Search result diversification plays a crucial role in improving users’ search experience by providing users with documents covering more subtopics. Previous studies have made great progress in leveraging inter-document interactions to measure the similarity among documents. However, different parts of the document may embody different subtopics and existing models ignore the subtle similarities and differences of content within each document. In this article, we propose a hierarchical attention framework to combine intra-document interactions with inter-document interactions in a complementary manner in order to conduct multi-grained document modeling. Specifically, we separate the document into passages to model the document content from multi-grained perspectives. Then, we design stacked interaction blocks to conduct inter-document and intra-document interactions. Moreover, to measure the subtopic coverage of each document more accurately, we propose a passage-aware document-subtopic interaction to perform fine-grained document-subtopic interaction. Experimental results demonstrate that our model achieves state-of-the-art performance compared with existing methods.

搜索结果多样化为用户提供涵盖更多子主题的文档，对于改善用户的搜索体验起着至关重要的作用。先前的研究在利用文档间交互来衡量文档之间的相似性方面取得了很大进展。然而，文档的不同部分可能包含不同的子主题，并且现有模型忽略了每个文档内内容的微妙相似性和差异。在本文中，我们提出了一种分层注意力框架，以互补的方式将文档内交互与文档间交互结合起来，以进行多粒度文档建模。具体来说，我们将文档分成多个段落，从多粒度的角度对文档内容进行建模。然后，我们设计堆叠交互块来进行文档间和文档内交互。此外，为了更准确地测量每个文档的副主题覆盖率，我们提出了一种段落感知的文档-副主题交互来执行细粒度的文档-副主题交互。实验结果表明，与现有方法相比，我们的模型实现了最先进的性能。