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Recommending journals programmatically

I had been keeping this project under my hat, but I’ve proposed it as a conference talk now, so maybe it’s time to share. I’ve been building a journal recommender tool. It’s aimed at faculty, and is built on data from the Directory of Open Access Journals. The idea is that a faculty member could submit a draft abstract, and the recommender tool would give back a top 5 list of journals that best match that abstract.

The point is that this might help faculty more effectively find a way to identify prospective journals for their work. This could speed up the time-consuming process of manually reading through many articles or abstracts in order to evaluate the fit of the article.

It has been interesting to try to figure out ways to write an effective matching script. The results need to be accurate, and the process needs to be quick enough that it can be delivered on the web. The second of these requirements turns out to be more challenging than the first. I’ve spent quite a bit of time trying to optimize the process, but it’s not there yet. Here is the code, if you are interested: https://github.com/MarkEEaton/doaj. I will follow up with more details as I work out the bugs.

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