Scholar Inbox: Personalized Paper Recommendations for Scientists

• URL: http://arxiv.org/abs/2504.08385v1
• Date: Fri, 11 Apr 2025 09:37:48 GMT
• Title: Scholar Inbox: Personalized Paper Recommendations for Scientists
• Authors: Markus Flicke, Glenn Angrabeit, Madhav Iyengar, Vitalii Protsenko, Illia Shakun, Jovan Cicvaric, Bora Kargi, Haoyu He, Lukas Schuler, Lewin Scholz, Kavyanjali Agnihotri, Yong Cao, Andreas Geiger,
• Abstract summary: 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.<n>We provide personalized recommendations, continuous updates from open-access archives, visual paper summaries, semantic search, and a range of tools to streamline research and promote open research access.<n>The platform's personalized recommendation system is trained on user ratings, ensuring that recommendations are tailored to individual researchers' interests.
• Score: 20.51711919521527
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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