Interactive Modeling of Concept Drift and Errors in Relevance Feedback
Interactive Modeling of Concept Drift and Errors in Relevance Feedback
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相关性反馈中概念漂移和错误的交互式建模
DOI：
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发表时间：
2016
期刊：
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通讯作者：
Samuel Kaski
中科院分区：
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作者：
A. Kangasrääsiö;Yi Chen;D. Glowacka;Samuel Kaski
在探索性搜索任务中，用户通常一开始就对他们的搜索目标有相当大的不确定性，因此用户的搜索意图可能是不稳定的，因为用户在搜索过程中不断学习和重新制定她的搜索假设。这可能导致用户给出的相关反馈中明显的概念漂移。我们建立了一个贝叶斯回归模型来预测每个用户反馈的准确性，从而发现反馈数据集中的离群点。为了配合这个模型，我们引入了一个时间线界面，该界面将反馈历史可视化给用户，并就哪些过去的反馈可能需要调整给她提供建议。该界面还允许用户调整模型做出的反馈精度推断。仿真实验表明，新用户模型的性能优于更简单的基准，并且在少量额外的用户交互的情况下，性能接近Oracle。一项用户研究表明，所提出的建模技术与时间线界面相结合，使用户更容易注意到并纠正反馈中的错误，产生更好和更多样化的推荐，使用户更容易找到他们喜欢的项目，并且更容易理解。
In exploratory search tasks, users usually start with considerable uncertainty about their search goals, and so the search intent of the user may be volatile as the user is constantly learning and reformulating her search hypothesis during the search. This may lead to a noticeable concept drift in the relevance feedback given by the user. We formulate a Bayesian regression model for predicting the accuracy of each individual user feedback and thus find outliers in the feedback data set. To accompany this model, we introduce a timeline interface that visualizes the feedback history to the user and gives her suggestions on which past feedback is likely in need of adjustment. This interface also allows the user to adjust the feedback accuracy inferences made by the model. Simulation experiments demonstrate that the performance of the new user model outperforms a simpler baseline and that the performance approaches that of an oracle, given a small amount of additional user interaction. A user study shows that the proposed modeling technique, combined with the timeline interface, made it easier for the users to notice and correct mistakes in their feedback, resulted in better and more diverse recommendations, allowed users to easier find items they liked, and was more understandable.