Searching distributed collections with inference networks
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
A language modeling framework for resource selection and results merging
Proceedings of the eleventh international conference on Information and knowledge management
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Stuff I've seen: a system for personal information retrieval and re-use
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Combining document representations for known-item search
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Relevant document distribution estimation method for resource selection
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Simple BM25 extension to multiple weighted fields
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Towards task-based personal information management evaluations
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Building simulated queries for known-item topics: an analysis using six european languages
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Designing games with a purpose
Communications of the ACM - Designing games with a purpose
Blog site search using resource selection
Proceedings of the 17th ACM conference on Information and knowledge management
Sources of evidence for vertical selection
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Server selection methods in personal metasearch: a comparative empirical study
Information Retrieval
Improving search engines using human computation games
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
Retrieval experiments using pseudo-desktop collections
Proceedings of the 18th ACM conference on Information and knowledge management
Building a desktop search test-bed
ECIR'07 Proceedings of the 29th European conference on IR research
Foundations and Trends in Information Retrieval
Seeding simulated queries with user-study data for personal search evaluation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Cognitive processes in query generation
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
Evaluating an associative browsing model for personal information
Proceedings of the 20th ACM international conference on Information and knowledge management
Ranking objects by following paths in entity-relationship graphs
Proceedings of the 4th workshop on Workshop for Ph.D. students in information & knowledge management
Workshop on evaluating personal search
ACM SIGIR Forum
Evaluating search in personal social media collections
Proceedings of the fifth ACM international conference on Web search and data mining
An analysis of free-text queries for a multi-field web form
Proceedings of the 4th Information Interaction in Context Symposium
Towards realistic known-item topics for the ClueWeb
Proceedings of the 4th Information Interaction in Context Symposium
Distributed information retrieval and applications
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
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A typical desktop environment contains many document types (email, presentations, web pages, pdfs, etc.) each with different metadata. Predicting which types of documents a user is looking for in the context of a given query is a crucial part of providing effective desktop search. The problem is similar to selecting resources in distributed IR, but there are some important differences. In this paper, we quantify the impact of type prediction in producing a merged ranking for desktop search and introduce a new prediction method that exploits type-specific metadata. In addition, we show that type prediction performance and search effectiveness can be further enhanced by combining existing methods of type prediction using discriminative learning models. Our experiments employ pseudo-desktop collections and a human computation game for acquiring realistic and reusable queries.