Academic event recommendation based on research similarity and exploring interaction between authors

In this study, a new academic event recommendation method is proposed. This method analyzes author interactions, academic event attendance records, research related, and textual descriptions from attended academic events to measure interaction strength between authors. Experiments on the DBLP dataset and Wiki Calls for Papers (WikiCFP) showed that the proposed method is helpful in improving the accuracy of a recommendation system in comparison with other methods. In addition, this method can be applied to various recommended tasks such as collaboration recommendation, papers recommendation, etc.

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