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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
Visual Intelligence & Social Multimedia Analytics Lab
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
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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/
Visual Intelligence & Social Multimedia Analytics Lab
Related Work: Consumer Reviews
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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.
Visual Intelligence & Social Multimedia Analytics Lab
Related Work: Automated Analyses
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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]
Visual Intelligence & Social Multimedia Analytics Lab
Main Idea: Cross Mining
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Shopping Transactions
Clothing Descriptions
• Consumers ID• # purchases• Date
• Round Neckline• Grey• Dress
GreyDress
Round NecklineDress
GreyRound Neckline
Dress
Visual Intelligence & Social Multimedia Analytics Lab
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
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Visual Intelligence & Social Multimedia Analytics Lab
System Framework
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Visual Intelligence & Social Multimedia Analytics Lab
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.
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Visual Intelligence & Social Multimedia Analytics Lab
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
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Visual Intelligence & Social Multimedia Analytics Lab
Datasets- New York Fashion Show
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Year #designer #images Year #designer #images
2014 27 3914 2015 20 4000
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Visual Intelligence & Social Multimedia Analytics Lab
Methodology- Noisy Image Pruning
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VGG‐16 deep neural network implemented using Tensorflowwith an accuracy of 75.5%, a recall of 70%, and a precision of 78.6%
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Visual Intelligence & Social Multimedia Analytics Lab
Methodology- Clothing Feature Learning
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Visual Intelligence & Social Multimedia Analytics Lab
Methodology- Profitable Clothing Feature Mining
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Visual Intelligence & Social Multimedia Analytics Lab
Experimental Results
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The classic/attractive, popular and unpopular clothing features in spring and winter
Visualization of the frequency of popular clothing features
Visual Intelligence & Social Multimedia Analytics Lab
Experimental Results
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Visual Intelligence & Social Multimedia Analytics Lab
Experimental Results
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Visual Intelligence & Social Multimedia Analytics Lab
Experimental Results
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Visual Intelligence & Social Multimedia Analytics Lab
Experimental Results
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Visual Intelligence & Social Multimedia Analytics Lab
Experimental Results
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Unique clothing styles at the time Particular clothing outfits
Visual Intelligence & Social Multimedia Analytics Lab
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
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Visual Intelligence & Social Multimedia Analytics Lab
Questions?