Effects of social approval votes on search performance

Gabriella Kazai*, Natasa Milic-Frayling

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Citations (Scopus)

Abstract

In this paper we develop a Social Information Retrieval model that incorporates different types of social approval votes for documents in a collection. The approvals reflect a level of endorsement by the community related to the collection and can be interpreted as trust, relevance, recommendation, and similar. They can come from perceived authorities, such as recognized experts and professional associations, or from aggregated opinions of a wider community, representing popular approval. We conducted preliminary experiments to incorporate social approval votes into search over 42,000 books by training neural networks. Using a set of 250 search topics with partial relevance judgments from non-expert users, we observe that the votes reflecting a broad appeal are most effective. We hypothesize that such sources of approval are more compatible with the general nature of the relevance judgments used in the experiments.

Original languageEnglish
Title of host publicationITNG 2009 - 6th International Conference on Information Technology
Subtitle of host publicationNew Generations
Pages1554-1559
Number of pages6
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event6th International Conference on Information Technology: New Generations, ITNG 2009 - Las Vegas, NV, United States
Duration: 27 Apr 200929 Apr 2009

Publication series

NameITNG 2009 - 6th International Conference on Information Technology: New Generations

Conference

Conference6th International Conference on Information Technology: New Generations, ITNG 2009
Country/TerritoryUnited States
CityLas Vegas, NV
Period27/04/0929/04/09

Keywords

  • Authority
  • Book retrieval
  • Popularity
  • Social information retrieval

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