DocumentCode
589255
Title
Venue Recommendation: Submitting Your Paper with Style
Author
Zaihan Yang ; Davison, Brian D.
Author_Institution
Dept. of Comput. Sci. & Eng., Lehigh Univ., Bethlehem, PA, USA
Volume
1
fYear
2012
fDate
12-15 Dec. 2012
Firstpage
681
Lastpage
686
Abstract
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 Cite Seer digital libraries demonstrate that our approach can provide effective recommendations.
Keywords
collaborative filtering; digital libraries; publishing; recommender systems; ACM; CiteSeer digital libraries; collaborative-filtering-based recommendation system; conferences; journals; publications; venue recommendation; venue recommendations; workshops; writing formats; writing-style information; Feature extraction; Optimization; Publishing; Recommender systems; Syntactics; Vectors; Writing; collaborative-filtering; features; recommendation;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Applications (ICMLA), 2012 11th International Conference on
Conference_Location
Boca Raton, FL
Print_ISBN
978-1-4673-4651-1
Type
conf
DOI
10.1109/ICMLA.2012.127
Filename
6406648
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