rater

rater is a comparative framework for multimodal recommender systems. It was developed to facilitate the designing, comparing, and sharing of recommendation models.

Feature

easy to use, rebuild and compare
SOTA model
classical model and deep model
model has great influence in the industry
model hsa been successfully applied by Google, Alibaba, Baidu and other well-known companies
engineering oriented, not just experimental data validation

Data

ml-1m: http://files.grouplens.org/datasets/movielens/ml-1m.zip
delicious-2k: http://files.grouplens.org/datasets/hetrec2011/hetrec2011-delicious-2k.zip
lastfm-dataset-360K: http://mtg.upf.edu/static/datasets/last.fm/lastfm-dataset-360K.tar.gz
slashdot: http://snap.stanford.edu/data/soc-Slashdot0902.txt.gz
epinions: http://snap.stanford.edu/data/soc-Epinions1.txt.gz
ml-100k: http://files.grouplens.org/datasets/movielens/ml-100k.zip
Criteo(dac full): https://s3-eu-west-1.amazonaws.com/kaggle-display-advertising-challenge-dataset/dac.tar.gz
Criteo(dac sample): http://labs.criteo.com/wp-content/uploads/2015/04/dac_sample.tar.gz

Install

pip3 install rater

git clone https://github.com/shibing624/rater.git
cd rater
python3 setup.py install

Usage

Load the built-in MovieLens 1M dataset (will be downloaded if not cached):

Output:

	MAE	RMSE	AUC	NDCG@10	Recall@10	Train (s)	Test (s)
[MF]	0.7430	0.8998	0.7445	0.0479	0.0352	0.13	1.57

For more details, please take a look at our examples.

Models

The models supported are listed below. Why don't you join us to lengthen the list?

Click Through Rate Prediction

model	paper
LR: Logistic Regression	Simple and Scalable Response Prediction for Display Advertising
FM: Factorization Machine	[ICDM 2010]Factorization Machines
GBDT+LR: Gradient Boosting Tree with Logistic Regression	Practical Lessons from Predicting Clicks on Ads at Facebook
FNN: Factorization-supported Neural Network	[ECIR 2016]Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction
PNN: Product-based Neural Network	[ICDM 2016]Product-based neural networks for user response prediction
Wide and Deep	[DLRS 2016]Wide & Deep Learning for Recommender Systems
DeepFM	[IJCAI 2017]DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
AFM: Attentional Factorization Machine	[IJCAI 2017]Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
NFM: Neural Factorization Machine	[SIGIR 2017]Neural Factorization Machines for Sparse Predictive Analytics
FFM: Field-aware Factorization Machine	[RecSys 2016]Field-aware Factorization Machines for CTR Prediction
CCPM: Convolutional Click Prediction Model	[CIKM 2015]A Convolutional Click Prediction Model
Piece-wise Linear Model	[arxiv 2017]Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction
DCN: Deep & Cross Network	[ADKDD 2017]Deep & Cross Network for Ad Click Predictions
xDeepFM	[KDD 2018]xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
AutoInt	[arxiv 2018]AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
ONN	[arxiv 2019]Operation-aware Neural Networks for User Response Prediction
FGCNN	[WWW 2019]Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction
FiBiNET	[RecSys 2019]FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
FLEN	[arxiv 2019]FLEN: Leveraging Field for Scalable CTR Prediction

Sequential Recommendation

model/keywords	paper
GRU4Rec	Session-based Recommendations with Recurrent Neural Networks
Caser	Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
DIN: Deep Interest Network	[KDD 2018]Deep Interest Network for Click-Through Rate Prediction
Self-Attention	Next Item Recommendation with Self-Attention
Hierarchical Attention	Sequential Recommender System based on Hierarchical Attention Networks
DIEN: Deep Interest Evolution Network	[AAAI 2019]Deep Interest Evolution Network for Click-Through Rate Prediction
DISN: Deep Session Interest Network	[IJCAI 2019]Deep Session Interest Network for Click-Through Rate Prediction

Embedding Methods

model	paper
node2vec	node2vec: Scalable Feature Learning for Networks
item2vec	ITEM2VEC: Neural item embedding for collaborative filtering
Airbnb embedding	Real-time Personalization using Embeddings for Search Ranking at Airbnb
EGES: Enhanced Graph Embedding with Side information	Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba

Model Evolution

refer: https://zhuanlan.zhihu.com/p/63186101

Contribute

Your contributions at any level of the library are welcome. If you intend to contribute, please:

Fork the rater repository to your own account.
Make changes and create pull requests.

You can also post bug reports and feature requests in GitHub issues.

License

Apache License 2.0

Reference

[Multilayer Perceptron Based Recommendation]
[Autoencoder Based Recommendation]
[CNN Based Recommendation]
[RNN Based Recommendation]
[Restricted Boltzmann Machine Based Recommendation]
[Neural Attention Based Recommendation]
[Neural AutoRegressive Based Recommendation]
[Deep Reinforcement Learning for Recommendation]
[GAN Based Recommendation]
[Deep Hybrid Models for Recommendation]
maciejkula/spotlight
shenweichen/DeepCTR
Magic-Bubble/RecommendSystemPractice
nzc/dnn_ctr

shibing624 / rater Goto Github PK

rater's Introduction

rater

Feature

Data

Install

Usage

Models

Click Through Rate Prediction

Sequential Recommendation

Embedding Methods

Model Evolution

Contribute

License

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