Supporting More-Like-This Information Needs: Finding Similar Web Content in Different Scenarios

摘要

We examine more-like-this information needs in different scenarios. A more-like-this information need occurs, when the user sees one interesting document and wants to access other but similar documents. One of our foci is on comparing different strategies to identify related web content. We compare following links (i.e., crawling), automatically generating keyqueries for the seen document (i.e., queries that have the document in the top of their ranks), and search engine operators that automatically display related results. Our experimental study shows that in different scenarios different strategies yield the most promising related results. One of our use cases is to automatically support people who monitor rightwing content on the web. In this scenario, it turns out that crawling from a given set of seed documents is the best strategy to find related pages with similar content. Querying or the related-operator yield much fewer good results. In case of news portals, however, crawling is a bad idea since hardly any news portal links to other news portals. Instead, a search engine's related operator or querying are better strategies. Finally, for identifying related scientific publications for a given paper, all three strategies yield good results.