|Project Name||Stars||Downloads||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|
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.
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.
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.
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.
xLearn has been developed and used by many active community members. Your help is very valuable to make it better for everyone.
Note that, please post iusse and contribution in English so that everyone can get help from them.
2019-4-25 xLearn 0.4.4 version release. Main update:
2019-3-25 xLearn 0.4.3 version release. Main update:
2019-3-12 xLearn 0.4.2 version release. Main update:
2019-1-30 xLearn 0.4.1 version release. Main update:
2018-11-22 xLearn 0.4.0 version release. Main update:
2018-11-10 xLearn 0.3.8 version release. Main update:
2018-11-08. xLearn gets 2000 star! Congs!
2018-10-29 xLearn 0.3.7 version release. Main update:
2018-10-22 xLearn 0.3.5 version release. Main update:
2018-10-21 xLearn 0.3.4 version release. Main update:
2018-10-14 xLearn 0.3.3 version release. Main update:
2018-09-21 xLearn 0.3.2 version release. Main update:
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:
2017-12-18 xLearn 0.2.0 version release. Main update:
2017-11-24 The first version (0.1.0) of xLearn release !