Decoupled Temporal Encoding (DTE)

This repository provides a minimal TensorFlow 1.12 implementation of Decoupled Temporal Encoding (DTE) for generative recommendation.

DTE contains two temporal components:

• Macro-temporal module: injects temporal context into input item embeddings.
• Micro-sequential module: adds a time-gated order bias to masked self-attention.

This code is intended as a reference implementation / reproducible demo for the paper, based on the KuaiRand 1K setting rather than a full production system.

Environment

This code is developed and tested with:

• Framework: TensorFlow 1.12

Note: this project is implemented in TensorFlow 1.x graph mode.

Model Setting

The demo follows the paper setting:

• decoder-only Transformer backbone
• single decoder block
• 8 attention heads
• embedding size = 64
• MLP tower = 16 -> 8 -> 1
• optimizer = Adam
• learning rate = 1e-4
• batch size = 1024

For the temporal modules:

Macro module

• learnable decay coefficient lambda
• adaptive fusion MLP: 16 -> 8 -> 4
• user context from average pooling over historical item embeddings

Micro module

• pairwise time gaps are computed in seconds
• learnable threshold tau
• fixed temperature gamma = 10

File Structure

• config.py
• feature_maps.py
• layers.py
• data_loader.py
• model.py
• train.py

Descriptions:

• layers.py: macro and micro temporal modules
• data_loader.py: local txt data reader
• model.py: model graph
• train.py: training entry

How to Run

Install dependencies:

pip install tensorflow==1.12.0 numpy

Prepare your local data and training file.

Run:

python train.py

Notes

• This is a minimal demo, not a full industrial system.
• Only a subset of user/item side features is retained.
• The code focuses on the two main ideas of the paper:
  • macro temporal encoding
  • micro time-gated attention bias

Citation

If you find this code useful, please cite:

@inproceedings{dte2026,
  title={Decoupled Temporal Encoding for Generative Recommendation},
  author={Pengfei Jia, Jingjian Wang, Jingmao Li, Ge Zhang, Feng Shi},
  year={2026}
}