为科研人员定制论文推荐，帮他们从海量文献中快速找到感兴趣内容。
Scholar Inbox: Personalized Paper Recommendations for Scientists
- 基于用户评分训练推荐系统，个性化匹配研究兴趣。
- 使用80万条公开评分数据评估，推荐准确率显著提升。
- 支持冷启动问题解决，适合刚进入新领域的研究人员。
Scholar Inbox 是一个新推出的开放获取平台，旨在帮助研究人员应对科学文献爆炸式增长带来的挑战。平台提供个性化论文推荐、来自开放存取档案（如 arXiv、bioRxiv）的持续更新、可视化论文摘要、语义搜索以及一系列工具，以简化研究工作流程并推动开放科学。其推荐系统基于用户评分训练，确保推荐内容贴合个人兴趣。为改善用户体验，平台还提供「科学地图」，展示各领域研究全景，帮助用户快速探索特定主题。该地图用于缓解推荐系统常见的冷启动问题，并结合主动学习策略，通过迭代邀请用户评分少量论文，实现快速偏好建模。我们在一个包含80万条用户评分的新数据集上评估了推荐系统质量，并进行了大规模用户研究，结果表明系统表现优异。相关数据集已公开。平台网址：https://www.scholar-inbox.com/
原文摘要 · Abstract (English)
Scholar Inbox is a new open-access platform designed to address the challenges researchers face in staying current with the rapidly expanding volume of scientific literature. We provide personalized recommendations, continuous updates from open-access archives (arXiv, bioRxiv, etc.), visual paper summaries, semantic search, and a range of tools to streamline research workflows and promote open research access. The platform's personalized recommendation system is trained on user ratings, ensuring that recommendations are tailored to individual researchers' interests. To further enhance the user experience, Scholar Inbox also offers a map of science that provides an overview of research across domains, enabling users to easily explore specific topics. We use this map to address the cold start problem common in recommender systems, as well as an active learning strategy that iteratively prompts users to rate a selection of papers, allowing the system to learn user preferences quickly. We evaluate the quality of our recommendation system on a novel dataset of 800k user ratings, which we make publicly available, as well as via an extensive user study. https://www.scholar-inbox.com/
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