基于图结构的可解释医学论文推荐系统，兼顾效率与透明度。
Building an Explainable Graph-based Biomedical Paper Recommendation System (Technical Report)
- 构建图神经网络模型，利用文献间关系实现推荐
- 支持3700万篇生物医学文献，计算成本可控
- 生成可理解的推荐理由，适合科研人员使用
数字图书馆为用户提供多种文献探索路径，其中论文推荐可帮助用户发现相关文献。现有方法常因计算成本高且缺乏可解释性而难以应用。为此，我们提出XGPRec——一种基于图结构的可解释推荐方法，并集成至现有生物医学知识发现系统。实验表明，XGPRec可在3700万篇生物医学文献的大型数据集上高效运行，具备良好推荐性能，尤其在概念中心型检索任务中表现优异；其生成的解释在初步用户研究中被证实具有实际帮助。代码已公开，供研究者复用与扩展。
原文摘要 · Abstract (English)
Digital libraries provide different access paths, allowing users to explore their collections. For instance, paper recommendation suggests literature similar to some selected paper. Their implementation is often cost-intensive, especially if neural methods are applied. Additionally, it is hard for users to understand or guess why a recommendation should be relevant for them. That is why we tackled the problem from a different perspective. We propose XGPRec, a graph-based and thus explainable method which we integrate into our existing graph-based biomedical discovery system. Moreover, we show that XGPRec (1) can, in terms of computational costs, manage a real digital library collection with 37M documents from the biomedical domain, (2) performs well on established test collections and concept-centric information needs, and (3) generates explanations that proved to be beneficial in a preliminary user study. We share our code so that user libraries can build upon XGPRec.
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