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Resumo:
We consider a model of an on-line software market, where an intermediary distributes products from sellers to buyers. When products of sellers are vertically differentiated, an intermediary, earning a proportion of sales, has an incentive to hide the worse product on the second page, and only keep the better product on the front page: that weakens the competition, allowing the seller with the better product to charge a higher price. With heterogeneous visiting costs to the second page, the platform's revenue might improve, but the outcome will become socially suboptimal.
Resumo:
Automatically determining and assigning shared and meaningful text labels to data extracted from an e-Commerce web page is a challenging problem. An e-Commerce web page can display a list of data records, each of which can contain a combination of data items (e.g. product name and price) and explicit labels, which describe some of these data items. Recent advances in extraction techniques have made it much easier to precisely extract individual data items and labels from a web page, however, there are two open problems: 1. assigning an explicit label to a data item, and 2. determining labels for the remaining data items. Furthermore, improvements in the availability and coverage of vocabularies, especially in the context of e-Commerce web sites, means that we now have access to a bank of relevant, meaningful and shared labels which can be assigned to extracted data items. However, there is a need for a technique which will take as input a set of extracted data items and assign automatically to them the most relevant and meaningful labels from a shared vocabulary. We observe that the Information Extraction (IE) community has developed a great number of techniques which solve problems similar to our own. In this work-in-progress paper we propose our intention to theoretically and experimentally evaluate different IE techniques to ascertain which is most suitable to solve this problem.