期刊名称：International Journal of Advances in Soft Computing and Its Applications
印刷版ISSN：2074-8523
出版年度：2015
卷号：7
期号：3-Special
出版社：International Center for Scientific Research and Studies
摘要：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
关键词：CrowdFlower; E-Commerce; Kaggle Competition; Random ; Forest; Relevance Prediction; Scikit-learn; Support Vector Machines
