# Academic Article Recommendation Using Multiple Perspectives

arXiv: https://arxiv.org/abs/2407.05836 · HTML: https://mlsanity.com/paper/2407.05836/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2024.

## Built on by (most cited first, 1 of 1)

- [Scholar Inbox: Personalized Paper Recommendations for Scientists](https://mlsanity.com/paper/2504.08385/) (2025)

## Abstract (arXiv)

We argue that Content-based filtering (CBF) and Graph-based methods (GB) complement one another in Academic Search recommendations. The scientific literature can be viewed as a conversation between authors and the audience. CBF uses abstracts to infer authors' positions, and GB uses citations to infer responses from the audience. In this paper, we describe nine differences between CBF and GB, as well as synergistic opportunities for hybrid combinations. Two embeddings will be used to illustrate these opportunities: (1) Specter, a CBF method based on BERT-like deepnet encodings of abstracts, and (2) ProNE, a GB method based on spectral clustering of more than 200M papers and 2B citations from Semantic Scholar.