Effective trend detection within a dynamic search context

Anat Hashavit, Roy Levin, Ido Guy, Gilad Kutiel

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

4 Scopus citations

Abstract

In recent years, studies about trend detection in online social media streams have begun to emerge. Since not all users are likely to always be interested in the same set of trends, some of the research also focused on personalizing the trends by using some predefined personalized context. In this paper, we take this problem further to a setting in which the user's context is not predefined, but rather determined as the user issues a query. This presents a new challenge since trends cannot be computed ahead of time using high latency algorithms. We present RT-Trend, an online trend detection algorithm that promptly finds relevant in-context trends as users issue search queries over a dataset of documents. We evaluate our approach using real data from an online social network by assessing its ability to predict actual activity increase of social network entities in the context of a search result. Since we implemented this feature into an existing tool with an active pool of users, we also report click data, which suggests positive feedback.

Original languageEnglish
Title of host publicationSIGIR 2016 - Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages817-820
Number of pages4
ISBN (Electronic)9781450342902
DOIs
StatePublished - 7 Jul 2016
Event39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016 - Pisa, Italy
Duration: 17 Jul 201621 Jul 2016

Publication series

NameSIGIR 2016 - Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

Conference39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016
Country/TerritoryItaly
CityPisa
Period17/07/1621/07/16

Keywords

  • Analytics
  • Tag cloud
  • Trend cloud
  • Trends
  • Word cloud

ASJC Scopus subject areas

  • Information Systems
  • Software

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