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A Leading Java Library for Recommender Systems

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Recommender systems have been well recognized as a typical application of Big Data and Machine Learning. LibRec is a GPL-licensed Java library (Java version 1.7+ required), aiming to solve two classic tasks in recommender systems, i.e., rating prediction and item ranking by implementing a suite of state-of-the-art recommendation algorithms. It has been listed by the RecSys Wiki (see the LibRec page).



More than 70 recommendation algorithms have been implemented, and more will be added in the LibRec.


LibRec has six main components including data split, conversion, similarity, algorithms, evaluators and filters.


LibRec is based on low coupling, flexible and either external textual or internal API configuration.


LibRec has more efficient implementations than other counterparts while producing comparable accuracy.


LibRec can get executed in a few lines of codes, and a number of demos are provided for easy start.


LibRec provides a set of recommendation interfaces for easy expansion to implement new recommenders.


   Research Publications   A number of papers adopting LibRec can be found here.

Guibing Guo, Jie Zhang, Zhu Sun and Neil Yorke-Smith, LibRec: A Java Library for Recommender Systems, in Posters, Demos, Late-breaking Results and Workshop Proceedings of the 23rd Conference on User Modelling, Adaptation and Personalization (UMAP), 2015.
G. Guo, J. Zhang and N. Yorke-Smith, TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings, in Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI), 2015, 123-129.
G. Guo, J. Zhang and N. Yorke-Smith, A Novel Recommendation Model Regularized with User Trust and Item Ratings, IEEE Transactions on Knowledge and Data Engineering (TKDE), 28(7), 1607-1620, 2016.

   Industrial Application     Please contact us for an industrial application of LibRec via guogb@swc.neu.edu.cn

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