当前排序学习算法在学习时将样本集中的所有查询及其相关文档等同对待,忽略了查询之间以及其相关文档之间的差异性,影响了排序模型的性能.对查询之间的差异进行分析,同时考虑文档排序位置造成的资料被检视概率不同的差异特性,提出了一种两层加权融合的排序方法.该方法为每一个查询及其相关文档建立一个子排序模型,在此过程中,对文档赋予非对称权重,然后通过建立新的损失函数作为优化目标,利用损失函数调节不同查询产生损失之间的权重,最终实现多查询相关排序模型的加权融合.在标准数据集LETOR OHSUMED上的实验结果表明,所提方法在排序性能上有较大提升.%In ranking for document retrieval, queries often vary greatly from one another. However, most of the existing ranking methods do not consider significant differences between queries. Correctly ranking documents on the top of the result list is crucial, and one must conduct training in a way that such ranked results are accurate. A two-tier weighting aggregation ranking method was proposed. This method consisted of two steps, training of base rankers and query-level ranker aggregation. First, base rankers were established based on each query, assigning asymmetric weights to its relevant documents, then, query-level ranker aggregation used a supervised approach to learn query-dependent weights when these base rankers were combined. The experimental results on the benchmark data set LETRO ONHSUMED show that the ranking performance has been significantly improved.
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