StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking

Related tags

Deep LearningStackRec
Overview

StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking

Datasets

You can download datasets that have been pre-processed:

We construct a large-scale session-based recommendation dataset (denoted as Video-6M) by collecting the interactiton behaviors of nearly 6 million users in a week from a commercial recommender system. The dataset can be used to evaluate very deep recommendation models (up to 100 layers), such as NextItNet (as shown in our paper StackRec（SIGIR2021)). If you use this dataset in your paper, you should cite our NextItNet and StackRec for publish permission.

@article{yuan2019simple,
	title={A simple convolutional generative network for next item recommendation},
	author={Yuan, Fajie and Karatzoglou, Alexandros and Arapakis, Ioannis and Jose, Joemon M and He, Xiangnan},
	journal={Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining},
	year={2019}
}

@article{wang2020stackrec,
  title={StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking},
  author={Wang, Jiachun and Yuan, Fajie and Chen, Jian and Wu, Qingyao and Li, Chengmin and Yang, Min and Sun, Yang and Zhang, Guoxiao},
  journal={Proceedings of the 44th International ACM SIGIR conference on Research and Development in Information Retrieval},
  year={2021}
}

File Description

requirements.txt: the experiment environment

train_nextitnet_sc1.sh: the shell script to train StackRec with NextItNet in CL scenario
train_nextitnet_sc2.sh: the shell script to train StackRec with NextItNet in TF scenario
train_nextitnet_sc3.sh: the shell script to train StackRec with NextItNet in TS scenario
deep_nextitnet.py: the training file of NextItNet
deep_nextitnet_coldrec.py: the training file of NextItNet customized for coldrec source dataset
data_loader.py: the dataset loading file of NextItNet and GRec
data_loader_finetune.py: the dataset loading file of NextItNet and GRec customized for coldrec dataset
generator_deep.py: the model file of NextItNet
ops.py: the module file of NextItNet and GRec with stacking methods doubling blocks
ops_copytop.py: the module file of NextItNet with stacking methods allowed to stack top blocks
ops_original.py: the module file of NextItNet with stacking methods without alpha
fineall.py: the training file of NextItNet customized for coldrec target dataset

train_grec_sc1.sh: the shell script to train StackRec with GRec in CL scenario
deep_GRec: the training file of GRec
generator_deep_GRec.py: the model file of GRec
utils_GRec.py: some tools for GRec

train_sasrec_sc1.sh: the shell script to train StackRec with SASRec in CL scenario
baseline_SASRec.py: the training file of SASRec
Data_loader_SASRec.py: the dataset loading file of SASRec
SASRec_Alpha.py: the model file of SASRec

train_ssept_sc1.sh: the shell script to train StackRec with SSEPT in CL scenario
baseline_SSEPT.py: the training file of SSEPT
Data_loader_SSEPT.py: the dataset loading file of SSEPT
SSEPT_Alpha.py: the model file of SSEPT
utils.py: some tools for SASRec and SSEPT
Modules.py: the module file of SASRec and SSEPT with stacking methods

Stacking with NextItNet

Train in the CL scenario

Execute example:

sh train_nextitnet_sc1.sh

Train in the TS scenario

Execute example:

sh train_nextitnet_sc2.sh

Train in the TF scenario

Execute example:

sh train_nextitnet_sc3.sh

Stacking with GRec

Execute example:

sh train_grec_sc1.sh

Stacking with SASRec

Execute example:

sh train_sasrec_sc1.sh

Stacking with SSEPT

Execute example:

sh train_ssept_sc1.sh

Key Configuration

  • method: five stacking methods including from_scratch, stackC, stackA, stackR and stackE
  • data_ratio: the percentage of training data
  • dilation_count: the number of dilation factors {1,2,4,8}
  • num_blocks: the number of residual blocks
  • load_model: whether load pre-trained model or not
Owner
GitHub Repository
Official implementation of "Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform", ICCV 2021

Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform This repository is the implementation of "Variable-Rate Deep Image C

Myungseo Song 47 Dec 13, 2022
Code and data for ACL2021 paper Cross-Lingual Abstractive Summarization with Limited Parallel Resources.

