@inproceedings{9da151417ef5442f926b08759e4f80fd,
title = "Elsevier journal finder: Recommending journals for your paper",
abstract = "Rejection is the norm in academic publishing. One of the main reasons for rejections is that the topics of the submitted papers are not relevant to the scope of the journal, even when the papers themselves are excellent. Submission to a journal that fits well with the publication may avoid this issue. A system that is able to suggest journals that have published similar articles to the submitted papers may help authors choose where to submit. The Elsevier journal finder, a freely available online service, is one of the most comprehensive journal recommender systems, covering all scientific domains and more than 2,900 per-reviewed Elsevier journals. The system uses natural language processing for feature generation, and Okapi BM25 matching for the recommendation algorithm. The procedure is to paste text, such as an abstract, and get a list of recommend journals and relevant metadata. The website URL is http://journalfinder.elsevier.com.",
keywords = "Natural language processing, Noun phrase, Okapi BM25, Recommender system, TF-IDF",
author = "Ning Kang and Marius Doornenbal and Bob Schijvenaars",
year = "2015",
month = sep,
day = "16",
doi = "10.1145/2792838.2799663",
language = "English",
series = "RecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems",
publisher = "Association for Computing Machinery, Inc",
pages = "261--264",
booktitle = "RecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems",
note = "null ; Conference date: 16-09-2015 Through 20-09-2015",
}