What is xLearn?

xLearn is a high performance, easy-to-use, and scalable machine learning package that contains linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM), all of which can be used to solve large-scale machine learning problems. xLearn is especially useful for solving machine learning problems on large-scale sparse data. Many real world datasets deal with high dimensional sparse feature vectors like a recommendation system where the number of categories and users is on the order of millions. In that case, if you are the user of liblinear, libfm, and libffm, now xLearn is your another better choice.

Get Started! (English)

Get Started! (中文)

Performance

xLearn is developed by high-performance C++ code with careful design and optimizations. Our system is designed to maximize CPU and memory utilization, provide cache-aware computation, and support lock-free learning. By combining these insights, xLearn is 5x-13x faster compared to similar systems.

Ease-of-use

xLearn does not rely on any third-party library and users can just clone the code and compile it by using cmake. Also, xLearn supports very simple Python and CLI interface for data scientists, and it also offers many useful features that have been widely used in machine learning and data mining competitions, such as cross-validation, early-stop, etc.

Scalability

xLearn can be used for solving large-scale machine learning problems. xLearn supports out-of-core training, which can handle very large data (TB) by just leveraging the disk of a PC.

How to Contribute

xLearn has been developed and used by many active community members. Your help is very valuable to make it better for everyone.

Please contribute if you find any bug in xLearn.
Contribute new features you want to see in xLearn.
Contribute to the tests to make it more reliable.
Contribute to the documents to make it clearer for everyone.
Contribute to the examples to share your experience with other users.
Open issue if you met problems during development.

Note that, please post iusse and contribution in English so that everyone can get help from them.

What's New

2019-10-13 Andrew Kane add Ruby bindings for xLearn!
2019-4-25 xLearn 0.4.4 version release. Main update:
- Support Python DMatrix
- Better Windows support
- Fix bugs in previous version
2019-3-25 xLearn 0.4.3 version release. Main update:
- Fix bugs in previous version
2019-3-12 xLearn 0.4.2 version release. Main update:
- Release Windows version of xLearn
2019-1-30 xLearn 0.4.1 version release. Main update:
- More flexible data reader
2018-11-22 xLearn 0.4.0 version release. Main update:
- Fix bugs in previous version
- Add online learning for xLearn
2018-11-10 xLearn 0.3.8 version release. Main update:
- Fix bugs in previous version.
- Update early-stop mechanism.
2018-11-08. xLearn gets 2000 star! Congs!
2018-10-29 xLearn 0.3.7 version release. Main update:
- Add incremental Reader, which can save 50% memory cost.
2018-10-22 xLearn 0.3.5 version release. Main update:
- Fix bugs in 0.3.4.
2018-10-21 xLearn 0.3.4 version release. Main update:
- Fix bugs in on-disk training.
- Support new file format.
2018-10-14 xLearn 0.3.3 version release. Main update:
- Fix segmentation fault in prediction task.
- Update early-stop meachnism.
2018-09-21 xLearn 0.3.2 version release. Main update:
- Fix bugs in previous version
- New TXT format for model output
2018-09-08 xLearn uses the new logo:

2018-09-07 The Chinese document is available now!
2018-03-08 xLearn 0.3.0 version release. Main update:
- Fix bugs in previous version
- Solved the memory leak problem for on-disk learning
- Support TXT model checkpoint
- Support Scikit-Learn API
2017-12-18 xLearn 0.2.0 version release. Main update:
- Fix bugs in previous version
- Support pip installation
- New Documents
- Faster FTRL algorithm
2017-11-24 The first version (0.1.0) of xLearn release !

Project Name	Stars	Repos Using This	Packages Using This	Most Recent Commit	Total Releases	Latest Release	Open Issues	License	Language
Ad Papers	3,009			3 years ago			2	mit	Python
Papers on Computational Advertising
Xlearn	3,000	1	11	2 years ago	10	December 04, 2018	191	apache-2.0	C++
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.
Sparrowrecsys	1,634			2 years ago			21	apache-2.0	Python
A Deep Learning Recommender System
Fastfm	955	8	2	2 years ago	8	November 23, 2017	52	other	Python
fastFM: A Library for Factorization Machines
Lightctr	599			4 years ago			1	apache-2.0	C++
Lightweight and Scalable framework that combines mainstream algorithms of Click-Through-Rate prediction based computational DAG, philosophy of Parameter Server and Ring-AllReduce collective communication.
Daisyrec	496			8 months ago	16	August 14, 2022		mit	Python
This is the repository of our article published in RecSys 2020 "Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison" and of several follow-up studies.
Ytk Learn	351			a year ago			n,ull	mit	Java
Ytk-learn is a distributed machine learning library which implements most of popular machine learning algorithms(GBDT, GBRT, Mixture Logistic Regression, Gradient Boosting Soft Tree, Factorization Machines, Field-aware Factorization Machines, Logistic Regression, Softmax).
Attentional_factorization_machine	316			5 years ago			11		Python
TensorFlow Implementation of Attentional Factorization Machine
Polylearn	207			3 years ago			7	bsd-2-clause	Python
A library for factorization machines and polynomial networks for classification and regression in Python.
Rankfm	150			8 months ago	10	June 13, 2020	13	gpl-3.0	Python
Factorization Machines for Recommendation and Ranking Problems with Implicit Feedback Data