# Scientific Paper Recommendation Systems: a Literature Review of recent Publications

arXiv: https://arxiv.org/abs/2201.00682 · HTML: https://mlsanity.com/paper/2201.00682/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2022.

## Built on by (most cited first, 10 of 10)

- [From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems](https://mlsanity.com/paper/2503.01424/) (2025)
- [GraphMind: Interactive Novelty Assessment System for Accelerating Scientific Discovery](https://mlsanity.com/paper/2510.15706/) (2025)
- [Scholar Inbox: Personalized Paper Recommendations for Scientists](https://mlsanity.com/paper/2504.08385/) (2025)
- [Temporal Graph Neural Network-Powered Paper Recommendation on Dynamic Citation Networks](https://mlsanity.com/paper/2408.15371/) (2024)
- [appjsonify: An Academic Paper PDF-to-JSON Conversion Toolkit](https://mlsanity.com/paper/2310.01206/) (2023)
- [Movie Recommendation with Poster Attention via Multi-modal Transformer Feature Fusion](https://mlsanity.com/paper/2407.09157/) (2024)
- [Preface to the Special Issue of the TAL Journal on Scholarly Document Processing](https://mlsanity.com/paper/2506.03587/) (2025)
- [MIARec: Mutual-influence-aware Heterogeneous Network Embedding for Scientific Paper Recommendation](https://mlsanity.com/paper/2510.12054/) (2025)
- [The Quest for Reliable Metrics of Responsible AI](https://mlsanity.com/paper/2510.26007/) (2025)
- [Enhancing Academic Paper Recommendations Using Fine-Grained Knowledge Entities and Multifaceted Document Embeddings](https://mlsanity.com/paper/2601.19513/) (2026)

## Abstract (arXiv)

Scientific writing builds upon already published papers. Manual identification of publications to read, cite or consider as related papers relies on a researcher's ability to identify fitting keywords or initial papers from which a literature search can be started. The rapidly increasing amount of papers has called for automatic measures to find the desired relevant publications, so-called paper recommendation systems. As the number of publications increases so does the amount of paper recommendation systems. Former literature reviews focused on discussing the general landscape of approaches throughout the years and highlight the main directions. We refrain from this perspective, instead we only consider a comparatively small time frame but analyse it fully. In this literature review we discuss used methods, datasets, evaluations and open challenges encountered in all works first released between January 2019 and October 2021. The goal of this survey is to provide a comprehensive and complete overview of current paper recommendation systems.