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Research and implementation of a trend prediction model based on trend similarity for the changing trends of fashion elements in clothing

Authors:

Shunguang ZhanAuthors Info & Claims

EITCE '22: Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering

Pages 1460 - 1466

https://doi.org/10.1145/3573428.3573685

Published: 15 March 2023 Publication History

Abstract

Trend forecasting of clothing fashion elements is an important guide for product development and sales of garment companies. Existing work can only capture simple changing trend laws and patterns of mutual influence between trends but cannot give effective and practical guidance on the trend changes of clothing fashion elements. This paper uses user information to group rich fashion elements in a more accurate and meaningful way to predict the trend of future trends in fashion elements. By comparing the similarity between the recent trend changes and the historical trend information, we continuously evaluate the next change trend information from the similar historical trend information, learn the laws and patterns of clothing fashion element change trends and predict the future trend change direction. Our experiments show that the model proposed in this paper can effectively capture the changing laws of clothing fashion elements and the patterns that affect each other to predict the changing trends. Compared with the baseline method, the model has the best performance in MAE and MAPE indicators.

References

[1]

Katherine Cameron, Sammy Al-moukadem, Mark Boutilier, Mark Hamilton, Allison Jeffcoat, Matt Lee, Mai Martina, and Marie Price. 2021. Zara in China: Fashionably Fast.

[2]

Ajibolu Oyinkansola Gladys and Akinola Solomon Olalekan. 2021. A machine learning model for predicting colour trends in the textile fashion industry in south-west Nigeria.

[3]

Ali Fallah Tehrani and Diane Ahrens. 2015. Improved Forecasting and Purchasing of Fashion Products based on the Use of Big Data Techniques.

[4]

Zhendi Wang and Xiaogang Liu. 2021. Statistical Analysis and Big Data Based Intelligent Fashion Prediction Model. In 2021 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS), IEEE, 878–882.

[5]

KuanTing Chen, Kezhen Chen, Peizhong Cong, Winston H. Hsu, and Jiebo Luo. 2015. Who are the devils wearing prada in new york city? In Proceedings of the 23rd ACM international conference on Multimedia, 177–180.

Digital Library

[6]

Ziad Al-Halah and Kristen Grauman. 2020. Modeling Fashion Influence from Photos. IEEE Transactions on Multimedia 23, (2020), 4143–4157.

[7]

Utkarsh Mall, Kevin Matzen, Bharath Hariharan, Noah Snavely, and Kavita Bala. 2019. Geostyle: Discovering fashion trends and events. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 411–420.

[8]

Ziwei Liu, Ping Luo, Shi Qiu, Xiaogang Wang, and Xiaoou Tang. 2016. DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1096–1104.

[9]

Weijiang Yu, Xiaodan Liang, Ke Gong, Chenhan Jiang, Nong Xiao, and Liang Lin. 2019. Layout-graph reasoning for fashion landmark detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2937–2945.

[10]

Zhanghui Kuang, Yiming Gao, Guanbin Li, Ping Luo, Yimin Chen, Liang Lin, and Wayne Zhang. 2019. Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramid. arXiv:1908.11754 [cs] (August 2019).

[11]

Si Liu, Zheng Song, Meng Wang, Changsheng Xu, Hanqing Lu, and Shuicheng Yan. 2012. Street-to-shop: cross-scenario clothing retrieval via parts alignment and auxiliary set. In Proceedings of the 20th ACM international conference on Multimedia (MM ’12), Association for Computing Machinery, New York, NY, USA, 1335–1336.

Digital Library

[12]

Yuncheng Li, LiangLiang Cao, Jiang Zhu, and Jiebo Luo. 2017. Mining Fashion Outfit Composition Using An End-to-End Deep Learning Approach on Set Data. IEEE Trans. Multimedia 19, 8 (August 2017), 1946–1955.

Digital Library

[13]

Na Zheng, Xuemeng Song, Qingying Niu, Xue Dong, Yibing Zhan, and Liqiang Nie. 2021. Collocation and Try-on Network: Whether an Outfit is Compatible. In Proceedings of the 29th ACM International Conference on Multimedia. Association for Computing Machinery, New York, NY, USA, 309–317.

