# Science Concierge: A fast content-based recommendation system for scientific publications

arXiv: https://arxiv.org/abs/1604.01070 · HTML: https://mlsanity.com/paper/1604.01070/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2016.

## Built on by (most cited first, 4 of 4)

- [SemCSE: Semantic Contrastive Sentence Embeddings Using LLM-Generated Summaries For Scientific Abstracts](https://mlsanity.com/paper/2507.13105/) (2025)
- [GotFunding: A grant recommendation system based on scientific articles](https://mlsanity.com/paper/2405.12840/) (2024)
- [Movie Recommendation with Poster Attention via Multi-modal Transformer Feature Fusion](https://mlsanity.com/paper/2407.09157/) (2024)
- [Paper Quality Assessment based on Individual Wisdom Metrics from Open Peer Review](https://mlsanity.com/paper/2501.13014/) (2025)

## Abstract (arXiv)

Finding relevant publications is important for scientists who have to cope with exponentially increasing numbers of scholarly material. Algorithms can help with this task as they help for music, movie, and product recommendations. However, we know little about the performance of these algorithms with scholarly material. Here, we develop an algorithm, and an accompanying Python library, that implements a recommendation system based on the content of articles. Design principles are to adapt to new content, provide near-real time suggestions, and be open source. We tested the library on 15K posters from the Society of Neuroscience Conference 2015. Human curated topics are used to cross validate parameters in the algorithm and produce a similarity metric that maximally correlates with human judgments. We show that our algorithm significantly outperformed suggestions based on keywords. The work presented here promises to make the exploration of scholarly material faster and more accurate.