A query suggestion method based on random walk and topic concepts

2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) Pub Date : 2017-05-01 DOI:10.1109/ICIS.2017.7960002

Jiawei Liu, Qingshan Li, Yishuai Lin, Yingjian Li

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引用次数: 6

Abstract

Related query suggestion is very important for search engines. Users could find required information more quickly and accurately with the help of query suggestions, which could greatly improve users' search experience. Thus, query suggestion technology has become a research hotspot in the field of the search engine. Most of existing methods focused on the query log data to mine related queries. However, some of the query log data exist relatively sparse characteristics and have some interferential noise data. Besides, the method that only focus on query log trend to fail to consider the user's initial query intention. These shortages would reduce the accuracy of the recommendation. Thus, this paper proposes a query suggestion method based on random walk and topic concepts (QuS-RWTC). The method is based on the query log data and suggestions from other mature search engines, which could make the suggestions more comprehensive and obtain a higher coverage. In addition, the paper further executes procedures of topic concepts to re-order the candidate queries, which make the suggestions more accurate, since they are more satisfied to the user's initial intention. The results prove the excellent performance of QuS-RWTC method compared with traditional methods and validate the importance of topic concepts.

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一种基于随机漫步和主题概念的查询建议方法

相关查询建议对于搜索引擎来说是非常重要的。在查询建议的帮助下，用户可以更快、更准确地找到需要的信息，极大地提高了用户的搜索体验。因此，查询建议技术已成为搜索引擎领域的一个研究热点。现有的方法大多集中在查询日志数据上挖掘相关查询。然而，一些查询日志数据存在相对稀疏的特征，并且存在一些干扰噪声数据。此外，只关注查询日志趋势的方法没有考虑用户的初始查询意图。这些不足将降低建议的准确性。为此，本文提出了一种基于随机漫步和主题概念的查询建议方法(QuS-RWTC)。该方法基于查询日志数据和其他成熟搜索引擎的建议，可以使建议更全面，覆盖率更高。此外，本文进一步执行主题概念流程对候选查询进行重新排序，使得建议更加准确，更符合用户的初衷。结果表明，与传统方法相比，QuS-RWTC方法具有优异的性能，并验证了主题概念的重要性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS)

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