论文标题
立即使用用户反馈来改善顺序查询建议
Improving Sequential Query Recommendation with Immediate User Feedback
论文作者
论文摘要
我们为交互式数据探索设置中的下一个查询建议提出了一种算法，例如知识发现以进行信息收集。最先进的查询建议算法基于利用历史交互数据的顺序到序列学习方法。由于学习过程中涉及的监督，这种方法无法适应立即的用户反馈。我们建议使用基于变压器的因果语言模型来查询建议，以适应使用多臂Bandit（MAB）框架的即时用户反馈。我们使用流行的在线文献发现服务中的日志文件进行了大规模的实验研究，并证明我们的算法在基于最新的变压器的最先进的查询建议模型方面可以大大改善每轮遗憾，这些模型无法立即使用用户反馈。我们的数据模型和源代码可从https://github.com/shampp/exp3_ss获得
We propose an algorithm for next query recommendation in interactive data exploration settings, like knowledge discovery for information gathering. The state-of-the-art query recommendation algorithms are based on sequence-to-sequence learning approaches that exploit historical interaction data. Due to the supervision involved in the learning process, such approaches fail to adapt to immediate user feedback. We propose to augment the transformer-based causal language models for query recommendations to adapt to the immediate user feedback using multi-armed bandit (MAB) framework. We conduct a large-scale experimental study using log files from a popular online literature discovery service and demonstrate that our algorithm improves the per-round regret substantially, with respect to the state-of-the-art transformer-based query recommendation models, which do not make use of immediate user feedback. Our data model and source code are available at https://github.com/shampp/exp3_ss
