Recommendation Model by Integrating Knowledge Graph and Image Features

CHEN Qiaosong; GUO Aodong; DU Yulu; ZHANG Yiwen; ZHU Yue

doi:10.11999/JEIT210230

Volume 44 Issue 5

May 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(5): 1723-1733

CHEN Qiaosong, GUO Aodong, DU Yulu, ZHANG Yiwen, ZHU Yue. Recommendation Model by Integrating Knowledge Graph and Image Features[J]. Journal of Electronics & Information Technology, 2022, 44(5): 1723-1733. doi: 10.11999/JEIT210230

Citation:

CHEN Qiaosong, GUO Aodong, DU Yulu, ZHANG Yiwen, ZHU Yue. Recommendation Model by Integrating Knowledge Graph and Image Features[J]. Journal of Electronics & Information Technology, 2022, 44(5): 1723-1733. doi: 10.11999/JEIT210230

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Recommendation Model by Integrating Knowledge Graph and Image Features

doi: 10.11999/JEIT210230

1.
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
College of Information Engineering, Anhui Xinhua University, Hefei 230000, China
3.
School of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China

Funds: The Social Science Project of Chongqing University of Posts and Telecommunications (K2021-114)

Received Date: 2021-03-22
Accepted Date: 2022-01-05
Rev Recd Date: 2022-01-03

Available Online: 2022-01-27

Publish Date: 2022-05-25

Abstract

Abstract

At present, the study of knowledge graph focuses mainly on information retrieval, natural language understanding and other fields. Integrating knowledge graph with recommendation system has been concerned by scholars in the field. In order to mine the rich information ignored in knowledge graph, the knowledge graph is extended to multimodal and a recommendation model that incorporates Knowledge Graph with Image (KG-I) features is proposed. Different from other recommendation algorithms, visual embedding, knowledge embedding and structure embedding are combined to capture implicit feedback between user-items. The Deep Walk is used to capture the spatial structure and the ideal of RippleNet to retain the semantic features of knowledge graph, and the effect of images on preference is considered to integrate information. Compared with other models on the real data set, the influence of various features is studied, and the performance of different sparsity data is analyzed. The results show that the personalized recommendation model based on knowledge graph and image features outperforms other algorithms and the data sparsity can be alleviated.
- Image processing,
- Knowledge graph,
- Data fusion,
- Multimodality

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References(24)

References

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