recommending appropriate products to them. There are many techniques in recommendation systems which can predict rating scores to recommend next products. In this work, we propose an...

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Bibliographic Details
Published in:SN computer science 2024-06, Vol.5 (6), p.674, Article 674
Main Authors: Bao, Le Huynh Quoc, Khoa, Huynh Huu Bao, Thai-Nghe, Nguyen
Format: Article
Language:English
Subjects:
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Data Structures and Information Theory
Datasets
Electronic commerce
Factorization
Feature extraction
Image enhancement
Information Systems and Communication Service
Intelligent Systems and Applications
Motion pictures
Neural networks
Original Research
Pattern Recognition and Graphics
Recommender systems
Social networks
Software Engineering/Programming and Operating Systems
Vision
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Summary:Recommender systems are widely used in many domains, especially in E-commerce. It can be used for attracting users by recommending appropriate products to them. There are many techniques in recommendation systems which can predict rating scores to recommend next products. In this work, we propose an ensemble model for combining Image-based recommendation and Deep Matrix factorization. Specifically, in the proposed model, we have utilized the pre-trained deep learning models (e.g., the VGG16) to extract the image features. Next, based on the image features, we compute similarities between the products to generate recommendations. We integrated the Deep Matrix Factorization model to predict the ratings between users and items into the Image-based recommendation to enhance the effectiveness of the recommendation model. Experimental results on public data sets show that the approach can give good recommendations at more than 90% of accuracy.
ISSN:2661-8907
2662-995X
2661-8907
DOI:10.1007/s42979-024-02978-z