利用PubMed用户查询日志对推荐相似文章进行事后解释
Harnessing PubMed User Query Logs for Post Hoc Explanations of Recommended Similar Articles
AI总结：
本研究利用PubMed用户查询日志构建PubCLogs数据集，训练基于Transformer的HSAT模型，通过识别相似文章标题中的相关标记来解释文献推荐，显著优于多个基线模型。
摘要：
基于参考文献查找相关文章是科学研究中不可或缺的一部分。PubMed与许多学术搜索引擎一样，具有“相似文章”功能，可推荐与用户当前查看文章相关的文章。解释推荐项对用户，尤其是在文献检索过程中，具有极大的实用性。由于每年有超过一百万篇生物医学论文发表，解释推荐的相似文章将有助于研究人员和临床医生检索相关文献。然而，当前大多数文献推荐系统缺乏对其推荐建议的解释。我们采用事后（post hoc）方法，通过识别相似文章标题中的相关标记来解释推荐。我们的主要贡献是构建了PubCLogs数据集，该数据集通过重新利用PubMed用户查询日志中的560万对共点击文章构建而成。利用PubCLogs数据集，我们训练了“高亮相似文章标题”（Highlight Similar Article Title, HSAT）模型，这是一种基于Transformer的模型，旨在根据种子文章的标题和摘要，选择相似文章标题中最相关的部分。HSAT在我们的实证评估中表现出强大的性能，在PubCLogs测试集上达到了91.72%的F1分数，显著优于包括BM25（70.62）、MPNet（67.11）、MedCPT（62.22）、GPT-3.5（46.00）和GPT-4（64.89）在内的多个基线模型。在另一个独立的人工标注测试集上的额外评估进一步验证了HSAT的性能。此外，我们用户研究的参与者表示更偏好HSAT，因为它在简洁性和全面性之间取得了更好的平衡。我们的研究表明，重新利用学术搜索引擎的用户查询日志是训练最先进模型以解释文献推荐的一种有前景的方法。
英文摘要：
Searching for a related article based on a reference article is an integral part of scientific research. PubMed, like many academic search engines, has a "similar articles" feature that recommends articles relevant to the current article viewed by a user. Explaining recommended items can be of great utility to users, particularly in the literature search process. With more than a million biomedical papers being published each year, explaining the recommended similar articles would facilitate researchers and clinicians in searching for related articles. Nonetheless, the majority of current literature recommendation systems lack explanations for their suggestions. We employ a post hoc approach to explaining recommendations by identifying relevant tokens in the titles of similar articles. Our major contribution is building PubCLogs by repurposing 5.6 million pairs of coclicked articles from PubMed's user query logs. Using our PubCLogs dataset, we train the Highlight Similar Article Title (HSAT), a transformer-based model designed to select the most relevant parts of the title of a similar article, based on the title and abstract of a seed article. HSAT demonstrates strong performance in our empirical evaluations, achieving an F1 score of 91.72 percent on the PubCLogs test set, considerably outperforming several baselines including BM25 (70.62), MPNet (67.11), MedCPT (62.22), GPT-3.5 (46.00), and GPT-4 (64.89). Additional evaluations on a separate, manually annotated test set further verifies HSAT's performance. Moreover, participants of our user study indicate a preference for HSAT, due to its superior balance between conciseness and comprehensiveness. Our study suggests that repurposing user query logs of academic search engines can be a promising way to train state-of-the-art models for explaining literature recommendation.
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