Learning to aggregate vertical results into web search results

Jaime Arguello, Fernando Diaz, Jamie Callan
2011 Proceedings of the 20th ACM international conference on Information and knowledge management - CIKM '11  
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
more » ... cals 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. We present 3 general approaches that address these challenges in different ways and compare their results across a set of 13 verticals and 1070 queries. We show that the best approaches are those that allow the learning algorithm to learn a vertical-specific relationship between features and relevance.
doi:10.1145/2063576.2063611 dblp:conf/cikm/ArguelloDC11 fatcat:x4bi7owrdbd65arqv4o643q6tu