When Fashion Meets Big Data: Discriminative Mining of Best Selling Clothing Features

Kuan-Ting Chen; Jiebo Luo

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arXiv · 1611.03915

When Fashion Meets Big Data: Discriminative Mining of Best Selling Clothing Features

Kuan-Ting Chen·Jiebo Luo

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Abstract

With the prevalence of e-commence websites and the ease of online shopping, consumers are embracing huge amounts of various options in products. Undeniably, shopping is one of the most essential activities in our society and studying consumer's shopping behavior is important for the industry as well as sociology and psychology. Indisputable, one of the most popular e-commerce categories is clothing business. There arises the needs for analysis of popular and attractive clothing features which could further boost many emerging applications, such as clothing recommendation and advertising. In this work, we design a novel system that consists of three major components: 1) exploring and organizing a large-scale clothing dataset from a online shopping website, 2) pruning and extracting images of best-selling products in clothing item data and user transaction history, and 3) utilizing a machine learning based approach to discovering fine-grained clothing attributes as the representative and discriminative characteristics of popular clothing style elements. Through the experiments over a large-scale online clothing shopping dataset, we demonstrate the effectiveness of our proposed system, and obtain useful insights on clothing consumption trends and profitable clothing features.

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Kuan-Ting Chen, Jiebo Luo. 2017-02-22. When Fashion Meets Big Data: Discriminative Mining of Best Selling Clothing Features. https://arxiv.org/abs/1611.03915

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