论文标题
在搜索会话中建模用户的信息需求
Modeling Information Need of Users in Search Sessions
论文作者
论文摘要
用户发出查询以搜索引擎，并尝试在产生的结果中找到所需的信息。如果首先不满足他们的信息需求，他们会重复此过程。在查询中识别描述用户实际信息需求的查询中的重要单词至关重要，并将确定搜索课程的过程。为此，我们提出了一个基于序列的神经体系结构，该神经体系结构利用用户发出的过去查询集，以及他们探索的结果。首先，我们采用模型来预测当前查询中重要的单词，这些单词很重要，并且将保留在下一个查询中。此外，作为我们模型的下游应用，我们将其评估为下一个查询建议的广泛流行任务。我们表明，我们捕获信息需求的直观策略可以在两个大型现实世界搜索日志数据集上的这些任务上产生卓越的性能。
Users issue queries to Search Engines, and try to find the desired information in the results produced. They repeat this process if their information need is not met at the first place. It is crucial to identify the important words in a query that depict the actual information need of the user and will determine the course of a search session. To this end, we propose a sequence-to-sequence based neural architecture that leverages the set of past queries issued by users, and results that were explored by them. Firstly, we employ our model for predicting the words in the current query that are important and would be retained in the next query. Additionally, as a downstream application of our model, we evaluate it on the widely popular task of next query suggestion. We show that our intuitive strategy of capturing information need can yield superior performance at these tasks on two large real-world search log datasets.
