Factorization Machines for Recommendation and Ranking Problems with Implicit Feedback Data
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Updated
Aug 14, 2024 - Python
Factorization Machines for Recommendation and Ranking Problems with Implicit Feedback Data
Easy-to-use,Modular and Extendible package of deep-learning based CTR models .
DeepTables: Deep-learning Toolkit for Tabular data
Official code for "DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation" (TPAMI2022) and "Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison" (RecSys2020)
A highly-modularized and recommendation-efficient recommendation library based on PyTorch.
Fast and accurate machine learning on sparse matrices - matrix factorizations, regression, classification, top-N recommendations.
Fwumious Wabbit, fast on-line machine learning toolkit written in Rust
基于混合推荐算法的文学作品推荐系统-算法后端
A deep matching model library for recommendations & advertising. It's easy to train models and to export representation vectors which can be used for ANN search.
This repo includes some graph-based CTR prediction models and other representative baselines.
Factorization Machine models in PyTorch
Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing
A high-performance toolkit for LR/FM training on large-scale sparse data.
Building recommender systems in Julia
A library of recommender systems with collaborative, content-based filtering, and hybrid models.
零售电商客户流失模型,基于tensorflow,xgboost4j-spark,spark-ml实现LR,FM,GBDT,RF,进行模型效果对比,离线/在线部署方式总结
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.
My simplest implementations of common ML algorithms
The primary objective of this study is to explore the feasibility of using machine learning algorithms to classify health insurance plans based on their coverage for routine dental services. To achieve this, I used six different classification algorithms: LR, DT, RF, GBT, SVM, FM(Tech: PySpark, SQL, Databricks, Zeppelin books, Hadoop, Spark-Submit)
a simple yet versatile recommendation systems library in python
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