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

Visual Intelligence & Social Multimedia Analytics Lab

When Fashion Meets Big Data: Discriminative Mining of Best Selling

Clothing Features

Kuan-Ting Chen and Jiebo Luo

Cognitive Computing Track 2017 World Wide Web Conference

Motivation• Shopping is one of the most essential behaviors

- E-commerce sales will reach $2.352 trillion in 2017 and increase nearly 73% to $4.058 trillion in 2020 [eMarketers]

• One of the most popular e-commerce categories is clothing business- The most popular e-commerce categories growing in

prominence for online shopping includes clothing, and airline and hotel reservations [Nielsen]

• Benefits of determinizing best selling clothing items- Boosting many emerging applications such as clothing

recommendation and advertising by clothing brand association

1. eMarketer analyses and organizes data from over 4,000 global sources, which provides online ad trend in many aspects. http://www.emarketer.com2. Nielsen is a famous internet marketing research company that study consumer data more than 100 countries to online trend and behavior.

http://www.nielsen.com/

Related Work: Consumer Reviews

Customer Reviews

The consumers’ reviews might be noisy, ambiguously and inconsistent

• M. Hu and B. Liu. Mining and summarizing customer reviews. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'04, pages 168‐177, New York, NY, USA, 2004. ACM.

• M. Hu and B. Liu. Mining opinion features in customer reviews. In Proceedings of the 19th National Conference on Artificial Intelligence, AAAI'04, pages 755‐760. AAAI Press, 2004.• V. Y. Karkare and S. R. Gupta. Product evaluation using mining and rating opinions of product features. In Electronic Systems, Signal Processing and Computing Technologies {ICESC), 2014

International Conference on, pages 382‐385, Jan 2014. • R. Kumar V; K. Raghuveer. Web user opinion analysis for product features extraction. In International Journal of Web & Semantic Technology, volume 3, pages 382‐385, Nov 2012.

Related Work: Automated Analyses

Clothing parsing Fashion trend analysis Clothing retrieval

[Kiapour et al. ICCV 2015][Liu et al. MM 2012]

[Hidayati MM 2014][*Chen MM 2015]

[Bourdev et al. ICCV 2011][Liu et al. TMM 2014][Liu et al. CVPR 2013]

[Nguyen et al. MM 2012][Liu et al. CVPR 2016]

Main Idea: Cross Mining

Shopping Transactions

Clothing Descriptions

• Consumers ID• # purchases• Date

• Round Neckline• Grey• Dress

GreyDress

Round NecklineDress

GreyRound Neckline

Contributions

• Proposing a framework that facilitates the investigation of consumers' clothing preference in a fine-grained manner

• Conducting empirical analysis of a large-scale online shopping dataset collected between June 2014 and June 2015

• Implementing an effective and efficient method for pruning noisy images in the online shopping dataset

• Mining attractive and profitable clothing features on a large volume of clothing data with customers' transaction history

• Discovering significant insights using the proposed frame-work from real-world large-scale data

System Framework

Datasets - Clothing products on Taobao• Taobao is one of the largest online shopping websites in China,

similar to eBay and Amazon• The item data table (about 0.5-million product information)

- item id: a unique id for each product- cat id: the category id the product belongs to- name arr: an array that contains the name of this product- img data: image information of each product

• The item image (images for each product in the item data table)• The user history table (around 10-million user transaction data)

- user id: user’s unique id in one transaction- item : id of specific product the user purchases in this transaction- date: time information of this transaction.

The Number of Transaction in Each Month(2014.06 — 2015.06)

• There are more # transactions on- October — December (11/11, equiv. Black Friday)- March — May

Datasets- New York Fashion Show

Year #designer #images Year #designer #images

2014 27 3914 2015 20 4000

Methodology- Noisy Image Pruning

VGG‐16 deep neural network implemented using Tensorflowwith an accuracy of 75.5%, a recall of 70%, and a precision of 78.6%

Methodology- Clothing Feature Learning

Methodology- Profitable Clothing Feature Mining

Experimental Results

The classic/attractive, popular and unpopular clothing features in spring and winter

Visualization of the frequency of popular clothing features

Unique clothing styles at the time Particular clothing outfits

Contributions

• Proposing a framework that facilitates the investigation of consumers' clothing preference in a fine-grained manner

• Conducting empirical analysis of a large-scale online shopping dataset collected between June 2014 and June 2015

• Implementing an effective and efficient method for pruning noisy images in the online shopping dataset

• Mining attractive and profitable clothing features on a large volume of clothing data with customers' transaction history

• Discovering significant insights using the proposed frame-work from real-world large-scale data

Questions?

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

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