Retrievability Based Document Selection for Relevance Feedback with Automatically Generated Query Variants

Anirban Chakraborty, Debasis Ganguly, Owen Conlan

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

Abstract

To mitigate the problem of over-dependence of a pseudo-relevance feedback algorithm on the top-M document set, we make use of a set of equivalence classes of queries rather than one single query. These query equivalents are automatically constructed either from a) a knowledge base of prior distributions of terms with respect to the given query terms, or b) iteratively generated from a relevance model of term distributions in the absence of such priors. These query variants are then used to estimate the retrievability of each document with the hypothesis that documents that are more likely to be retrieved at top-ranks for a larger number of these query variants are more likely to be effective for relevance feedback. Results of our experiments show that our proposed method is able to achieve substantially better precision at top-ranks (e.g. higher nDCG@5 and P@5 values) for ad-hoc IR and points-of-interest (POI) recommendation tasks.
Original languageEnglish
Title of host publicationProceedings of the 29th ACM International Conference on Information & Knowledge Management
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery (ACM)
Pages125–134
Number of pages10
ISBN (Print)9781450368599
DOIs
Publication statusPublished - 19 Oct 2020
Event29th ACM International Conference on Information and Knowledge Management - Omline Conference
Duration: 19 Oct 202023 Oct 2020
https://www.cikm2020.org/index.html

Publication series

NameCIKM '20
PublisherAssociation for Computing Machinery

Conference

Conference29th ACM International Conference on Information and Knowledge Management
Abbreviated titleCIKM 2020
CityOmline Conference
Period19/10/2023/10/20
Internet address

Keywords

  • pseudo-relevance feedback
  • query variants
  • retrievability

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