Paper Info
Title
Measuring And Mitigating Product Data Inaccuracy In Online Retailing
Abstract
Driven by the proliferation of Smartphones and e-Commerce, consumers rely more on online product information to make purchasing decisions. Beyond price comparisons, consumers want to know more about feature differences of similar products. However, these comparisons require rich and accurate product data. As one of the first studies, we quantify how accurate online product data is today and evaluate existing approaches of mitigating inaccuracy. The result shows that the accuracy varies a lot across different Web sites and can be as low as 20%. However, when aggregating product information across different Web pages, the accuracy can be improved on average by 11.3%. Based on the analysis, we propose an attribute-based authentication approach based on Semantic Web to further mitigate online data inaccuracy.
Year
DOI
Venue
2014
10.1007/978-3-319-11746-1_39
WEB INFORMATION SYSTEMS ENGINEERING, PT II
Keywords
Field
DocType
Data Quality, Data Accuracy, Product Data, Linked Data
Data accuracy,Data mining,Authentication,Data quality,Web page,Computer science,Linked data,Semantic Web,Product data,Purchasing,Database
Conference
Volume
ISSN
Citations 
8787
0302-9743
0
PageRank 
References 
Authors
0.34
7
2
Name
Order
Citations
PageRank
Runhua Xu1353.65
Alexander Ilic212921.71