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In this repository, a number of sequential recommendation models are implemented using Python and Tensorflow.The implemented models cover common sequential recommendation algorithms (session based ). We implement the code in the paper in a concise way, including how to construct samples and training, to help readers better understand the paper's ideas.
Algorithms Implemented
So far, we have implemented these models, covering deep learning and traditional methods. Follow up to continue to update。
model
paper
methods
AttRec
Next Item Recommendation with Self-Attention
self-attention
Caser
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
CNN
GRU4Rec
Session-based Recommendations with Recurrent Neural Networks
GRU
FPMC
Factorizing Personalized Markov Chains for Next-Basket Recommendation
MF+MC
TransRec
Translation-based Recommendation
MF
SASRec
Self-Attentive Sequential Recommendation
transfomer
and so on.
Usage
To use the code, enter the models directory and execute run_Model.pysuch as:
cd models/AttRecpython run_Attrec.py
Note: Due to the different sample construction methods and experimental methods of different algorithms, we generate independent codes for each algorithm.
Requirements
Tensorflow 1.1+
Python 3.6+,
numpy
pandas
ToDo List
More models
Code refactoring
Support tf.data.datasets and tf.estimator
About
Several sequential recommended models implemented by tenosrflow1.x