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RecoLibry Suite: a set of intelligent tools for the development of recommender systems

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Abstract

Recommendation systems are a key part of almost every modern consumer website. Recommender systems include techniques to filter, explore and rank a huge amount of information and items according to the user’s current interests, and the similarity among users and items. Designing and implementing a recommender system usually requires high programming and machine learning skills. To alleviate these processes we present RecoLibry Suite: a set of intelligent tools to assist different types of users on the development of recommender systems. RecoLibry Suite supports not only the design and development of recommender systems but also its deployment as software as a service. We have evaluated the usability of the proposed tools with real users.

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Notes

  1. http://gaia.fdi.ucm.es.

  2. https://www.seldon.io/.

  3. An interactive visualisation of the ontology is available at http://gaia.fdi.ucm.es/ontologies/reconto.

  4. https://github.com/google/guice.

  5. https://github.com/UCM-GAIA/RecoLibry-Core.

  6. https://grouplens.org/datasets/movielens/.

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Authors and Affiliations

  1. Department of Software Engineering and Artificial Intelligence, Universidad Complutense de Madrid, Madrid, Spain

    Jose Luis Jorro-Aragoneses, Belén Díaz-Agudo, Juan A. Recio-García & Guillermo Jimenez-Díaz

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  1. Jose Luis Jorro-Aragoneses
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  2. Belén Díaz-Agudo
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  3. Juan A. Recio-García
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  4. Guillermo Jimenez-Díaz
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Correspondence to Jose Luis Jorro-Aragoneses.

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Supported by the UCM (Research Group 921330), the Spanish Committee of Economy and Competitiveness (TIN2017-87330-R) and the funding provided by Banco Santander in UCM (CT17/17-CT17/18).

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Jorro-Aragoneses, J.L., Díaz-Agudo, B., Recio-García, J.A. et al. RecoLibry Suite: a set of intelligent tools for the development of recommender systems. Autom Softw Eng 27, 63–89 (2020). https://doi.org/10.1007/s10515-020-00269-4

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