2011 LatentTopicFeedbackforInformati
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- (Andrzejewski & Buttler, 2011) ⇒ David Andrzejewski, and David Buttler. (2011). “Latent Topic Feedback for Information Retrieval.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020503
Subject Headings:
Notes
Cited By
- http://scholar.google.com/scholar?q=%222011%22+Latent+Topic+Feedback+for+Information+Retrieval
- http://dl.acm.org/citation.cfm?id=2020408.2020503&preflayout=flat#citedby
Quotes
Author Keywords
- Algorithms; experimentation; latent topic models; query formulation; relevance feedback; user feedback
Abstract
We consider the problem of a user navigating an unfamiliar corpus of text documents where document metadata is limited or unavailable, the domain is specialized, and the user base is small. These challenging conditions may hold, for example, within an organization such as a business or government agency. We propose to augment standard keyword search with user feedback on latent topics. These topics are automatically learned from the corpus in an unsupervised manner and presented alongside search results. User feedback is then used to reformulate the original query, resulting in improved information retrieval performance in our experiments.
References
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Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
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2011 LatentTopicFeedbackforInformati | David Andrzejewski David Buttler | Latent Topic Feedback for Information Retrieval | 10.1145/2020408.2020503 | 2011 |