An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Trust region Newton methods for large-scale logistic regression
Proceedings of the 24th international conference on Machine learning
Learning query intent from regularized click graphs
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Information Retrieval
Introduction to Information Retrieval
Integration of news content into web results
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Sources of evidence for vertical selection
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Adaptation of offline vertical selection predictions in the presence of user feedback
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Click-through prediction for news queries
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Generalized distances between rankings
Proceedings of the 19th international conference on World wide web
Vertical selection in the presence of unlabeled verticals
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the fourth ACM international conference on Web search and data mining
A methodology for evaluating aggregated search results
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Beyond ten blue links: enabling user click modeling in federated web search
Proceedings of the fifth ACM international conference on Web search and data mining
Evaluating aggregated search pages
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Task complexity, vertical display and user interaction in aggregated search
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Possibilistic model for aggregated search in XML documents
International Journal of Intelligent Information and Database Systems
The effect of aggregated search coherence on search behavior
Proceedings of the 21st ACM international conference on Information and knowledge management
Evaluating reward and risk for vertical selection
Proceedings of the 21st ACM international conference on Information and knowledge management
Using intent information to model user behavior in diversified search
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Distributed information retrieval and applications
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Incorporating vertical results into search click models
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Aggregated search interface preferences in multi-session search tasks
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Preference based evaluation measures for novelty and diversity
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Which vertical search engines are relevant?
Proceedings of the 22nd international conference on World Wide Web
On the reliability and intuitiveness of aggregated search metrics
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Factors affecting aggregated search coherence and search behavior
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Aggregated search: A new information retrieval paradigm
ACM Computing Surveys (CSUR)
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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 vertical-specific. 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.