An Enhanced Multi-Modal Recommendation Based on Alternate Training With Knowledge Graph Representation
Deep network recommendation is a cutting-edge topic in current recommendation system research, which as a combination of recommendation systems and deep learning theory can effectively improve recommendation accuracy. In a real recommendation scenario, all the effective information in a data set sho...
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doaj-07380b0e83a746f0beb85f29b3ad75232021-03-30T03:41:55ZengIEEEIEEE Access2169-35362020-01-01821301221302610.1109/ACCESS.2020.30393889264240An Enhanced Multi-Modal Recommendation Based on Alternate Training With Knowledge Graph RepresentationYuequn Wang0https://orcid.org/0000-0001-7154-0539Liyan Dong1https://orcid.org/0000-0001-7491-5893Hao Zhang2https://orcid.org/0000-0002-2058-7123Xintao Ma3Yongli Li4Minghui Sun5https://orcid.org/0000-0002-1809-8187College of Computer Science and Technology, Jilin University, Changchun, ChinaCollege of Computer Science and Technology, Jilin University, Changchun, ChinaCollege of Computer Science and Technology, Jilin University, Changchun, ChinaCollege of Computer Science and Technology, Jilin University, Changchun, ChinaSchool of Information Science and Technology, Northeast Normal University, Changchun, ChinaCollege of Computer Science and Technology, Jilin University, Changchun, ChinaDeep network recommendation is a cutting-edge topic in current recommendation system research, which as a combination of recommendation systems and deep learning theory can effectively improve recommendation accuracy. In a real recommendation scenario, all the effective information in a data set should be extracted, both explicit and implicit, because the comprehensive degree of information is proportional to the recommendation performance. This article proposes an enhanced multi-modal recommendation based on alternate training with knowledge graph representation (SI-MKR) based on the MKR deep learning recommendation model. Our framework is an enhanced recommendation system based on knowledge graph representation, using valuable external knowledge as multi-modal information. The SI-MKR model solves the problem of ignoring the diversity of data types in the multi-modal knowledge-based recommendation system, which adds user and item attribute information from a knowledge graph as an enhancement recommendation multi-tasking training. By analysing the content of the item and user attributes, the SI-MKR model classifies the attributes of the items and users, processes the text type attributes and multi-value type attributes separately for feature extraction, and other types of attributes are used as inputs to the knowledge graph embedding unit. In addition, the knowledge graph data form a triplet unit, thus continuing the knowledge graph data training process. The feature extraction unit of the knowledge graph and the recommended unit are connected through the cross-compression unit for alternate training. During the deep learning framework training process, the recommendation system's item has a potential correlation with the head entity in the knowledge graph which embodies the idea of multi-tasking. Through extensive experiments on real-world datasets, we demonstrate that SI-MKR achieves substantial gains in movie recommendation over advanced model baselines. Even user-item interactions are sparse, SI-MKR maintains better performance than the MKR model.https://ieeexplore.ieee.org/document/9264240/Feature learninggraph representationsknowledge graphrecommendation system |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yuequn Wang Liyan Dong Hao Zhang Xintao Ma Yongli Li Minghui Sun |
spellingShingle |
Yuequn Wang Liyan Dong Hao Zhang Xintao Ma Yongli Li Minghui Sun An Enhanced Multi-Modal Recommendation Based on Alternate Training With Knowledge Graph Representation IEEE Access Feature learning graph representations knowledge graph recommendation system |
author_facet |
Yuequn Wang Liyan Dong Hao Zhang Xintao Ma Yongli Li Minghui Sun |
author_sort |
Yuequn Wang |
title |
An Enhanced Multi-Modal Recommendation Based on Alternate Training With Knowledge Graph Representation |
title_short |
An Enhanced Multi-Modal Recommendation Based on Alternate Training With Knowledge Graph Representation |
title_full |
An Enhanced Multi-Modal Recommendation Based on Alternate Training With Knowledge Graph Representation |
title_fullStr |
An Enhanced Multi-Modal Recommendation Based on Alternate Training With Knowledge Graph Representation |
title_full_unstemmed |
An Enhanced Multi-Modal Recommendation Based on Alternate Training With Knowledge Graph Representation |
title_sort |
enhanced multi-modal recommendation based on alternate training with knowledge graph representation |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Deep network recommendation is a cutting-edge topic in current recommendation system research, which as a combination of recommendation systems and deep learning theory can effectively improve recommendation accuracy. In a real recommendation scenario, all the effective information in a data set should be extracted, both explicit and implicit, because the comprehensive degree of information is proportional to the recommendation performance. This article proposes an enhanced multi-modal recommendation based on alternate training with knowledge graph representation (SI-MKR) based on the MKR deep learning recommendation model. Our framework is an enhanced recommendation system based on knowledge graph representation, using valuable external knowledge as multi-modal information. The SI-MKR model solves the problem of ignoring the diversity of data types in the multi-modal knowledge-based recommendation system, which adds user and item attribute information from a knowledge graph as an enhancement recommendation multi-tasking training. By analysing the content of the item and user attributes, the SI-MKR model classifies the attributes of the items and users, processes the text type attributes and multi-value type attributes separately for feature extraction, and other types of attributes are used as inputs to the knowledge graph embedding unit. In addition, the knowledge graph data form a triplet unit, thus continuing the knowledge graph data training process. The feature extraction unit of the knowledge graph and the recommended unit are connected through the cross-compression unit for alternate training. During the deep learning framework training process, the recommendation system's item has a potential correlation with the head entity in the knowledge graph which embodies the idea of multi-tasking. Through extensive experiments on real-world datasets, we demonstrate that SI-MKR achieves substantial gains in movie recommendation over advanced model baselines. Even user-item interactions are sparse, SI-MKR maintains better performance than the MKR model. |
topic |
Feature learning graph representations knowledge graph recommendation system |
url |
https://ieeexplore.ieee.org/document/9264240/ |
work_keys_str_mv |
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