Abstract
Information overload is one of the main problem of nowadays information retrieval systems. To obtain relevant information or items, many users use recommendation systems which are commonly available: for products in Internet stores, musics, books, etc. Also in the field of research papers it is hard to find relevant items. There exists many scientific search engines that retrieve huge databases to find best papers but it would be comfortable to have an ability to find the best journal or conference where to publish current paper. Every researcher receive many “calls for papers” but many times the propositions are rather random and a little correlated with our research. In this paper we explore possibilities of collaborative filtering and content-based approaches to Publication Recommender System. We have presented a simple case study for a selected group of users.
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This research was partially supported by the Polish Ministry of Science and Higher Education.
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Maleszka, B. (2019). A Framework for Research Publication Recommendation System. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11683. Springer, Cham. https://doi.org/10.1007/978-3-030-28377-3_14
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DOI: https://doi.org/10.1007/978-3-030-28377-3_14
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