Abstract-Enriching web shop pages with structured data has recently become popular in e-commerce. It is mainly driven by search engines favouring those pages. While structured data in ecommerce is mainly generated automatically by shop extensions, this data covers only a small share of the market, resulting in a major hamper for applications operating on aggregated data. In this context, more than 90% of product detail pages on the web are generated by only 7 e-commerce systems. Meanwhile, little research addresses methods to automatically detect e-commerce systems. Automated detection would allow to design system-specific extractors able to grow the amount of structured data in e-commerce. Therefore, we propose a novel approach to this problem, which filters features generated from HTML tag attributes with an e-commerce specific white list. We evaluate 6 classification algorithms on the problem and discuss computational effort. We can show that this approach is capable of detecting the 6 most important e-commerce systems with a F1-score of 0.9 by analyzing only one HTML page per web shop. We evaluate our findings on an independent dataset and on reference shop sites.
Similarly, Stoll and Hepp REF propose a technique that analyzes HTML tag attributes of e-commerce sites to discover which of the six most popular e-commerce systems is being used.
Detection of E-Commerce Systems with Sparse Features and Supervised Classification
2013 IEEE 10th International Conference on e-Business Engineering
2013 IEEE 10th International Conference on e-Business Engineering
Computer Science