Learning to aggregate vertical results into web search results

Aggregated search is the task of integrating results from potentially multiple specialized search services, or verticals, into the Web search results. The task requires predicting not only which verticals to present (the focus of most prior research), but also predicting where in the Web results to present them (i.e., above or below the Web results, or somewhere in between). Learning models to aggregate results from multiple verticals is associated with two major challenges. First, because verticals retrieve different types of results and address different search tasks, results from different verticals are associated with different types of predictive evidence (or features). Second, even when a feature is common across verticals, its predictiveness may be verticalspecific. Therefore, approaches to aggregating vertical results require handling an inconsistent feature representation across verticals, and, potentially, a vertical-specific relationship between features and relevance....

• Type: Journal
• Year: 2011
• Where: CIKM
• Authors: Jaime Arguello, Fernando Diaz, Jamie Callan
• Keywords: Carnegie Mellon University, CIKM 2011, Information Storage And Retrieval, Information Technology, Specialized Search Services