Efficient Visual-Aware Fashion Recommendation Using Compressed Node Features and Graph-Based Learning
In fashion e-commerce, predicting item compatibility using visual features remains a significant challenge. Current recommendation systems often struggle to incorporate high-dimensional visual data into graph-based learning models effectively. This limitation presents a substantial opportunity to en...
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MDPI AG
2024-09-01
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| Online Access: | https://www.mdpi.com/2504-4990/6/3/104 |
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| author | Umar Subhan Malhi Junfeng Zhou Abdur Rasool Shahbaz Siddeeq |
| author_facet | Umar Subhan Malhi Junfeng Zhou Abdur Rasool Shahbaz Siddeeq |
| author_sort | Umar Subhan Malhi |
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| description | In fashion e-commerce, predicting item compatibility using visual features remains a significant challenge. Current recommendation systems often struggle to incorporate high-dimensional visual data into graph-based learning models effectively. This limitation presents a substantial opportunity to enhance the precision and effectiveness of fashion recommendations. In this paper, we present the Visual-aware Graph Convolutional Network (VAGCN). This novel framework helps improve how visual features can be incorporated into graph-based learning systems for fashion item compatibility predictions. The VAGCN framework employs a deep-stacked autoencoder to convert the input image’s high-dimensional raw CNN visual features into more manageable low-dimensional representations. In addition to improving feature representation, the GCN can also reason more intelligently about predictions, which would not be possible without this compression. The GCN encoder processes nodes in the graph to capture structural and feature correlation. Following the GCN encoder, the refined embeddings are input to a multi-layer perceptron (MLP) to calculate compatibility scores. The approach extends to using neighborhood information only during the testing phase to help with training efficiency and generalizability in practical scenarios, a key characteristic of our model. By leveraging its ability to capture latent visual features and neighborhood-based learning, VAGCN thoroughly investigates item compatibility across various categories. This method significantly improves predictive accuracy, consistently outperforming existing benchmarks. These contributions tackle significant scalability and computational efficiency challenges, showcasing the potential transformation of recommendation systems through enhanced feature representation, paving the way for further innovations in the fashion domain. |
| format | Article |
| id | doaj-art-ebbfe94ab6304116a578a54aa9cd6b6a |
| institution | OA Journals |
| issn | 2504-4990 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
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| series | Machine Learning and Knowledge Extraction |
| spelling | doaj-art-ebbfe94ab6304116a578a54aa9cd6b6a2025-08-20T01:55:38ZengMDPI AGMachine Learning and Knowledge Extraction2504-49902024-09-01632111212910.3390/make6030104Efficient Visual-Aware Fashion Recommendation Using Compressed Node Features and Graph-Based LearningUmar Subhan Malhi0Junfeng Zhou1Abdur Rasool2Shahbaz Siddeeq3School of Computer Science and Technology, Donghua University, Songjiang, Shanghai 200051, ChinaSchool of Computer Science and Technology, Donghua University, Songjiang, Shanghai 200051, ChinaDepartment of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI 96822, USAFaculty of Information Technology and Communication Sciences, Tampere University, 33100 Tampere, FinlandIn fashion e-commerce, predicting item compatibility using visual features remains a significant challenge. Current recommendation systems often struggle to incorporate high-dimensional visual data into graph-based learning models effectively. This limitation presents a substantial opportunity to enhance the precision and effectiveness of fashion recommendations. In this paper, we present the Visual-aware Graph Convolutional Network (VAGCN). This novel framework helps improve how visual features can be incorporated into graph-based learning systems for fashion item compatibility predictions. The VAGCN framework employs a deep-stacked autoencoder to convert the input image’s high-dimensional raw CNN visual features into more manageable low-dimensional representations. In addition to improving feature representation, the GCN can also reason more intelligently about predictions, which would not be possible without this compression. The GCN encoder processes nodes in the graph to capture structural and feature correlation. Following the GCN encoder, the refined embeddings are input to a multi-layer perceptron (MLP) to calculate compatibility scores. The approach extends to using neighborhood information only during the testing phase to help with training efficiency and generalizability in practical scenarios, a key characteristic of our model. By leveraging its ability to capture latent visual features and neighborhood-based learning, VAGCN thoroughly investigates item compatibility across various categories. This method significantly improves predictive accuracy, consistently outperforming existing benchmarks. These contributions tackle significant scalability and computational efficiency challenges, showcasing the potential transformation of recommendation systems through enhanced feature representation, paving the way for further innovations in the fashion domain.https://www.mdpi.com/2504-4990/6/3/104fashion recommendation systemsrepresentation learninggraph convolutional networks |
| spellingShingle | Umar Subhan Malhi Junfeng Zhou Abdur Rasool Shahbaz Siddeeq Efficient Visual-Aware Fashion Recommendation Using Compressed Node Features and Graph-Based Learning Machine Learning and Knowledge Extraction fashion recommendation systems representation learning graph convolutional networks |
| title | Efficient Visual-Aware Fashion Recommendation Using Compressed Node Features and Graph-Based Learning |
| title_full | Efficient Visual-Aware Fashion Recommendation Using Compressed Node Features and Graph-Based Learning |
| title_fullStr | Efficient Visual-Aware Fashion Recommendation Using Compressed Node Features and Graph-Based Learning |
| title_full_unstemmed | Efficient Visual-Aware Fashion Recommendation Using Compressed Node Features and Graph-Based Learning |
| title_short | Efficient Visual-Aware Fashion Recommendation Using Compressed Node Features and Graph-Based Learning |
| title_sort | efficient visual aware fashion recommendation using compressed node features and graph based learning |
| topic | fashion recommendation systems representation learning graph convolutional networks |
| url | https://www.mdpi.com/2504-4990/6/3/104 |
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