计算机科学
情报检索
图形
推荐系统
领域(数学)
引用
数据科学
万维网
理论计算机科学
数学
纯数学
作者
Fanqi Meng,Dehong Gao,Wenjie Li,Xu Sun,Yuexian Hou
标识
DOI:10.1145/2505515.2507831
摘要
With the tremendous amount of research publications, it has become increasingly important to provide a researcher with a rapid and accurate recommendation of a list of reference papers about a research field or topic. In this paper, we propose a unified graph model that can easily incorporate various types of useful information (e.g., content, authorship, citation and collaboration networks etc.) for efficient recommendation. The proposed model not only allows to thoroughly explore how these types of information can be better combined, but also makes personalized query-oriented reference paper recommendation possible, which as far as we know is a new issue that has not been explicitly addressed in the past. The experiments have demonstrated the clear advantages of personalized recommendation over non-personalized recommendation.
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