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An efficient approach to suggesting topically related web queries using hidden topic model

机译：使用隐藏主题模型建议与主题相关的Web查询的有效方法

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摘要

Keyword-based Web search is a widely used approach for locating information on the Web. However, Web users usually suffer from the difficulties of organizing and formulating appropriate input queries due to the lack of sufficient domain knowledge, which greatly affects the search performance. An effective tool to meet the information needs of a search engine user is to suggest Web queries that are topically related to their initial inquiry. Accurately computing query-to-query similarity scores is a key to improve the quality of these suggestions. Because of the short lengths of queries, traditional pseudo-relevance or implicit-relevance based approaches expand the expression of the queries for the similarity computation. They explicitly use a search engine as a complementary source and directly extract additional features (such as terms or URLs) from the top-listed or clicked search results. In this paper, we propose a novel approach by utilizing the hidden topic as an expandable feature. This has two steps. In the offline model-learning step, a hidden topic model is trained, and for each candidate query, its posterior distribution over the hidden topic space is determined to re-express the query instead of the lexical expression. In the online query suggestion step, alter inferring the topic distribution for an input query in a similar way, we then calculate the similarity between candidate queries and the input query in terms of their corresponding topic distributions; and produce a suggestion list of candidate queries based on the similarity scores. Our experimental results on two real data sets show that the hidden topic based suggestion is much more efficient than the traditional term or URL based approach, and is effective in finding topically related queries for suggestion.
机译：基于关键字的Web搜索是在Web上定位信息的一种广泛使用的方法。但是，由于缺乏足够的领域知识，Web用户通常会难以组织和制定适当的输入查询，这极大地影响了搜索性能。满足搜索引擎用户信息需求的有效工具是建议与他们的初始查询局部相关的Web查询。准确计算查询之间的相似性分数是提高这些建议质量的关键。由于查询长度短，传统的基于伪相关性或隐式相关性的方法扩展了查询的表达式，以进行相似度计算。他们明确地使用搜索引擎作为补充资源，并直接从排名靠前或点击的搜索结果中提取其他功能（例如字词或URL）。在本文中，我们提出了一种利用隐藏主题作为可扩展功能的新颖方法。这有两个步骤。在离线模型学习步骤中，对隐藏主题模型进行了训练，对于每个候选查询，确定其在隐藏主题空间上的后验分布以重新表达查询，而不是词汇表达。在在线查询建议步骤中，以类似方式更改推断输入查询的主题分布，然后根据对应的主题分布计算候选查询和输入查询之间的相似度；并根据相似度得分生成候选查询的建议列表。我们在两个真实数据集上的实验结果表明，基于隐藏主题的建议比传统的基于术语或基于URL的方法要有效得多，并且可以有效地找到与建议相关的局部查询。

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