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.

网络搜索是最频繁的在线活动之一。在这种情况下，广泛的信息查询要求用户意图获得有关特定主题或领域的知识。为了更好地满足学习需求，交互式信息检索领域的最新研究提出了超越搜索结果相关性排名并考虑面向学习的搜索会话中用户的知识状态的重要性。先前的工作已经研究了监督模型的使用，以根据搜索会话期间的用户交互来预测用户的知识获取和知识状态。但是，在此任务中，用户与之交互的资源的特性尚未得到充分的探索和利用。在这项工作中，我们介绍了一套新颖的以资源为中心的功能，并展示了它们有能力显着改进监督模型，以预测Web搜索会话中用户的知识获取和知识状态。鉴于针对此类任务的可靠培训数据稀少且获取成本高昂，因此我们将做出重要贡献。我们介绍了各种功能选择策略，旨在选择有限的有效和通用功能子集。