Paper (6 pages)
Official published version: http://dx.doi.org/10.1109/ICMLA.2012.127 Author's version: PDF (108KB)
One of the principal goals for most research scien- tists 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.
In Proceedings of the 11th International Conference on Machine Learning and Applications (ICMLA), pages 681-686, Boca Raton, FL, December 2012.
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