https://github.com/jiwidi/Behavior-Sequence-Transformer-Pytorch

This is a pytorch implementation for the BST model from Alibaba https://arxiv.org/pdf/1905.06874.pdf
https://github.com/jiwidi/Behavior-Sequence-Transformer-Pytorch

alibaba behavior behavior-sequence-transformer pytorch recommenders seq2seq seq2seq-attn transformers

Last synced: 9 days ago
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This is a pytorch implementation for the BST model from Alibaba https://arxiv.org/pdf/1905.06874.pdf

• Host: GitHub
• URL: https://github.com/jiwidi/Behavior-Sequence-Transformer-Pytorch
• Owner: jiwidi
• License: mit
• Created: 2021-06-27T16:20:17.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2022-07-11T14:55:20.000Z (over 2 years ago)
• Last Synced: 2024-08-02T15:15:24.808Z (3 months ago)
• Topics: alibaba, behavior, behavior-sequence-transformer, pytorch, recommenders, seq2seq, seq2seq-attn, transformers
• Language: Jupyter Notebook
• Homepage:
• Size: 224 KB
• Stars: 140
• Watchers: 2
• Forks: 29
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Behavior-Sequence-Transformer-Pytorch

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/jiwidi/Behavior-Sequence-Transformer-Pytorch/blob/master/colab-bst.ipynb)

This is a pytorch implementation for the BST model from Alibaba https://arxiv.org/pdf/1905.06874.pdf

![](img/bst.png "BST ARCHITECTURE")

This model is a novel recommender architecture based on seq2seq models. We translate user behaviour into sequences and predict a rating for each target item (movie).

# Dataset

For this implementation we used Movielens [1M Dataset](https://movielens.org/) that contains timestamps per each rating, making it perfect to test in the sequence recommendation model.

# Running

You can run it in colab [here](https://colab.research.google.com/github/jiwidi/Behavior-Sequence-Transformer-Pytorch/blob/master/colab-bst.ipynb). If you prefer to run locally the model architecture is contained on `pytorch-best.ipynb` while data processing is on the `prepare_data.ipynb` notebook and should be run first.

# Results

Training on all-1 user ratings and leaving the latest rating for test we obtain the following results

| Dataset |  MAE  | RMSE |

| :------ | :---: | ---: |

| Train   | 0.72  | 0.84 |

| Test    | 0.74  | 0.93 |

Here is a screenshot of training logs we we see overfitting from epoch 12-15.

![](img/logs.png)

# References

* Original paper [1](https://arxiv.org/pdf/1905.06874.pdf)

* Keras implementation [2](https://keras.io/examples/structured_data/movielens_recommendations_transformers/)

* Tensorflow implementation [3](https://github.com/shenweichen/DeepCTR/blob/master/deepctr/models/bst.py)