LibRec (http://www.librec.net) is a Java library for recommender systems (Java version 1.7 or higher required). It implements a suit of state-of-the-art recommendation algorithms. It consists of three major components: Generic Interfaces, Data Structures and Recommendation Algorithms.

Links: Home | Getting Started | Algorithms | Examples | Demo | Datasets

• Cross-platform: as a Java software, LibRec can be easily deployed and executed in any platforms, including MS Windows, Linux and Mac OS.
• Fast execution: LibRec runs much faster than other libraries, and a detailed comparison over different algorithms on various datasets is available via here.
• Easy configuration: LibRec configs recommenders using a configuration file: librec.conf.
• Easy expansion: LibRec provides a set of well-designed recommendation interfaces by which new algorithms can be easily implemented.

• librec-v1.4 (under development, see what's new)
• librec-v1.3
• librec-v1.2
• librec-v1.1
• librec-v1.0

You can use LibRec as a part of your projects, and use the following codes to run a recommender.

public void main(String[] args) throws Exception {

	// config logger
	Logs.config("log4j.xml", true);

	// config recommender
	String configFile = "librec.conf"; 

	// run algorithm
	LibRec librec = new LibRec();
	librec.setConfigFiles(configFile);
	librec.execute(args);
}

Please cite the following papers if LibRec is helpful to your research.

1. 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.

I would like to expression my appreciation to the following people for contributing source codes to LibRec, including Bin Wu, Ge Zhou. I also appreciate many others for reporting bugs and issues, and for providing valuable suggestions and support.

LibRec has been used in the following publications (let me know if your paper is not listed):

1. 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.
2. Z. Sun, G. Guo and J. Zhang, Exploiting Implicit Item Relationships for Recommender Systems, in Proceedings of the 23rd International Conference on User Modeling, Adaptation and Personalization (UMAP), 2015.

LibRec is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License (GPL) as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LibRec is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with LibRec. If not, see http://www.gnu.org/licenses/.