论文标题
减少查询自动完成中的错误信息
Reducing Misinformation in Query Autocompletions
论文作者
论文摘要
查询自动完成，可以通过建议在下拉框中进行部分键入查询来帮助搜索引擎的用户加快搜索。这些建议的查询自动完成通常基于搜索引擎用户先前输入的大量查询日志。因此，输入的错误信息 - 无论是意外还是故意操纵搜索引擎）可能最终陷入了搜索引擎的建议中，可能会损害组织，个人和人群。本文提出了一种通过从大型Web爬网中提取锚固文本来生成查询自动完成的替代方法，而无需使用查询日志。我们的评估表明，即使查询日志自动完成对较短的查询的表现更好，但锚文本自动完成的内容的表现要超过2个单词或更多查询的查询日志自动完成。
Query autocompletions help users of search engines to speed up their searches by recommending completions of partially typed queries in a drop down box. These recommended query autocompletions are usually based on large logs of queries that were previously entered by the search engine's users. Therefore, misinformation entered -- either accidentally or purposely to manipulate the search engine -- might end up in the search engine's recommendations, potentially harming organizations, individuals, and groups of people. This paper proposes an alternative approach for generating query autocompletions by extracting anchor texts from a large web crawl, without the need to use query logs. Our evaluation shows that even though query log autocompletions perform better for shorter queries, anchor text autocompletions outperform query log autocompletions for queries of 2 words or more.
