Recommendation of Scholarly Venues Based on Dynamic User Interests

TL;DR

This paper introduces an adaptive system for recommending scholarly venues based on researchers' reading behaviors, improving relevance and supporting new researchers and venues without prior publication data.

Contribution

The study presents a novel implicit rating technique and a large-scale evaluation demonstrating improved venue recommendations over baseline methods.

Findings

The proposed system outperforms baseline recommendation methods.

It provides relevant venue suggestions for new researchers and emerging venues.

The system offers real-time, interest-aligned recommendations based on reading behavior.

Abstract

The ever-growing number of venues publishing academic work makes it difficult for researchers to identify venues that publish data and research most in line with their scholarly interests. A solution is needed, therefore, whereby researchers can identify information dissemination pathways in order to both access and contribute to an existing body of knowledge. In this study, we present a system to recommend scholarly venues rated in terms of relevance to a given researcher's current scholarly pursuits and interests. We collected our data from an academic social network and modeled researchers' scholarly reading behavior in order to propose a new and adaptive implicit rating technique for venues. We present a way to recommend relevant, specialized scholarly venues using these implicit ratings that can provide quick results, even for new researchers without a publication history and for emerging scholarly venues that do not yet have an impact factor. We performed a large-scale experiment with real data to evaluate the current scholarly recommendation system and showed that our proposed system achieves better results than the baseline. The results provide important up-to-the-minute signals that compared with post-publication usage-based metrics represent a closer reflection of a researcher's interests.