# Leveraging Multiple Relations for Fashion Trend Forecasting Based on Social Media

arXiv: https://arxiv.org/abs/2105.03299 · HTML: https://mlsanity.com/paper/2105.03299/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 37 references that are in the map.
- First posted on arXiv 7 May 2021.

## Builds on (most cited first, 12 of 12)

- [A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction](https://mlsanity.com/paper/1704.02971/) (2017)
- [Temporal Relational Ranking for Stock Prediction](https://mlsanity.com/paper/1809.09441/) (2018)
- [Fashion Forward: Forecasting Visual Style in Fashion](https://mlsanity.com/paper/1705.06394/) (2017)
- [GeoStyle: Discovering Fashion Trends and Events](https://mlsanity.com/paper/1908.11412/) (2019)
- [Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology](https://mlsanity.com/paper/1901.04028/) (2019)
- [Knowledge Enhanced Neural Fashion Trend Forecasting](https://mlsanity.com/paper/2005.03297/) (2020)
- [Mining Fashion Outfit Composition Using An End-to-End Deep Learning Approach on Set Data](https://mlsanity.com/paper/1608.03016/) (2016)
- [StreetStyle: Exploring world-wide clothing styles from millions of photos](https://mlsanity.com/paper/1706.01869/) (2017)
- [Who, Where, and What to Wear? Extracting Fashion Knowledge from Social Media](https://mlsanity.com/paper/1908.08985/) (2019)
- [From Paris to Berlin: Discovering Fashion Style Influences Around the World](https://mlsanity.com/paper/2004.01316/) (2020)
- [Position-based Content Attention for Time Series Forecasting with Sequence-to-sequence RNNs](https://mlsanity.com/paper/1703.10089/) (2017)
- [Bilinear Supervised Hashing Based on 2D Image Features](https://mlsanity.com/paper/1901.01474/) (2019)

## Built on by (most cited first, 3 of 3)

- [Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models](https://mlsanity.com/paper/2402.03659/) (2024)
- [Diffusion Models for Generative Outfit Recommendation](https://mlsanity.com/paper/2402.17279/) (2024)
- [The multi-modal universe of fast-fashion: the Visuelle 2.0 benchmark](https://mlsanity.com/paper/2204.06972/) (2022)

## Abstract (arXiv)

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, 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 and GeoStyle 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.