Academic Article Recommendation Using Multiple Perspectives
Information Retrieval
2024-07-09 v1
Abstract
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.
Cite
@article{arxiv.2407.05836,
title = {Academic Article Recommendation Using Multiple Perspectives},
author = {Kenneth Church and Omar Alonso and Peter Vickers and Jiameng Sun and Abteen Ebrahimi and Raman Chandrasekar},
journal= {arXiv preprint arXiv:2407.05836},
year = {2024}
}