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yinyajun/Details-In-Recommendation

Recommender-In-Detail is a package which offers detailed implementations of state-of-the-art techniques and basic methods in recommendation.

Python

19

62 commits

updated Sep 12, 2019

See the code

README

Details-In-Recommendation

As its name suggests,Details-In-Recommendation is a package which offers detailed implementations of state-of-the-art techniques and basic methods in recommendation. Most of them could be used in production environments through simple modifications. The structure of the package is as follows:

  1. models: contains many sub-package which implements recommendation methods.
  2. metrics: some metrics to evaluate recommendation.
  3. dataset: dataset to used in examples.
  4. examples: examples to test our implementation in models by using dataset in dataset and metrics in metrics.
  5. utils: some useful tools, e.g., embedding visualization, and etc.

中文博客-Chinese Blogs.

Implemented Models

MODELSTAGPAPERS
DeepWalkEmbeddingDeepWalk: Online Learning of Social Representations
Node2VecEmbeddingnode2vec: Scalable Feature Learning for Networks
FaceBook GBDT+LREmbedding/CTRPractical lessons from predicting clicks on Ads at Facebook
Wide&DeepDeep CTRWide & Deep Learning for Recommender Systems
Youtube Candidate GenerationEmbedding/Deep CTRDeep Neural Networks for YouTube Recommendations
Youtube RankingDeep CTRDeep Neural Networks for YouTube Recommendations
DCNDeep CTRDeep & Cross Network for Ad Click Predictions
PNNDeep CTRProduct-based Neural Networks for User Response Prediction
DeepFMDeep CTRDeepFM: A Factorization-Machine based Neural Network for CTR Prediction
ESMMDeep CTR /CVREntire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate
DINDeep CTRDeep Interest Network for Click-Through Rate Prediction
XdeepFMDeep CTRxDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
NCFDeep MFNeural Collaborative Filtering
LFMMF/CFfunkSVD
wALSMF/CFCollaborativeFiltering for Implicit Feedback Datasets
eALSMF/CFFast Matrix Factorization for Online Recommendation with Implicit Feedback
BPRMF/CFBPR: Bayesian Personalized Ranking from Implicit Feedback
VBPRMF/CFVBPR: Visual Bayesian Personalized Ranking from Implicit Feedback
LSAMF/topic model
pLSAtopic modelCollaborative topic modeling for recommending scientific articles
LDAtopic model
Item_basedCFItem-based collaborative filtering recommendation algorithms
Personal RankCFTopic-Sensitive PageRank
Item2VecEmbeddingItem2Vec:Neural Item Embedding for Collaborative Filtering
AutoEncoder
Tompson SamplingBandit
UCBBandit
LinUCBBandit
BertNLP
Causal EmbeddingMF/Cause

Metrics

METRICSPAPERS
ndcg@K

Dataset

https://tianchi.aliyun.com/dataset/

Utils

Contributors

yinyajun

62 commits

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yinyajun/Details-In-Recommendation

Recommender-In-Detail is a package which offers detailed implementations of state-of-the-art techniques and basic methods in recommendation.

Python

19

62 commits

updated Sep 12, 2019

See the code

README

Details-In-Recommendation

As its name suggests,Details-In-Recommendation is a package which offers detailed implementations of state-of-the-art techniques and basic methods in recommendation. Most of them could be used in production environments through simple modifications. The structure of the package is as follows:

  1. models: contains many sub-package which implements recommendation methods.
  2. metrics: some metrics to evaluate recommendation.
  3. dataset: dataset to used in examples.
  4. examples: examples to test our implementation in models by using dataset in dataset and metrics in metrics.
  5. utils: some useful tools, e.g., embedding visualization, and etc.

中文博客-Chinese Blogs.

Implemented Models

MODELSTAGPAPERS
DeepWalkEmbeddingDeepWalk: Online Learning of Social Representations
Node2VecEmbeddingnode2vec: Scalable Feature Learning for Networks
FaceBook GBDT+LREmbedding/CTRPractical lessons from predicting clicks on Ads at Facebook
Wide&DeepDeep CTRWide & Deep Learning for Recommender Systems
Youtube Candidate GenerationEmbedding/Deep CTRDeep Neural Networks for YouTube Recommendations
Youtube RankingDeep CTRDeep Neural Networks for YouTube Recommendations
DCNDeep CTRDeep & Cross Network for Ad Click Predictions
PNNDeep CTRProduct-based Neural Networks for User Response Prediction
DeepFMDeep CTRDeepFM: A Factorization-Machine based Neural Network for CTR Prediction
ESMMDeep CTR /CVREntire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate
DINDeep CTRDeep Interest Network for Click-Through Rate Prediction
XdeepFMDeep CTRxDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
NCFDeep MFNeural Collaborative Filtering
LFMMF/CFfunkSVD
wALSMF/CFCollaborativeFiltering for Implicit Feedback Datasets
eALSMF/CFFast Matrix Factorization for Online Recommendation with Implicit Feedback
BPRMF/CFBPR: Bayesian Personalized Ranking from Implicit Feedback
VBPRMF/CFVBPR: Visual Bayesian Personalized Ranking from Implicit Feedback
LSAMF/topic model
pLSAtopic modelCollaborative topic modeling for recommending scientific articles
LDAtopic model
Item_basedCFItem-based collaborative filtering recommendation algorithms
Personal RankCFTopic-Sensitive PageRank
Item2VecEmbeddingItem2Vec:Neural Item Embedding for Collaborative Filtering
AutoEncoder
Tompson SamplingBandit
UCBBandit
LinUCBBandit
BertNLP
Causal EmbeddingMF/Cause

Metrics

METRICSPAPERS
ndcg@K

Dataset

https://tianchi.aliyun.com/dataset/

Utils

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Prayforhanluo/CTR_Algorithm

一些经典的CTR算法的复现; LR, FM, FFM, AFM, DeepFM, xDeepFM, PNN, DCN, DCNv2, DIFM, AutoInt,…

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100.0%