Digital Library

[14]

Xingchen Li, Xiang Wang, Xiangnan He, Long Chen, Jun Xiao, and Tat-Seng Chua. 2020. Hierarchical Fashion Graph Network for Personalized Outfit Recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, New York, NY, USA, 159–168.

Digital Library

[15]

Al-Halah, R. Stiefelhagen, and K. Grauman. 2017. Fashion Forward: Forecasting Visual Style in Fashion. In 2017 IEEE International Conference on Computer Vision (ICCV), 388–397.

[16]

Youngseung Jeon, Seungwan Jin, and Kyungsik Han. 2021. FANCY: Human-centered, Deep Learning-based Framework for Fashion Style Analysis. In Proceedings of the Web Conference 2021 (WWW ’21), Association for Computing Machinery, New York, NY, USA, 2367–2378.

Digital Library

[17]

Youngseung Jeon, Seungwan Jin, Bogoan Kim, and Kyungsik Han. 2020. FashionQ: An Interactive Tool for Analyzing Fashion Style Trend with Quantitative Criteria. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (CHI EA ’20), Association for Computing Machinery, New York, NY, USA, 1–7.

Digital Library

[18]

Shintami C. Hidayati, Kai-Lung Hua, Wen-Huang Cheng, and Shih-Wei Sun. 2014. What are the Fashion Trends in New York? In Proceedings of the 22nd ACM international conference on Multimedia (MM ’14), Association for Computing Machinery, New York, NY, USA, 197–200.

Digital Library

[19]

Yunshan Ma, Yujuan Ding, Xun Yang, Lizi Liao, Wai Keung Wong, and Tat-Seng Chua. 2020. Knowledge Enhanced Neural Fashion Trend Forecasting. In Proceedings of the 2020 International Conference on Multimedia Retrieval (ICMR ’20), Association for Computing Machinery, New York, NY, USA, 82–90.

Digital Library

[20]

Saeed Khaki, Lizhi Wang, and Sotirios V. Archontoulis. 2020. A cnn-rnn framework for crop yield prediction. Frontiers in Plant Science 10, (2020), 1750.

[21]

Mathias Berglund, Tapani Raiko, Mikko Honkala, Leo Kärkkäinen, Akos Vetek, and Juha Karhunen. 2015. Bidirectional recurrent neural networks as generative models-reconstructing gaps in time series. arXiv preprint arXiv:1504.01575 (2015).

[22]

Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. arXiv:1406.1078 [cs, stat] (September 2014).

[23]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long Short-Term Memory. Neural Computation 9, 8 (November 1997), 1735–1780.

Digital Library

Cited By

Jiju AAnilkumar AGokul Krishnan KGeorge JPrasanth R(2024)Review on Fashion Trend Analysis and Forecasting Techniques - A Machine Learning Approach2024 5th International Conference on Innovative Trends in Information Technology (ICITIIT)10.1109/ICITIIT61487.2024.10580247(1-6)Online publication date: 15-Mar-2024
https://doi.org/10.1109/ICITIIT61487.2024.10580247

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EITCE '22: Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering

October 2022

1999 pages

ISBN:9781450397148

DOI:10.1145/3573428

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

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Published: 15 March 2023

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EITCE 2022

EITCE 2022: 2022 6th International Conference on Electronic Information Technology and Computer Engineering

October 21 - 23, 2022

Xiamen, China

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Overall Acceptance Rate 508 of 972 submissions, 52%

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Cited By

Jiju AAnilkumar AGokul Krishnan KGeorge JPrasanth R(2024)Review on Fashion Trend Analysis and Forecasting Techniques - A Machine Learning Approach2024 5th International Conference on Innovative Trends in Information Technology (ICITIIT)10.1109/ICITIIT61487.2024.10580247(1-6)Online publication date: 15-Mar-2024
https://doi.org/10.1109/ICITIIT61487.2024.10580247

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