Predicting the relevance of search results for e-commerce systems

Search engines (e.g. Google.com, Yahoo.com, and Bing.com) have become the dominant model of online search. Large and small e-commerce provide built-in search capability to their visitors to examine the products they have. While most large business are able to hire the necessary skills to build advan...

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Main Authors:	Al-Taie, Mohammed Zuhair, Shamsuddin, Siti Mariyam, Lucas, Joel Pinho
Format:	Article
Published:	International Center for Scientific Research and Studies (ICSRS) 2015
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://eprints.utm.my/id/eprint/54984/
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Institution:	Universiti Teknologi Malaysia

id	my.utm.54984
record_format	eprints
spelling	my.utm.549842017-07-31T08:09:32Z http://eprints.utm.my/id/eprint/54984/ Predicting the relevance of search results for e-commerce systems Al-Taie, Mohammed Zuhair Shamsuddin, Siti Mariyam Lucas, Joel Pinho QA75 Electronic computers. Computer science Search engines (e.g. Google.com, Yahoo.com, and Bing.com) have become the dominant model of online search. Large and small e-commerce provide built-in search capability to their visitors to examine the products they have. While most large business are able to hire the necessary skills to build advanced search engines, small online business still lack the ability to evaluate the results of their search engines, which means losing the opportunity to compete with larger business. The purpose of this paper is to build an open-source model that can measure the relevance of search results for online businesses as well as the accuracy of their underlined algorithms. We used data from a Kaggle.com competition in order to show our model running on real data. International Center for Scientific Research and Studies (ICSRS) 2015 Article PeerReviewed Al-Taie, Mohammed Zuhair and Shamsuddin, Siti Mariyam and Lucas, Joel Pinho (2015) Predicting the relevance of search results for e-commerce systems. International Journal of Advances in Soft Computing and its Applications, 7 (3). pp. 85-93. ISSN 2074-8523
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Al-Taie, Mohammed Zuhair Shamsuddin, Siti Mariyam Lucas, Joel Pinho Predicting the relevance of search results for e-commerce systems
description	Search engines (e.g. Google.com, Yahoo.com, and Bing.com) have become the dominant model of online search. Large and small e-commerce provide built-in search capability to their visitors to examine the products they have. While most large business are able to hire the necessary skills to build advanced search engines, small online business still lack the ability to evaluate the results of their search engines, which means losing the opportunity to compete with larger business. The purpose of this paper is to build an open-source model that can measure the relevance of search results for online businesses as well as the accuracy of their underlined algorithms. We used data from a Kaggle.com competition in order to show our model running on real data.
format	Article
author	Al-Taie, Mohammed Zuhair Shamsuddin, Siti Mariyam Lucas, Joel Pinho
author_facet	Al-Taie, Mohammed Zuhair Shamsuddin, Siti Mariyam Lucas, Joel Pinho
author_sort	Al-Taie, Mohammed Zuhair
title	Predicting the relevance of search results for e-commerce systems
title_short	Predicting the relevance of search results for e-commerce systems
title_full	Predicting the relevance of search results for e-commerce systems
title_fullStr	Predicting the relevance of search results for e-commerce systems
title_full_unstemmed	Predicting the relevance of search results for e-commerce systems
title_sort	predicting the relevance of search results for e-commerce systems
publisher	International Center for Scientific Research and Studies (ICSRS)
publishDate	2015
url	http://eprints.utm.my/id/eprint/54984/
_version_	1643653658098794496

Predicting the relevance of search results for e-commerce systems

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