论文标题
仍然没有找到您要寻找的东西 - 从用户互动功能中检测到Web搜索任务的意图
Still Haven't Found What You're Looking For -- Detecting the Intent of Web Search Missions from User Interaction Features
论文作者
论文摘要
网络搜索是最常见的在线活动之一。传统信息检索技术集中于用户查询背后的信息需求，但以前的工作表明，用户行为和互动可以提供重要的信号，以了解搜索任务的基本意图。既定的分类法都区分了交易，导航和信息搜索任务，尤其是后者涉及学习目标，即获得有关特定主题的知识的意图。我们通过利用从在线搜索任务中从用户互动中获得的一系列功能来介绍一种监督的方法，以将在线搜索任务分类为其中的任何一个类别。将我们的模型应用于现实世界查询日志的数据集，我们表明搜索任务可以按平均F1分数为63％，准确性为69％，而在信息和导航任务上的性能尤其有望（F1> 75％）。这表明在线搜索过程中利用这种监督分类的潜力可以更好地促进检索和排名以及改善关联服务，例如有针对性的在线广告。
Web search is among the most frequent online activities. Whereas traditional information retrieval techniques focus on the information need behind a user query, previous work has shown that user behaviour and interaction can provide important signals for understanding the underlying intent of a search mission. An established taxonomy distinguishes between transactional, navigational and informational search missions, where in particular the latter involve a learning goal, i.e. the intent to acquire knowledge about a particular topic. We introduce a supervised approach for classifying online search missions into either of these categories by utilising a range of features obtained from the user interactions during an online search mission. Applying our model to a dataset of real-world query logs, we show that search missions can be categorised with an average F1 score of 63% and accuracy of 69%, while performance on informational and navigational missions is particularly promising (F1>75%). This suggests the potential to utilise such supervised classification during online search to better facilitate retrieval and ranking as well as to improve affiliated services, such as targeted online ads.
