Abstract
In this paper, we present \(\mathsf {FairScholar}\), a novel scientific paper recommendation system that aims at balancing both relevance and diversity while searching for research papers in response to keyword queries. Our system performs a vertex reinforced random-walk, a time heterogeneous random-walk on the citation graph of papers in order to factor in diversity while serving recommendations. To incorporate semantically similar items in the search results, it uses a query expansion step that finds similar keywords using community detection. An online demo of our search engine is available at http://www.cnergres.iitkgp.ac.in/FairScholar/.
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Anand, A., Chakraborty, T., Das, A. (2017). FairScholar: Balancing Relevance and Diversity for Scientific Paper Recommendation. In: Jose, J., et al. Advances in Information Retrieval. ECIR 2017. Lecture Notes in Computer Science(), vol 10193. Springer, Cham. https://doi.org/10.1007/978-3-319-56608-5_76
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DOI: https://doi.org/10.1007/978-3-319-56608-5_76
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Publisher Name: Springer, Cham
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Online ISBN: 978-3-319-56608-5
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