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A novel personalized academic venue hybrid recommender

To see his work accepted and published, a researcher should submit it to the most appropriate conferences or journals. When a researcher schedules to submit his paper, it is generally difficult to him to find an upcoming conference that fits his research topics and also his requirements. To tackle this problem, we propose a personalized academic venue recommendation solution related to venues in the computer science field. Since generally researchers who are cited in the references share the same research interests with a target researcher, our approach is based on bibliographic data augmented by citation relationships between papers. The main idea is to recommend venues on the basis of those of his co-authors, co-citers and co-affiliated. The reliability of each researcher is taken into account to make recommendations. Then, call of papers data are used to recommend personalized upcoming conferences to a given researcher. Our hybrid recommendation system is able to filter out irrelevant conferences that do not respond to the researcher's requirements (ranking, publisher, location). The cold start problem for young researchers is also taken into account. Experiments with the bibliographic citation dataset show that our new approach outperforms the standard collaborative filtering and provides accurate recommendation.