Topic-independent modeling of user knowledge in informational search sessions

Ran Yu, Rui Tang, Markus Rokicki, Ujwal Gadiraju, Stefan Dietze

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Web search is among the most frequent online activities. In this context, widespread informational queries entail user intentions to obtain knowledge with respect to a particular topic or domain. To serve learning needs better, recent research in the field of interactive information retrieval has advocated the importance of moving beyond relevance ranking of search results and considering a user’s knowledge state within learning oriented search sessions. Prior work has investigated the use of supervised models to predict a user’s knowledge gain and knowledge state from user interactions during a search session. However, the characteristics of the resources that a user interacts with have neither been sufficiently explored, nor exploited in this task. In this work, we introduce a novel set of resource-centric features and demonstrate their capacity to significantly improve supervised models for the task of predicting knowledge gain and knowledge state of users in Web search sessions. We make important contributions, given that reliable training data for such tasks is sparse and costly to obtain. We introduce various feature selection strategies geared towards selecting a limited subset of effective and generalizable features.

Original languageEnglish
Pages (from-to)240-268
Number of pages29
JournalInformation Retrieval Journal
Volume24
Issue number3
DOIs
Publication statusPublished - 2021

Keywords

  • Human–computer interaction
  • Knowledge gain
  • Online learning
  • SAL
  • Search as learning

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