Temporal feedback for tweet search with non-parametric density estimation

Miles Efron, Jimmy Lin, Jiyin He, Arjen De Vries

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper investigates the temporal cluster hypothesis: in search tasks where time plays an important role, do relevant documents tend to cluster together in time? We explore this question in the context of tweet search and temporal feed- back: starting with an initial set of results from a baseline retrieval model, we estimate the temporal density of relevant documents, which is then used for result reranking. Our contributions lie in a method to characterize this temporal density function using kernel density estimation, with and without human relevance judgments, and an approach to integrating this information into a standard retrieval model. Experiments on TREC datasets confirm that our temporal feedback formulation improves search effectiveness, thus providing support for our hypothesis. Our approach outperforms both a standard baseline and previous temporal retrieval models. Temporal feedback improves over standard lexical feedback (with and without human judgments), illustrating that temporal relevance signals exist independently of document content.

Original languageEnglish (US)
Title of host publicationSIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery
Pages33-42
Number of pages10
ISBN (Print)9781450322591
DOIs
StatePublished - 2014
Event37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014 - Gold Coast, QLD, Australia
Duration: Jul 6 2014Jul 11 2014

Publication series

NameSIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval

Other

Other37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014
CountryAustralia
CityGold Coast, QLD
Period7/6/147/11/14

Keywords

  • Cluster hypothesis
  • Relevance feedback
  • Temporal clustering

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Information Systems

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