Next Generation of Product Search and Discovery
Online shopping has become an important part of people’s daily life with the rapid development of e-commerce. In some domains such as books, electronics, and CD/DVDs, online shopping has surpassed or even replaced the traditional shopping method. Compared with traditional retailing, e-commerce is in...
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ndltd-fiu.edu-oai-digitalcommons.fiu.edu-etd-34982018-01-05T15:31:51Z Next Generation of Product Search and Discovery Zeng, Kaiman Online shopping has become an important part of people’s daily life with the rapid development of e-commerce. In some domains such as books, electronics, and CD/DVDs, online shopping has surpassed or even replaced the traditional shopping method. Compared with traditional retailing, e-commerce is information intensive. One of the key factors to succeed in e-business is how to facilitate the consumers’ approaches to discover a product. Conventionally a product search engine based on a keyword search or category browser is provided to help users find the product information they need. The general goal of a product search system is to enable users to quickly locate information of interest and to minimize users’ efforts in search and navigation. In this process human factors play a significant role. Finding product information could be a tricky task and may require an intelligent use of search engines, and a non-trivial navigation of multilayer categories. Searching for useful product information can be frustrating for many users, especially those inexperienced users. This dissertation focuses on developing a new visual product search system that effectively extracts the properties of unstructured products, and presents the possible items of attraction to users so that the users can quickly locate the ones they would be most likely interested in. We designed and developed a feature extraction algorithm that retains product color and local pattern features, and the experimental evaluation on the benchmark dataset demonstrated that it is robust against common geometric and photometric visual distortions. Besides, instead of ignoring product text information, we investigated and developed a ranking model learned via a unified probabilistic hypergraph that is capable of capturing correlations among product visual content and textual content. Moreover, we proposed and designed a fuzzy hierarchical co-clustering algorithm for the collaborative filtering product recommendation. Via this method, users can be automatically grouped into different interest communities based on their behaviors. Then, a customized recommendation can be performed according to these implicitly detected relations. In summary, the developed search system performs much better in a visual unstructured product search when compared with state-of-art approaches. With the comprehensive ranking scheme and the collaborative filtering recommendation module, the user’s overhead in locating the information of value is reduced, and the user’s experience of seeking for useful product information is optimized. 2015-11-12T08:00:00Z text application/pdf http://digitalcommons.fiu.edu/etd/2312 http://digitalcommons.fiu.edu/cgi/viewcontent.cgi?article=3498&context=etd FIU Electronic Theses and Dissertations FIU Digital Commons visual search content based image retrieval ranking hypergraph learning recommendation collaborative filtering clustering Other Electrical and Computer Engineering Signal Processing |
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visual search content based image retrieval ranking hypergraph learning recommendation collaborative filtering clustering Other Electrical and Computer Engineering Signal Processing |
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visual search content based image retrieval ranking hypergraph learning recommendation collaborative filtering clustering Other Electrical and Computer Engineering Signal Processing Zeng, Kaiman Next Generation of Product Search and Discovery |
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Online shopping has become an important part of people’s daily life with the rapid development of e-commerce. In some domains such as books, electronics, and CD/DVDs, online shopping has surpassed or even replaced the traditional shopping method. Compared with traditional retailing, e-commerce is information intensive. One of the key factors to succeed in e-business is how to facilitate the consumers’ approaches to discover a product. Conventionally a product search engine based on a keyword search or category browser is provided to help users find the product information they need. The general goal of a product search system is to enable users to quickly locate information of interest and to minimize users’ efforts in search and navigation. In this process human factors play a significant role. Finding product information could be a tricky task and may require an intelligent use of search engines, and a non-trivial navigation of multilayer categories. Searching for useful product information can be frustrating for many users, especially those inexperienced users.
This dissertation focuses on developing a new visual product search system that effectively extracts the properties of unstructured products, and presents the possible items of attraction to users so that the users can quickly locate the ones they would be most likely interested in. We designed and developed a feature extraction algorithm that retains product color and local pattern features, and the experimental evaluation on the benchmark dataset demonstrated that it is robust against common geometric and photometric visual distortions. Besides, instead of ignoring product text information, we investigated and developed a ranking model learned via a unified probabilistic hypergraph that is capable of capturing correlations among product visual content and textual content. Moreover, we proposed and designed a fuzzy hierarchical co-clustering algorithm for the collaborative filtering product recommendation. Via this method, users can be automatically grouped into different interest communities based on their behaviors. Then, a customized recommendation can be performed according to these implicitly detected relations. In summary, the developed search system performs much better in a visual unstructured product search when compared with state-of-art approaches. With the comprehensive ranking scheme and the collaborative filtering recommendation module, the user’s overhead in locating the information of value is reduced, and the user’s experience of seeking for useful product information is optimized. |
author |
Zeng, Kaiman |
author_facet |
Zeng, Kaiman |
author_sort |
Zeng, Kaiman |
title |
Next Generation of Product Search and Discovery |
title_short |
Next Generation of Product Search and Discovery |
title_full |
Next Generation of Product Search and Discovery |
title_fullStr |
Next Generation of Product Search and Discovery |
title_full_unstemmed |
Next Generation of Product Search and Discovery |
title_sort |
next generation of product search and discovery |
publisher |
FIU Digital Commons |
publishDate |
2015 |
url |
http://digitalcommons.fiu.edu/etd/2312 http://digitalcommons.fiu.edu/cgi/viewcontent.cgi?article=3498&context=etd |
work_keys_str_mv |
AT zengkaiman nextgenerationofproductsearchanddiscovery |
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