Scholarly Article Recommendation as a Service

Babel is a platform for research in scholaraly article recommendation. Put succinctly, we are creating a platform to provide scholaraly article recommendations as a service, exposed as REST APIs, available for anyone in the world to use, free of charge.

Babel is a research project of the DataLab, part of the Information School at the University of Washington.

Who is using Babel?

Why build this?

We built Babel to solve two issues researchers face when trying to develop new scholarly article recommendation algorithms:

Who is Babel for?

  1. Scientists trying to create better recommendation algorithms for scholarly articles
  2. People who need scholarly articles recommendations
    1. Publishers, who want users to find related content
    2. Developers, looking to build new tools to facilitate the scholarly process

How can I use Babel's recommendations?

If you are an end user looking to access recommendations, try the demonstration website. If you are a publisher or tool developer, check our our API documentation. We publish a Swagger file you can use to build any SDKs you might need. Please send us an email to let us know you are using Babel, and join the announce list to be notified of any potentially breaking API changes. You might also join the discussion list if you are so inclined.

How can I add my recommender to Babel?

Please contact us to discuss how we can add your recommender to Babel.

What's the deal with this site?

This site is a demonstration only, a way to show what could be built (and how to use the APIs we expose). Feel free to use it if you find it uself, and we may develop it further later, but keep in mind it is a demo. Things may break or not look right.


  1. What is the price to use the service? This service is offered free of charges as part of a research project. All we ask is that you provide us with feedback that will help us improve the system.
  2. Why can’t I find my paper? We may not have a dataset containing your work, or we haven't had a chance to add it yet, or our metadata might be messed up. We are adding more datasets all the time, and improving our metadata, so please check back regularly!
  3. How often are the data updated? We plan on continually adding data as it because available for this research project.
  4. Do you plan to incorporate other recommendation algorithms? Yes, we plan on adding other recommendation algorithms. Currently, we have two variants of our Eigenfactor Recommends algorithm (Classic and Expert), but are looking to add other recommenders. If you wish to test your algorithm on Babel please contact us.
  5. Where can I find these recommendations? Currently recommendations are available from our demo website or via REST APIs. In the near future we hope to integrate our recommendation platform with publishers or other projects that have a need for scholarly article recommendations.


Babel is currently operating with six datasets:










Funding and Sponsors

We would like to thank our sponsors at the Metaknowledge Network funded by the Templeton Foundation.


If you have questions, please email Jevin D. West at