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.
CITATION STYLE
Maleszka, B. (2019). A framework for research publication recommendation system. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11683 LNAI, pp. 167–178). Springer Verlag. https://doi.org/10.1007/978-3-030-28377-3_14
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