Recommendation on Academic Networks using Direction Aware Citation Analysis
Onur K\"u\c{c}\"uktun\c{c}, Erik Saule, Kamer Kaya, \"Umit V., \c{C}ataly\"urek

TL;DR
This paper introduces direction-aware citation-based recommendation algorithms that help researchers find relevant academic papers, venues, and reviewers more efficiently by leveraging citation directionality and relevance feedback.
Contribution
It proposes novel direction-aware citation algorithms for paper, venue, and reviewer recommendation, incorporating relevance feedback to improve search efficiency.
Findings
Algorithms outperform baseline methods in accuracy
Direction awareness improves relevance of recommendations
Recommender system is publicly available online
Abstract
The literature search has always been an important part of an academic research. It greatly helps to improve the quality of the research process and output, and increase the efficiency of the researchers in terms of their novel contribution to science. As the number of published papers increases every year, a manual search becomes more exhaustive even with the help of today's search engines since they are not specialized for this task. In academics, two relevant papers do not always have to share keywords, cite one another, or even be in the same field. Although a well-known paper is usually an easy pray in such a hunt, relevant papers using a different terminology, especially recent ones, are not obvious to the eye. In this work, we propose paper recommendation algorithms by using the citation information among papers. The proposed algorithms are direction aware in the sense that…
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Taxonomy
TopicsComplex Network Analysis Techniques · Recommender Systems and Techniques · Advanced Clustering Algorithms Research
