Thesis
Predicting e-commerce item popularity using image quality features
Master of Science (M.S.), Drexel University
Jun 2016
DOI:
https://doi.org/10.17918/etd-6860
Abstract
In order to traverse the plethora of items for sale online, searching, ranking, and recommendation systems must be built, and the quality of these systems can make the difference between boom or bust. In all of these methods, being able to distinguish between popular and non-popular items is very important. Traditionally, these systems have only utilized textual metadata, however, images represent first order information to the shopper, and are composed of a variety of signals that shoppers respond to. In this thesis we look at the problem of predicting item popularity on a popular e-commerce site using image quality features, and show that these features provide complementary information to the textual features in making this prediction.
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Details
- Title
- Predicting e-commerce item popularity using image quality features
- Creators
- Stephen Zakrewsky - DU
- Contributors
- Ali Shokoufandeh (Advisor) - Drexel University (1970-)
- Awarding Institution
- Drexel University
- Degree Awarded
- Master of Science (M.S.)
- Publisher
- Drexel University; Philadelphia, Pennsylvania
- Number of pages
- viii, 28 pages
- Resource Type
- Thesis
- Language
- English
- Academic Unit
- Computer Science (Computing) (2013-2026); College of Computing and Informatics (2013-2026); Drexel University
- Other Identifier
- 6860; 991014632219004721