This project is focused on understanding and managing product data quality on the Web. One part is concerned with measuring and mitigating product data inaccuracy in online retailing. Another key aspect is on approaches (e.g. attribute-based authentication, source-based authentication, …) for increasing product data quality on the Web. This will ultimately illustrate use cases for the extension of existing standards.
Driven by the proliferation of Smartphones and e-Commerce, consumers rely more on online information to make purchase decisions. Beyond price comparison, they want to know more about feature differences of similar products. Providing products and services to fulfill growing consumer needs rely on accurate online product data. In this project, we quantify online product data accuracy and evaluate the existing approaches of mitigating inaccuracy. We also aim to provide an explanatory model to identify the potential sources of errors and their impact. An attribute-based authentication approach is developed to mitigate data inaccuracy. In future research, we will validate our model and measure data inaccuracy quantitatively on a large scale. Furthermore, we will evaluate our attribute-based approach in field research and GS1’s B2C Sandbox.
Manufacturers, online retailers, search engines and aggregators