2011 LatentTopicFeedbackforInformati

• (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:

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
2011 LatentTopicFeedbackforInformati	David Andrzejewski David Buttler			Latent Topic Feedback for Information Retrieval				10.1145/2020408.2020503		2011