\"Towards higher relevance and serendipity in scholarly paper recommendation\" by Kazunari Sugiyama and Min-Yen Kan with Martin Vesely as coordinator

Abstract

Finding relevant scholarly papers is an important task for researchers. Such a literature search involves identifying drawbacks in existing works and proposing new approaches that address them. However, the growing number of scientific published papers results in information overload even for simple searches, such that researchers have difficulty in finding papers relevant to their interests. Recommendation systems can help address this problem to find relevant papers efficiently. In this article, we summarize our work on scholarly paper recommendation from both relevance and serendipitous perspectives. Experimental results on a publicly-available scholarly paper recommendation dataset show that our proposed approaches provides promising recommendations for researchers, outperforming the state-of-the-art with statistical significance.

Min-Yen Kan

Associate Professor

WING lead; interests include Digital Libraries, Information Retrieval and Natural Language Processing.