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Click data as implicit relevance feedback in web search

机译：点击数据作为网络搜索中的隐式相关性反馈

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摘要

Search sessions consist of a person presenting a query to a search engine, followed by that person examining the search results, selecting some of those search results for further review, possibly following some series of hyperlinks, and perhaps backtracking to previously viewed pages in the session. The series of pages selected for viewing in a search session, sometimes called the click data, is intuitively a source of relevance feedback information to the search engine. We are interested in how that relevance feedback can be used to improve the search results quality for all users, not just the current user. For example, the search engine could learn which documents are frequently visited when certain search queries are given. In this article, we address three issues related to using click data as implicit relevance feedback: (1) How click data beyond the search results page might be more reliable than just the clicks from the search results page; (2) Whether we can further subselect from this click data to get even more reliable relevance feedback; and (3) How the reliability of click data for relevance feedback changes when the goal becomes finding one document for the user that completely meets their information needs (if possible). We refer to these documents as the ones that are strictly relevant to the query. Our conclusions are based on empirical data from a live website with manual assessment of relevance. We found that considering all of the click data in a search session as relevance feedback has the potential to increase both precision and recall of the feedback data. We further found that, when the goal is identifying strictly relevant documents, that it could be useful to focus on last visited documents rather than all documents visited in a search session.
机译：搜索会话由一个人向搜索引擎提出查询，然后由该人检查搜索结果，选择其中一些搜索结果进行进一步审核（可能跟随某些系列的超链接）以及可能回溯到会话中以前查看的页面组成。在搜索会话中选择进行查看的一系列页面（有时称为点击数据）在直观上是搜索引擎相关性反馈信息的来源。我们对如何将相关性反馈用于改善所有用户（而不仅仅是当前用户）的搜索结果质量感兴趣。例如，当给出某些搜索查询时，搜索引擎可以了解哪些文档经常被访问。在本文中，我们解决了与使用点击数据作为隐式相关性反馈相关的三个问题：（1）搜索结果页面之外的点击数据如何比仅来自搜索结果页面的点击更可靠； （2）是否可以进一步从该点击数据中进一步选择，以获得更可靠的相关性反馈； （3）当目标成为为用户找到完全满足其信息需求的文档（如果可能）时，针对相关反馈的点击数据的可靠性将如何变化。我们将这些文档称为与查询严格相关的文档。我们的结论基于来自实时网站的经验数据，并通过人工评估了相关性。我们发现，将搜索会话中的所有点击数据视为相关性反馈有可能提高反馈数据的准确性和查全率。我们还发现，当目标是确定严格相关的文档时，集中精力最后访问的文档而不是搜索会话中访问的所有文档可能会很有用。

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