Intent-aware query similarity
Intent-aware query similarity
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DOI：
10.1145/2063576.2063619
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发表时间：
2011-10
期刊：
影响因子：
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通讯作者：
J. Guo;Xueqi Cheng;Gu Xu;Xiaofei Zhu
中科院分区：
文献类型：
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作者：
J. Guo;Xueqi Cheng;Gu Xu;Xiaofei Zhu
查询相似度计算是IR中的一个重要问题，有着广泛的应用，包括查询推荐、查询扩展，甚至广告匹配。现有的关于查询相似性的工作旨在提供单一的相似性度量，而不考虑查询是不明确的并且通常具有多个搜索意图的事实。在本文中，我们认为查询相似性应该根据搜索意图来定义，即所谓的意图感知查询相似性。通过将搜索意图引入查询相似度的计算中，我们可以获得更准确且信息丰富的查询相似度度量，从而有助于各种应用，特别是与多样化相关的应用。具体来说，我们首先识别查询的潜在搜索意图，然后使用意图感知表示来测量不同意图下的查询相似度。采用正则化主题模型，通过使用搜索结果片段中的单词和查询共同点击的正则化来自动学习查询的潜在意图。实验结果证实了意图感知查询相似性对模糊查询的有效性，与传统方法相比，它可以提供明显更好的相似性分数。我们还通过实验验证了意图感知相似性在查询推荐应用中的实用性，它可以以结构化的方式向搜索用户建议不同的查询。
Query similarity calculation is an important problem and has a wide range of applications in IR, including query recommendation, query expansion, and even advertisement matching. Existing work on query similarity aims to provide a single similarity measure without considering the fact that queries are ambiguous and usually have multiple search intents. In this paper, we argue that query similarity should be defined upon search intents, so-called intent-aware query similarity. By introducing search intents into the calculation of query similarity, we can obtain more accurate and also informative similarity measures on queries and thus help a variety of applications, especially those related to diversification. Specifically, we first identify the potential search intents of queries, and then measure query similarity under different intents using intent-aware representations. A regularized topic model is employed to automatically learn the potential intents of queries by using both the words from search result snippets and the regularization from query co-clicks. Experimental results confirm the effectiveness of intent-aware query similarity on ambiguous queries which can provide significantly better similarity scores over the traditional approaches. We also experimentally verified the utility of intent-aware similarity in the application of query recommendation, which can suggest diverse queries in a structured way to search users.