An exploration of pattern-based subtopic modeling for search result diversification
An exploration of pattern-based subtopic modeling for search result diversification
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DOI：
10.1145/1998076.1998148
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发表时间：
2011-06
期刊：
影响因子：
9.8
通讯作者：
Wei Zheng;Xuan-Yi Wang;Hui Fang;Hong Cheng
中科院分区：
文献类型：
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作者：
Wei Zheng;Xuan-Yi Wang;Hui Fang;Hong Cheng
传统的信息检索模型不一定能为用户提供最佳的搜索体验，因为排名靠前的文档可能包含相同的相关信息，即查询的相同子主题。搜索结果多样化的目标是返回的搜索结果不仅与查询相关，而且涵盖了不同的子主题。因此，子主题建模是搜索结果多样化的一个重要研究课题。本文提出了一种基于模式的从检索文档中提取子主题的新方法。基本思想是显式地将查询子主题建模为相关文档中语义上有意义的文本单元。我们应用频繁模式挖掘算法从检索到的文档中有效地提取这些文本单元（模式）。然后，我们用单一模式对查询子主题建模，并根据子主题与查询的相似度对子主题进行排序。然后使用这些基于模式的子主题使搜索结果多样化。
Traditional information retrieval models do not necessarily provide users with optimal search experience because the top ranked documents may contain the same piece of relevant information, i.e., the same subtopic of a query. The goal of search result diversification is to return search results that not only are relevant to the query but also cover different subtopics. Therefore, the subtopic modeling is an important research topic in search result diversification. In this paper, we propose a novel pattern based method to extract subtopics from retrieved documents. The basic idea is to explicitly model a query subtopic as a semantically meaningful text unit in relevant documents. We apply a frequent pattern mining algorithm to efficiently extract these text units (patterns) from retrieved documents. We then model a query subtopic with a single pattern and rank subtopics based on their similarity with the query. These pattern based subtopics are then used to diversify search results.