PubMed Query Log Analysis and Use in Access Inhancement

生物医学文献检索是获取不断增加的信息的主要入口。PubMed/MEDLINE是用于此目的的最广泛的服务。在过去的几年里，人们一直在努力将新功能纳入PubMed，以促进用户搜索体验（例如相关文章）。 虽然这些功能已被证明是非常成功的，但搜索者表达其信息需求的方式几乎没有改变。通常，用户在搜索框中键入几个词，PubMed就会返回一个结果列表。当搜索结果不能满足他们的兴趣时，他们会在搜索引擎的支持下一次又一次地尝试。我们的目标是通过改善搜索者和PubMed搜索引擎之间的互动来做得更好。PubMed查询日志分析使我们能够通过更好地了解用户的信息需求和搜索策略来实现我们的目标。具体来说，我们正在开发两个新的交互式搜索助手：相关搜索（RQ）和PubMed广告。 RQ是一个通过响应用户输入自动建议替代查询来帮助用户改进搜索的过程。为此，我们已经成功地开发了技术，收集和汇总原始PubMed日志，并确定相关的PubMed查询在不同的用户会话。我们对RQ的部分研究已经部署到PubMed搜索引擎中。初步的实验结果表明，大约8%的时间用户点击细化查询时，建议有前途的和积极的用户反馈。为了开发PubMed Adsa功能，该功能将推荐少量对搜索者高度相关和重要的文章，我们比较和评估了几种现有的相关性排名和查询扩展算法，这些算法传统上被认为是提高检索效率的有用技术。然而，在PubMed检索的背景下，现有技术本身都无法找到值得注意的PubMed广告文章。目前的工作重点是寻找额外的证据，如文章的使用历史，并建立一个基于证据的模型，可以综合的方式整合文章的各个方面。 日志分析不仅可以帮助搜索作为一个整体，它可以发挥重要作用，在开发工具，以改善搜索的个人基础上，以及。通过我们对PubMed查询日志的分析，我们能够更好地了解每个用户的信息需求。因此，我们可以为每个PubMed用户建立一个独特的配置文件。这将使我们能够在PubMed中提供个性化的搜索建议。例如，如果用户被识别为乳腺癌研究人员，则可以建议MEDLINE中有关乳腺癌研究和治疗的任何新闻。目前，我们正在开发计算技术，根据一个人输入的查询和他/她查看的文档来描述个人用户的特征。

Biomedical literature search is the main entry point for an ever-increasing range of information. PubMed/MEDLINE is the most widely used service for this purpose. During the past few years, there have been efforts to incorporate new features into PubMed to facilitate the user search experience (e.g. Related Articles). Although those features have proved to be highly successful, the ways that searchers express their information needs have changed very little. Typically, users type a few words into the search box, and PubMed returns a list of results. When the search results fail to satisfy their interests, they try again and again with little support from the search engine. We aim to do better than this by improving the interaction between searchers and the PubMed search engine. PubMed query log analysis allows us to approach our objectives by better understanding users information needs and search strategies. Specifically, we are developing two new interactive search assistants: Related Queries (RQ) and PubMed Ads. RQ is a process to help users refine their search by automatically suggesting alternative queries in response to a user input. To this end, we have successfully developed techniques to collect and aggregate raw PubMed logs and to identify related PubMed queries in different user sessions. Some parts of our research on RQ have already been deployed into the live PubMed search engine. Preliminary experimental results show that approximately 8% of the time users clicked on refined queries when available, suggesting promising and positive user feedback. Toward the development of the PubMed Adsa feature that will recommend a small number of articles that are highly relevant and important to the searcherswe have compared and evaluated several existing relevance ranking and query expansion algorithms, traditionally known as useful techniques for boosting retrieval effectiveness. However, in the context of PubMed search, none of the existing techniques itself was able to find noteworthy articles for PubMed Ads. Current work is focused on finding additional evidence such as articles usage history, and building an evidence-based model that can integrate all aspects of an article in a comprehensive manner. Not only can log analysis help search as a whole, it can play an important role in developing tools for improving search on an individual basis as well. Through our analysis of PubMed query logs, we are able to better understand each individual users information needs. As a result, we could build a unique profile for each PubMed user. This would allow us to provide personalized search recommendations in PubMed. For instance, if a user is identified as a breast cancer researcher, then any news on breast cancer research and treatment in MEDLINE could be suggested. Currently, we are developing computational techniques to characterize individual users based on both the queries a person entered and documents s/he viewed.