Abstract
Recommendation systems provide an approach to facilitate the user’s desire. It is helpful in recommending the things from various domains. Researchers express their ideas and experience in an academic article for the research community. However, they have ample of options when they aspire to publish. At times, they end up with incorrect submission resulting in waste of time and effort of editor as well as himself. Journal selection has been a very tedious task for the novice authors. In this paper, Journal Recommendation System (JRS) is proposed, which will solve the problem of publication for many authors. Content-based filtering method is used for this purpose. The dataset used is prepared by the authors and distance algorithm is used for recommendation.
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Jain, S., Khangarot, H., Singh, S. (2019). Journal Recommendation System Using Content-Based Filtering. In: Kalita, J., Balas, V., Borah, S., Pradhan, R. (eds) Recent Developments in Machine Learning and Data Analytics. Advances in Intelligent Systems and Computing, vol 740. Springer, Singapore. https://doi.org/10.1007/978-981-13-1280-9_9
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DOI: https://doi.org/10.1007/978-981-13-1280-9_9
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