  For many years, achievements and discoveries made by scientists are made
aware through research papers published in appropriate journals or conferences.
Often, established scientists and especially newbies are caught up in the
dilemma of choosing an appropriate conference to get their work through. Every
scientific conference and journal is inclined towards a particular field of
research and there is a vast multitude of them for any particular field.
Choosing an appropriate venue is vital as it helps in reaching out to the right
audience and also to further one's chance of getting their paper published. In
this work, we address the problem of recommending appropriate conferences to
the authors to increase their chances of acceptance. We present three different
approaches for the same involving the use of social network of the authors and
the content of the paper in the settings of dimensionality reduction and topic
modeling. In all these approaches, we apply Correspondence Analysis (CA) to
derive appropriate relationships between the entities in question, such as
conferences and papers. Our models show promising results when compared with
existing methods such as content-based filtering, collaborative filtering and
hybrid filtering.
