Abstract
The overwhelming number of scientific articles over the years calls for smart automatic tools to facilitate the process of literature review. Here, we propose for the first time a framework of faceted recommendation for scientific articles (abbreviated as FeRoSA) which apart from ensuring quality retrieval of scientific articles for a query paper, also efficiently arranges the recommended papers into different facets (categories). Providing users with an interface which enables the filtering of recommendations across multiple facets can increase users’ control over how the recommendation system behaves. FeRoSA is precisely built on a random walk based framework on an induced subnetwork consisting of nodes related to the query paper in terms of either citations or content similarity. Rigorous analysis based an experts’ judgment shows that FeRoSA outperforms two baseline systems in terms of faceted recommendations (overall precision of 0.65). Further, we show that the faceted results of FeRoSA can be appropriately combined to design a better flat recommendation system as well. An experimental version of FeRoSA is publicly available at www.ferosa.org (receiving as many as 170 hits within the first 15 days of launch).
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Notes
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The expert opinion was taken from the annotators, who were later involved in evaluating the systems as discussed in Sect. 5. For a direct reference of a paper, we asked experts whether the reference indicates BG, AA, MD or CM and then compared their opinion with our section annotation (in four categories).
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In the interest of space, the detailed experiments and results of the supervised classification are not presented in this paper.
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This indeed reduced the evaluators’ effort of reading multiple papers.
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Chakraborty, T., Krishna, A., Singh, M., Ganguly, N., Goyal, P., Mukherjee, A. (2016). FeRoSA: A Faceted Recommendation System for Scientific Articles. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J., Wang, R. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2016. Lecture Notes in Computer Science(), vol 9652. Springer, Cham. https://doi.org/10.1007/978-3-319-31750-2_42
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