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Metadata

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

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

MODELS TAG PAPERS
DeepWalk Embedding DeepWalk: Online Learning of Social Representations
Node2Vec Embedding node2vec: Scalable Feature Learning for Networks
FaceBook GBDT+LR Embedding/CTR Practical lessons from predicting clicks on Ads at Facebook
Wide&Deep Deep CTR Wide & Deep Learning for Recommender Systems
Youtube Candidate Generation Embedding/Deep CTR Deep Neural Networks for YouTube Recommendations
Youtube Ranking Deep CTR Deep Neural Networks for YouTube Recommendations
DCN Deep CTR Deep & Cross Network for Ad Click Predictions
PNN Deep CTR Product-based Neural Networks for User Response Prediction
DeepFM Deep CTR DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
ESMM Deep CTR /CVR Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate
DIN Deep CTR Deep Interest Network for Click-Through Rate Prediction
XdeepFM Deep CTR xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
NCF Deep MF Neural Collaborative Filtering
LFM MF/CF funkSVD
wALS MF/CF CollaborativeFiltering for Implicit Feedback Datasets
eALS MF/CF Fast Matrix Factorization for Online Recommendation with Implicit Feedback
BPR MF/CF BPR: Bayesian Personalized Ranking from Implicit Feedback
VBPR MF/CF VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback
LSA MF/topic model
pLSA topic model Collaborative topic modeling for recommending scientific articles
LDA topic model
Item_based CF Item-based collaborative filtering recommendation algorithms
Personal Rank CF Topic-Sensitive PageRank
Item2Vec Embedding Item2Vec:Neural Item Embedding for Collaborative Filtering
AutoEncoder
Tompson Sampling Bandit
UCB Bandit
LinUCB Bandit
Bert NLP
Causal Embedding MF/Cause

Metrics

METRICS PAPERS
ndcg@K

Dataset

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

Utils

← Metadata

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verified publisher icon yinyajun

Metadata

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

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