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.
Highlights
People ubiquitously use Web search to find a variety of information and satisfy a wide range of information needs
To evaluate the performance of our approach, we tune the hyperparameters according to the average F1 score through repeated cross-topic validation, as described in Sect
Knowledge indicator classes Compared to the baseline, for pre-knowledge state (pre-KS), our model shows particular improvements in F1 score for the moderate class, indicating that the resource features allow for better identifying medium knowledge state compared to the user behavior features
Summary
People ubiquitously use Web search to find a variety of information and satisfy a wide range of information needs. Informational search sessions involve an inherent learning intent, i.e. the desire of a user to acquire knowledge or information with respect to a particular topic, assumed to be present on one or more Web pages. In this context, the individual relevance of search results is strongly dependent on the current knowledge state of the corresponding user. Recent research at the intersection of information retrieval and learning theory has recognized the importance of learning scopes and focused on observing and detecting learning needs during Web search. Zhang et al (2011) have shown that data obtained online during the search process provides valuable indicators about the domain knowledge of a user
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