• 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