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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bădică, C., Bădică, A.: Rule learning for feature values extraction from HTML product information sheets. In: Antoniou, G., Boley, H. (eds.) RuleML 2004. LNCS, vol. 3323, pp. 37–48. Springer, Heidelberg (2004)
Barnes, S., Vidgen, R.: An Integrative Approach to the Assessment of E-commerce Quality. Journal of Electronic Commerce Research 3(3) (2002)
Bovee, M., Srivastava, R.P., Mak, B.: A Conceptual Framework and Belief-function Approach to Assessing Overall Information Quality. Int. J. Intell. Syst 18, 51–74 (2003)
Cho, V.: Data Quality on the Internet. Services and Business Computing Solutions with XML: Application for Quality Management and Best Processes, pp. 171–176. Information Science Reference, New York (2009)
Eppler, M., Muenzenmayer, P.: Measuring Information Quality in the Web Context: A Survey of State-of-the-Art Instruments and an Application Methodology. In: 7th International Conference on Information Quality, Cambridge, pp. 187–196 (2002)
European Union.: Regulation (EU) No 1169/2011 of the European parliament and of the council of 25 October 2011. Official Journal of the European Union (2011)
Evans, P.F., Sehgal, V., Bugnaru, C., McGowan, B.: US Online Retail Forecast, 2008 to 2013. Forrester Inc. Report (2009)
Fürber, C., Hepp, M.: A Semantic Web Information Quality Assessment Framework. In: 25th European Conference on Information System, Helsinki (2011)
Klein, B.D.: User Perceptions of Data Quality: Internet and Traditional Text Sources. The Journal of Computer Information System 41(2001), 9–18 (2001)
Knap, T., Michelfeit, J., Necasky, M.: Linked Open Data Aggregation: Conflict Resolution and Aggregate Quality. In: Computer Software and Applications Conference Workshops (2012)
Knight, S., Burn, J.: Developing a Framework for Assessing Information Quality on the World Wide Web. Informing Science Journal 8, 159–172 (2005)
Mendes, P.N., Mühleisen, H., Bizer, C.: Sieve: Linked Data Quality Assessment and Fusion. In: Joint EDBT/ICDT Workshops, Berlin, pp. 116-123 (2012)
Redman, T.C.: The Impact of Poor Data Quality on the Typical Enterprise. Magazine Communications of the ACM 41, 79–82 (1998)
Rivest, R.L., Shamir, A., Adleman, L.: A Method for Obtaining Digital Signatures and Public-key Cryptosystems. Magazine Communications of the ACM 21, 120–126 (1978)
Wang, R., Strong, D.: Beyond Accuracy: What Data Quality Means to Data Consumer. Journal Management Information System 12, 5–33 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Xu, R., Ilic, A. (2014). Measuring and Mitigating Product Data Inaccuracy in Online Retailing. In: Benatallah, B., Bestavros, A., Manolopoulos, Y., Vakali, A., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2014. WISE 2014. Lecture Notes in Computer Science, vol 8787. Springer, Cham. https://doi.org/10.1007/978-3-319-11746-1_39
Download citation
DOI: https://doi.org/10.1007/978-3-319-11746-1_39
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11745-4
Online ISBN: 978-3-319-11746-1
eBook Packages: Computer ScienceComputer Science (R0)