Venue Recommendation: Submitting your Paper with Style

被引:50
|
作者
Yang, Zaihan [1 ]
Davison, Brian D. [1 ]
机构
[1] Lehigh Univ, Dept Comp Sci & Engn, Bethlehem, PA 18015 USA
来源
2012 11TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2012), VOL 1 | 2012年
基金
美国国家科学基金会;
关键词
D O I
10.1109/ICMLA.2012.127
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
One of the principal goals for most research scientists is to publish. There are many thousands of publications: journals, conferences, workshops, and more, covering different topics and requiring different writing formats. However, when a researcher that is new to a certain research domain finishes the work, it is sometimes difficult to find a proper place to submit the paper. To solve this problem, we provide a collaborative-filtering-based recommendation system that can provide venue recommendations to researchers. In particular, we consider both topic and writing-style information, and differentiate the contributions of different kinds of neighboring papers to make such recommendations. Experiments based on real-world data from ACM and CiteSeer digital libraries demonstrate that our approach can provide effective recommendations.
引用
收藏
页码:681 / 686
页数:6
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