Multi-Task Framework for Cross-Lingual Abstractive Summarization (MCLAS) The code for ACL2021 paper Cross-Lingual Abstractive Summarization with Limit

Yu Bai 43 Nov 07, 2022
A Lightweight Hyperparameter Optimization Tool 🚀

Lightweight Hyperparameter Optimization 🚀 The mle-hyperopt package provides a simple and intuitive API for hyperparameter optimization of your Machin

136 Jan 08, 2023
[ICCV 2021] FaPN: Feature-aligned Pyramid Network for Dense Image Prediction

FaPN: Feature-aligned Pyramid Network for Dense Image Prediction [arXiv] [Project Page] @inproceedings{ huang2021fapn, title={{FaPN}: Feature-alig

Shihua Huang 23 Jul 22, 2022
Implementation of a Transformer using ReLA (Rectified Linear Attention)

ReLA (Rectified Linear Attention) Transformer Implementation of a Transformer using ReLA (Rectified Linear Attention). It will also contain an attempt

Phil Wang 49 Oct 14, 2022
A Simplied Framework of GAN Inversion

Framework of GAN Inversion Introcuction You can implement your own inversion idea using our repo. We offer a full range of tuning settings (in hparams

Kangneng Zhou 13 Sep 27, 2022
Fast, flexible and easy to use probabilistic modelling in Python.

Please consider citing the JMLR-MLOSS Manuscript if you've used pomegranate in your academic work! pomegranate is a package for building probabilistic

Jacob Schreiber 3k Dec 29, 2022
Pytorch implementation of Integrating Tree Path in Transformer for Code Representation

This is an official Pytorch implementation of the approaches proposed in: Han Peng, Ge Li, Wenhan Wang, Yunfei Zhao, Zhi Jin “Integrating Tree Path in

Han Peng 16 Dec 23, 2022
FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification

FPGA & FreeNet Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification by Zhuo Zheng, Yanfei Zhong, Ailong M

Zhuo Zheng 92 Jan 03, 2023
Adaptive Denoising Training (ADT) for Recommendation.

DenoisingRec Adaptive Denoising Training for Recommendation. This is the pytorch implementation of our paper at WSDM 2021: Denoising Implicit Feedback

Wenjie Wang 51 Dec 30, 2022
A Pytorch Implementation for Compact Bilinear Pooling.

CompactBilinearPooling-Pytorch A Pytorch Implementation for Compact Bilinear Pooling. Adapted from tensorflow_compact_bilinear_pooling Prerequisites I

169 Dec 23, 2022
Keras udrl - Keras implementation of Upside Down Reinforcement Learning

keras_udrl Keras implementation of Upside Down Reinforcement Learning This is me

Eder Santana 7 Jan 24, 2022
Multi-Objective Loss Balancing for Physics-Informed Deep Learning

Multi-Objective Loss Balancing for Physics-Informed Deep Learning Code for ReLoBRaLo. Abstract Physics Informed Neural Networks (PINN) are algorithms

Rafael Bischof 16 Dec 12, 2022
Implementation of fast algorithms for Maximum Spanning Tree (MST) parsing that includes fast ArcMax+Reweighting+Tarjan algorithm for single-root dependency parsing.

Fast MST Algorithm Implementation of fast algorithms for (Maximum Spanning Tree) MST parsing that includes fast ArcMax+Reweighting+Tarjan algorithm fo

Miloš Stanojević 11 Oct 14, 2022
A simple algorithm for extracting tree height in sparse scene from point cloud data.

TREE HEIGHT EXTRACTION IN SPARSE SCENES BASED ON UAV REMOTE SENSING This is the offical python implementation of the paper "Tree Height Extraction in

6 Oct 28, 2022
DeepLab-ResNet rebuilt in TensorFlow

DeepLab-ResNet-TensorFlow This is an (re-)implementation of DeepLab-ResNet in TensorFlow for semantic image segmentation on the PASCAL VOC dataset. Fr

Vladimir 1.2k Nov 04, 2022
Patch-Diffusion Code (AAAI2022)

Patch-Diffusion This is an official PyTorch implementation of "Patch Diffusion: A General Module for Face Manipulation Detection" in AAAI2022. Require

H 7 Nov 02, 2022
DECAF: Deep Extreme Classification with Label Features

DECAF DECAF: Deep Extreme Classification with Label Features @InProceedings{Mittal21, author = "Mittal, A. and Dahiya, K. and Agrawal, S. and Sain

46 Nov 06, 2022
《K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters》(2020)

K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters This repository is the implementation of the paper "K-Adapter: Infusing Knowledge

Microsoft 118 Dec 13, 2022
Code for our paper "SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization", ACL 2021

SimCLS Code for our paper: "SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization", ACL 2021 1. How to Install Requirements

Yixin Liu 150 Dec 12, 2022