Fashion Social Media Mining and Analysis When Fashion Meets Big Data - I, II, III

Objective
(1) Investigated brand-new aspects to enable automatically fashion commenting for consumer dressing. [1]
(2) Proposed a framework that facilitates the investigation of consumers' clothing preference in a fine-grained manner. [2]
(3) Discovered fashion trends in New York City by constructing a dataset from the New York Fashion Shows and New York street chic in order to understand likely clothing fashion trends. [3]

Papers
[1] "Netizen-Style Commenting on Fashion Photos: Dataset and Diversity Measures," Wen Hua Lin, Kuan-Ting Chen, Hung Yueh Chiang, and Winston Hsu, The Web Conference (WWW) Conference, Lyon, France, April 2018. [Paper][Dataset]
[2] "When Fashion Meets Big Data: Discriminative Mining of Best Selling Clothing Features," Kuan-Ting Chen, Jiebo Luo, World Wide Web (WWW) Conference, Perth, Australia, April 2017. [Paper]
[3] "Who are the Devils Wearing Prada in New York City?," Kuan-Ting Chen, Kezhen Chen, Peizhong Cong, Winston Hsu, Jiebo Luo, ACM Multimedia (MM), Brisbane, Australia, October 2015. [Paper]

Media News: New York Post Science News El Pais (Spanish) MIT Technology Review

Abstract
Recently, image captioning has appeared promising, which is expected to widely apply in chatbot area. Yet, "vanilla" sentences, only describing shallow appearances (e.g., types, colors), generated by current works are not satisfied netizen style resulting in lacking engagement with users. Hence, we propose Netizen Style Commenting (NSC), to generate characteristic comments to a user-contributed fashion photo. We are devoted to modulating the comments in a vivid "netizen" style which reflects the culture in a designated social community and hopes to facilitate more engagement with users. In this work, we design a novel framework that consists of three major components: (1) We construct a large-scale clothing dataset named NetiLook to discover netizen-style comments. (2) We propose three unique measures to estimate the diversity of comments. (3) We bring diversity by marrying topic models with neural networks to make up the insufficiency of conventional image captioning works.

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.

System Framework