Code for ICDM 2022 paper, Beyond Double Ascent via Recurrent Neural Tangent Kernel in Sequential Recommendation.
Download datasets from RecSysDatasets or their Google Drive. And put the files in ./dataset/
like the following.
$ tree
.
├── Amazon_Beauty
│ ├── Amazon_Beauty.inter
│ └── Amazon_Beauty.item
├── Amazon_Clothing_Shoes_and_Jewelry
│ ├── Amazon_Clothing_Shoes_and_Jewelry.inter
│ └── Amazon_Clothing_Shoes_and_Jewelry.item
├── Amazon_Sports_and_Outdoors
│ ├── Amazon_Sports_and_Outdoors.inter
│ └── Amazon_Sports_and_Outdoors.item
├── ml-1m
│ ├── ml-1m.inter
│ ├── ml-1m.item
│ ├── ml-1m.user
│ └── README.md
└── yelp
├── README.md
├── yelp.inter
├── yelp.item
└── yelp.user
Run python3 run_overrec.py
.
This experiment does not require GPU calculation. It is purely CPU computation. Yet it may require large memory for kernel calculation, ranging from 100 Gb ~ 300 Gb for different datasets.
We implement SKNN and STAN in this repo.
Run python3 run_sknn.py
or python3 run_stan.py
.
If you find this repo useful, please cite
@article{OverRec,
author = {Ruihong Qiu and
Zi Huang and
Hongzhi Yin},
title = {Beyond Double Ascent via Recurrent Neural Tangent Kernel in Sequential Recommendation},
journal = {CoRR},
volume = {abs/2209.03735},
year = {2022},
}