tree_enhanced_embedding_model
Explainable REcommender
An explainable recommendation framework combining embedding-based and tree-based models for transparent and interpretable recommendations.
TEM: Tree-enhanced Embedding Model for Explainable Recommendation, WWW2018
74 stars
3 watching
15 forks
last commit: over 7 years agoLinked from 1 awesome list
attention-mechanismdecision-treesexplainable-recommendationswww2018xgboost
xnuohz/awesome-drug-discoveryRelated projects:
| Repository | Description | Stars |
|---|---|---|
 modeloriented/dalex | A tool to help understand and explain the behavior of complex machine learning models | 1,390 |
 explainx/explainx | A framework to explain and debug blackbox machine learning models with a single line of code. | 419 |
| modeloriented/modelstudio | A tool for creating interactive, model-agnostic explanations of machine learning models in R | 328 |
 alibaba/easyrec | A framework for building and deploying scalable recommendation algorithms | 1,814 |
 pbiecek/xaiaterum2020 | An R package and workshop materials for explaining machine learning models using explainable AI techniques | 52 |
 thomasp85/lime | An R package for providing explanations of predictions made by black box classifiers. | 486 |
| modeloriented/ibreakdown | A tool for explaining predictions from machine learning models by attributing them to specific input variables and their interactions. | 82 |
 jalammar/ecco | An interactive visualization library for exploring and understanding transformer-based language models | 1,986 |
 neulab/explainaboard | An interactive tool to analyze and compare the performance of natural language processing models | 362 |
 lantanacamara/lightgbmexplainer | An R package to provide interpretability features for LightGBM models. | 23 |
 muesli/regommend | A simple recommendation system built on top of Go, allowing users to create tables with user ratings and retrieve personalized recommendations. | 314 |
 marcotcr/anchor | Provides a method to generate explanations for predictions made by any black box classifier. | 798 |
 applieddatasciencepartners/xgboostexplainer | Provides tools to understand and interpret the decisions made by XGBoost models in machine learning | 253 |
 interpretml/dice | Provides counterfactual explanations for machine learning models to facilitate interpretability and understanding. | 1,373 |
 giuseppec/iml | Provides methods to interpret and explain the behavior of machine learning models | 494 |