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Personalized Clothing Recommendation Based on User Emotional Analysis
Joint Authors
Su, Xueping
Gao, Meng
Ren, Jie
Li, Yunhong
Rätsch, Matthias
Source
Discrete Dynamics in Nature and Society
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-03-05
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
With the continuous development of economy, consumers pay more attention to the demand for personalization clothing.
However, the recommendation quality of the existing clothing recommendation system is not enough to meet the user’s needs.
When browsing online clothing, facial expression is the salient information to understand the user’s preference.
In this paper, we propose a novel method to automatically personalize clothing recommendation based on user emotional analysis.
Firstly, the facial expression is classified by multiclass SVM.
Next, the user’s multi-interest value is calculated using expression intensity that is obtained by hybrid RCNN.
Finally, the multi-interest value is fused to carry out personalized recommendation.
The experimental results show that the proposed method achieves a significant improvement over other algorithms.
American Psychological Association (APA)
Su, Xueping& Gao, Meng& Ren, Jie& Li, Yunhong& Rätsch, Matthias. 2020. Personalized Clothing Recommendation Based on User Emotional Analysis. Discrete Dynamics in Nature and Society،Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1153453
Modern Language Association (MLA)
Su, Xueping…[et al.]. Personalized Clothing Recommendation Based on User Emotional Analysis. Discrete Dynamics in Nature and Society No. 2020 (2020), pp.1-8.
https://search.emarefa.net/detail/BIM-1153453
American Medical Association (AMA)
Su, Xueping& Gao, Meng& Ren, Jie& Li, Yunhong& Rätsch, Matthias. Personalized Clothing Recommendation Based on User Emotional Analysis. Discrete Dynamics in Nature and Society. 2020. Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1153453
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1153453