# Scholar Inbox: Personalized Paper Recommendations for Scientists

arXiv: https://arxiv.org/abs/2504.08385 · HTML: https://mlsanity.com/paper/2504.08385/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 46 references that are in the map.
- First posted on arXiv 11 Apr 2025.

## Builds on (most cited first, 14 of 14)

- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://mlsanity.com/paper/2308.03281/) (2023)
- [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://mlsanity.com/paper/2004.07180/) (2020)
- [Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings](https://mlsanity.com/paper/2202.06671/) (2022)
- [OpenResearcher: Unleashing AI for Accelerated Scientific Research](https://mlsanity.com/paper/2408.06941/) (2024)
- [Scientific Paper Recommendation: A Survey](https://mlsanity.com/paper/2008.13538/) (2020)
- [Bursting Scientific Filter Bubbles: Boosting Innovation via Novel Author Discovery](https://mlsanity.com/paper/2108.05669/) (2021)
- [The Matthew effect in empirical data](https://mlsanity.com/paper/1408.5124/) (2014)
- [Scientific Paper Recommendation Systems: a Literature Review of recent Publications](https://mlsanity.com/paper/2201.00682/) (2022)
- [Biases in Scholarly Recommender Systems: Impact, Prevalence, and Mitigation](https://mlsanity.com/paper/2301.07483/) (2023)
- [Chronological Citation Recommendation with Time Preference](https://mlsanity.com/paper/2101.07609/) (2021)
- [Tag-Aware Document Representation for Research Paper Recommendation](https://mlsanity.com/paper/2209.03660/) (2022)
- [Academic Article Recommendation Using Multiple Perspectives](https://mlsanity.com/paper/2407.05836/) (2024)

## Built on by (most cited first, 3 of 3)

- [LMR-BENCH: Evaluating LLM Agent's Ability on Reproducing Language Modeling Research](https://mlsanity.com/paper/2506.17335/) (2025)
- [GraphMind: Interactive Novelty Assessment System for Accelerating Scientific Discovery](https://mlsanity.com/paper/2510.15706/) (2025)
- [Language Models Don't Know What You Want: Evaluating Personalization in Deep Research Needs Real Users](https://mlsanity.com/paper/2603.16120/) (2026)

## Abstract (arXiv)

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/