Publication Type

Journal Article

Version

publishedVersion

Publication Date

5-2021

Abstract

—Fashion trend forecasting is of great research significance in providing useful suggestions for both fashion companies and fashion lovers. Although various studies have been devoted to tackling this challenging task, they only studied limited fashion elements with highly seasonal or simple patterns, which could hardly reveal the real complex fashion trends. Moreover, the mainstream solutions for this task are still statistical-based and solely focus on time-series data modeling, which limit the forecast accuracy. Towards insightful fashion trend forecasting, previous work [1] proposed to analyze more fine-grained fashion elements which can informatively reveal fashion trends. Specifically, it focused on detailed fashion element trend forecasting for specific user groups based on social media data. In addition, it proposed a neural network-based method, namely KERN, to address the problem of fashion trend modeling and forecasting. In this work, to extend the previous work [1], we propose an improved model named Relation Enhanced Attention Recurrent (REAR) network. Compared to KERN, the REAR model leverages not only the relations among fashion elements, but also those among user groups, thus capturing more types of correlations among various fashion trends. To further improve the performance of long-range trend forecasting, the REAR method devises a sliding temporal attention mechanism, which is able to capture temporal patterns on future horizons better. Extensive experiments and more analysis have been conducted on the FIT [1] and GeoStyle [2] datasets to evaluate the performance of REAR. Experimental and analytical results demonstrate the effectiveness of the proposed REAR model in fashion trend forecasting, which also show the improvement of REAR compared to the KERN.

Keywords

Fashion Trend Forecasting; Time Series Forecasting; Fashion Analysis; Social Media

Discipline

Information Security

Research Areas

Data Science and Engineering; Intelligent Systems and Optimization

Publication

IEEE Transactions on Multimedia

Volume

24

First Page

2287

Last Page

2299

ISSN

1520-9210

Identifier

10.1109/TMM.2021.3078907

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

LARC and authors

Additional URL

https://doi.org/10.1109/TMM.2021.3078907

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