TL;DR
This paper proposes a novel venue recommendation system that estimates the potential citation impact of a paper in different venues using treatment effect estimation, addressing selection bias and unobserved outcomes.
Contribution
It introduces a treatment effect estimation framework to recommend publication venues based on potential impact, overcoming biases in traditional venue recommendation methods.
Findings
Effective venue recommendations based on citation impact potential.
Bias correction improves accuracy of impact estimation.
Validated on computer science conference data.
Abstract
Choosing a publication venue for an academic paper is a crucial step in the research process. However, in many cases, decisions are based solely on the experience of researchers, which often leads to suboptimal results. Although there exist venue recommender systems for academic papers, they recommend venues where the paper is expected to be published. In this study, we aim to recommend publication venues from a different perspective. We estimate the number of citations a paper will receive if the paper is published in each venue and recommend the venue where the paper has the most potential impact. However, there are two challenges to this task. First, a paper is published in only one venue, and thus, we cannot observe the number of citations the paper would receive if the paper were published in another venue. Secondly, the contents of a paper and the publication venue are not…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
MethodsCausal inference
