Searching distributed collections with inference networks
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
A language modeling framework for resource selection and results merging
Proceedings of the eleventh international conference on Information and knowledge management
Relevant document distribution estimation method for resource selection
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
A regression framework for learning ranking functions using relative relevance judgments
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Domain Adaptation of Conditional Probability Models Via Feature Subsetting
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Blog site search using resource selection
Proceedings of the 17th ACM conference on Information and knowledge management
Trada: tree based ranking function adaptation
Proceedings of the 17th ACM conference on Information and knowledge management
Integration of news content into web results
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Domain adaptation in natural language processing
Domain adaptation in natural language processing
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
SUSHI: scoring scaled samples for server selection
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Improving web page classification by label-propagation over click graphs
Proceedings of the 18th ACM conference on Information and knowledge management
Classification-based resource selection
Proceedings of the 18th ACM conference on Information and knowledge management
Central-rank-based collection selection in uncooperative distributed information retrieval
ECIR'07 Proceedings of the 29th European conference on IR research
Proceedings of the fourth ACM international conference on Web search and data mining
Generalized link suggestions via web site clustering
Proceedings of the 20th international conference on World wide web
A methodology for evaluating aggregated search results
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Interest and Evaluation of Aggregated Search
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Learning to aggregate vertical results into web search results
Proceedings of the 20th ACM international conference on Information and knowledge management
Learning to rank with multi-aspect relevance for vertical search
Proceedings of the fifth ACM international conference on Web search and data mining
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
The effect of aggregated search coherence on search behavior
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
Vertical selection in the information domain of children
Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
Incorporating vertical results into search click models
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
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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Vertical aggregation is the task of incorporating results from specialized search engines or verticals (e.g., images, video, news) into Web search results. Vertical selection is the subtask of deciding, given a query, which verticals, if any, are relevant. State of the art approaches use machine learned models to predict which verticals are relevant to a query. When trained using a large set of labeled data, a machine learned vertical selection model outperforms baselines which require no training data. Unfortunately, whenever a new vertical is introduced, a costly new set of editorial data must be gathered. In this paper, we propose methods for reusing training data from a set of existing (source) verticals to learn a predictive model for a new (target) vertical. We study methods for learning robust, portable, and adaptive cross-vertical models. Experiments show the need to focus on different types of features when maximizing portability (the ability for a single model to make accurate predictions across multiple verticals) than when maximizing adaptability (the ability for a single model to make accurate predictions for a specific vertical). We demonstrate the efficacy of our methods through extensive experimentation for 11 verticals