EdgeRec

This repository is the implement of EdgeRec using Mobile Intelligent Dataset(part 1).

Prerequisites

A basic pytorch installation. The version is 1.7.
Python packages you might not have: jsonargparse, tqdm, sklearn.

Installation

Clone the repository

git clone https://github.com/tao-shen/EdgeRec

Usage

Setup dataset

We setup two datasize of data: full refers to the full dataset and demo refers to the first 10000 samples of the full dataset.

We provide the demo dataset here and you can find the detailed description and download te full dataset here(part 1).
```
    # demo dataset
    datasize: demo
    device: cuda:0
    lr: 0.01
    batchsize: 100
    # full dataset
    datasize: full
    device: cuda:0
    lr: 0.001
    batchsize: 10000
```
Run the main.py

if you want to run on the demo dataset, for example
```
python main.py --device=cuda:0 --datasize=demo --lr=0.01 --batchsize=100
```
if you want to run on the full dataset, for example
```
python main.py --device=cuda:0 --datasize=full --lr=0.001 --batchsize=10000
```
Note: The parsed arguments will overwrite the configuration in yaml file.

Results

Setting	AUC
scores only	0.7277
trained model	0.7310

On full dataset, the baseline auc:0.7277 is evaluated only using scores in test samples, which is provided by cloud. We train the DIN model, with batchsize=10000,lr=0.001, the result auc:0.7310, shows in the Table.

License

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.