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.
References
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE TKDE 17(6), 734–749 (2005)
Agarwal, N., Haque, E., Liu, H., Parsons, L.: Research paper recommender systems: a subspace clustering approach. In: Fan, W., Wu, Z., Yang, J. (eds.) WAIM 2005. LNCS, vol. 3739, pp. 475–491. Springer, Heidelberg (2005)
Bast, H., Weber, I.: Type less, find more: fast autocompletion search with a succinct index. In: SIGIR, Seattle, WA, pp. 364–371. ACM (2006)
Celma, Ò.: Music Recommendation: A multi-faceted approach. Master’s thesis (2006)
Chakraborty, T., Modani, N., Narayanam, R., Nagar, S.: Discern: a diversified citation recommendation system for scientific queries. In: ICDE, Seoul, pp. 555–566 (2015)
Councill, I.G., Giles, C.L., Kan, M.Y.: Parscit: an open-source CRF reference string parsing package. In: LREC, Marrakech, Morocco (2008)
Diederich, J., Balke, W.T., Thaden, U.: Demonstrating the semantic growbag: automatically creating topic facets for facetedDBLP. In: JCDL, pp. 505–505. ACM, New York (2007)
Gipp, B., Beel, J., Hentschel, C.: Scienstein: a research paper recommender system. In: ICETiC, Virudhunagar, India, pp. 309–315 (2009)
He, Q., Pei, J., Kifer, D., Mitra, P., Giles, L.: Context-aware citation recommendation. In: WWW, pp. 421–430. ACM, New York (2010)
Hearst, M.A.: Design recommendations for hierarchical faceted search interfaces. In: Proceedings of SIGIR 2006, Workshop on Faceted Search, Seattle, Washington, pp. 26–30 (2006)
Hu, Z., Chen, C., Liu, Z.: Where are citations located in the body of scientific articles? A study of the distributions of citation locations. J. Informetrics 7(4), 887–896 (2013)
Jochim, C., Schütze, H.: Towards a generic and flexible citation classifier based on a faceted classification scheme. In: COLING, Mumbai, India, pp. 1343–1358 (2012)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: SIGKDD, New York, USA, pp. 426–434 (2008)
Lee, C.P., Lin, C.J.: Large-scale linear rankSVM. Neural Comput. 26(4), 781–817 (2014)
Lee, J., Lee, K., Kim, J.G.: Personalized academic research paper recommendation system. CoRR abs/1304.5457 (2013)
Liang, Y., Li, Q., Qian, T.: Finding relevant papers based on citation relations. In: Wang, H., Li, S., Oyama, S., Hu, X., Qian, T. (eds.) WAIM 2011. LNCS, vol. 6897, pp. 403–414. Springer, Heidelberg (2011)
Manning, C., Klein, D.: Optimization, maxent models, and conditional estimation without magic. In: NAACL, p. 8. ACL, Stroudsburg (2003)
Nanba, H., Okumura, M.: Towards multi-paper summarization using reference information. In: IJCAI, pp. 926–931. Morgan Kaufmann, Stockholm (1999)
Pan, J.Y., Yang, H.J., Faloutsos, C., Duygulu, P.: Automatic multimedia cross-modal correlation discovery. In: SIGKDD, Seattle, WA, pp. 653–658 (2004)
Peng, T., Seng-cho, T.: itrustu: a blog recommender system based on multi-faceted trust and collaborative filtering. In: SAC, Hawaii, USA, pp. 1278–1285. ACM (2009)
Pihur, V., Datta, S., Datta, S.: Rankaggreg, an R package for weighted rank aggregation. BMC Bioinform. 10(1), 62 (2009)
Radev, D., Muthukrishnan, P., Qazvinian, V., Abu-Jbara, A.: The ACL anthology network corpus. In: LREC, pp. 1–26 (2013)
Sacco, G.M., Tzitzikas, Y.: Dynamic Taxonomies and Faceted Search: Theory, Practice, and Experience, 1st edn. Springer, Heidelberg (2009)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW, pp. 285–295. ACM, New York (2001)
Sugiyama, K., Kan, M.Y.: Scholarly paper recommendation via user’s recent research interests. In: JCDL, pp. 29–38. ACM, New York (2010)
Tunkelang, D.: Dynamic category sets: an approach for faceted search. In: ACM SIGIR. ACM, New York (2006)
Vallet, D., Halvey, M., Hannah, D., Jose, J.M.: A multi faceted recommendation approach for explorative video retrieval tasks. In: IUI, New York, USA, pp. 389–392 (2010)
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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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