基于查询性能预测的鲁棒检索排序研究

信息检索技术致力于从海量的信息资源中为用户获取所需的信息。相较于传统的简单模型,近些年来的大量研究工作在提升了检索结果平均质量的同时,往往忽略了鲁棒性的问题,即造成了很多查询的性能下降,导致用户满意度的显著下降。本文提出了一种基于排序学习的查询性能预测方法,针对每一个查询,对多种模型得到的检索结果列表进行预测,将其中预测性能最优的检索结果列表展示给用户。在LETOR的三个标准数据集OHSUMED、MQ2008和MSLR-WEB10K上的一系列对比实验表明,在以经典的BM25模型作为基准的情况下,与当前最好的检索模型之一LambdaMART相比,该方法在提升了检索结果平均质量的同时,显著地减少了性能下降的查询的数量,具备较好的鲁棒性。

Robust Ranking via Query Performance Prediction

The main purpose of information retrieval technology is satisfying users information needs by using massive amounts of information recource. Recent years, many techniques increase average effectiveness relative to traditional simple model while they often ignore the robustness issue. Users satisfaction will be significantly hurt because of degraded results of many queries. A query performance prediction method based on learning to rank is proposed to obtain robust ranking results. For each query, the performance of multiple ranking results generated by different models are predicted and the best one is shown to the user. A series of experiments are conducted on three standard LETOR benchmark datasets which are OHSUMED, MQ2008 and MSLR-WEB10K. The results show that, compared to one of the state-of-the-art models named LambdaMART, the ranking results obtained this way significantly reduced the number of queries whose performance are hurt with respect to BM25 model while improving the nearly same degree of everage effectiveness.