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

TL;DR

This paper presents a system for analyzing large-scale online clothing data to identify discriminative features of best-selling items, aiding recommendation and marketing strategies.

Contribution

It introduces a novel multi-component system for mining and analyzing popular clothing features from large online datasets using machine learning techniques.

Findings

Effective identification of discriminative clothing features

Insights into clothing consumption trends

Potential applications in recommendation and advertising

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.