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
RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation

RIFE - Real Time Video Interpolation arXiv | YouTube | Colab | Tutorial | Demo Table of Contents Introduction Collection Usage Evaluation Training and

hzwer 3k Jan 04, 2023
Lab Materials for MIT 6.S191: Introduction to Deep Learning

This repository contains all of the code and software labs for MIT 6.S191: Introduction to Deep Learning! All lecture slides and videos are available

Alexander Amini 5.6k Dec 26, 2022
smc.covid is an R package related to the paper A sequential Monte Carlo approach to estimate a time varying reproduction number in infectious disease models: the COVID-19 case by Storvik et al

smc.covid smc.covid is an R package related to the paper A sequential Monte Carlo approach to estimate a time varying reproduction number in infectiou

0 Oct 15, 2021
Analysis of rationale selection in neural rationale models

Neural Rationale Interpretability Analysis We analyze the neural rationale models proposed by Lei et al. (2016) and Bastings et al. (2019), as impleme

Yiming Zheng 3 Aug 31, 2022
ICNet and PSPNet-50 in Tensorflow for real-time semantic segmentation

Real-Time Semantic Segmentation in TensorFlow Perform pixel-wise semantic segmentation on high-resolution images in real-time with Image Cascade Netwo

Oles Andrienko 219 Nov 21, 2022
R-package accompanying the paper "Dynamic Factor Model for Functional Time Series: Identification, Estimation, and Prediction"

dffm The goal of dffm is to provide functionality to apply the methods developed in the paper “Dynamic Factor Model for Functional Time Series: Identi

Sven Otto 3 Dec 09, 2022
Learning from graph data using Keras

Steps to run = Download the cora dataset from this link : https://linqs.soe.ucsc.edu/data unzip the files in the folder input/cora cd code python eda

Mansar Youness 64 Nov 16, 2022
Annealed Flow Transport Monte Carlo

Annealed Flow Transport Monte Carlo Open source implementation accompanying ICML 2021 paper by Michael Arbel*, Alexander G. D. G. Matthews* and Arnaud

DeepMind 30 Nov 21, 2022
Research Artifact of USENIX Security 2022 Paper: Automated Side Channel Analysis of Media Software with Manifold Learning

Manifold-SCA Research Artifact of USENIX Security 2022 Paper: Automated Side Channel Analysis of Media Software with Manifold Learning The repo is org

Yuanyuan Yuan 172 Dec 29, 2022
PyTorch implementation and pretrained models for XCiT models. See XCiT: Cross-Covariance Image Transformer

Official code Cross-Covariance Image Transformer (XCiT)

Facebook Research 605 Jan 02, 2023
PyJokes - Joking around with Python library pyjokes

Hi, it's Muhaimin again 👋 This is something unorthodox but cool. Don't forget t

Muhaimin A. Salay Kanton 1 Feb 02, 2022
A clean and scalable template to kickstart your deep learning project 🚀 ⚡ 🔥

Lightning-Hydra-Template A clean and scalable template to kickstart your deep learning project 🚀 ⚡ 🔥 Click on Use this template to initialize new re

Hyunsoo Cho 1 Dec 20, 2021
Voice Gender Recognition

In this project it was used some different Machine Learning models to identify the gender of a voice (Female or Male) based on some specific speech and voice attributes.

Anne Livia 1 Jan 27, 2022
Implementation of "Debiasing Item-to-Item Recommendations With Small Annotated Datasets" (RecSys '20)

Debiasing Item-to-Item Recommendations With Small Annotated Datasets This is the code for our RecSys '20 paper. Other materials can be found here: Ful

Microsoft 34 Aug 10, 2022
SwinIR: Image Restoration Using Swin Transformer

SwinIR: Image Restoration Using Swin Transformer This repository is the official PyTorch implementation of SwinIR: Image Restoration Using Shifted Win

Jingyun Liang 2.4k Jan 08, 2023
Code for the bachelors-thesis flaky fault localization

Flaky_Fault_Localization Scripts for the Bachelors-Thesis: "Flaky Fault Localization" by Christian Kasberger. The thesis examines the usefulness of sp

Christian Kasberger 1 Oct 26, 2021
Code for our ACL 2021 paper - ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer

ConSERT Code for our ACL 2021 paper - ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer Requirements torch==1.6.0

Yan Yuanmeng 478 Dec 25, 2022
Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network

Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network This repository is the official implementation of Speech Separati

Kai Li (李凯) 116 Nov 09, 2022
Official PyTorch Implementation of GAN-Supervised Dense Visual Alignment

GAN-Supervised Dense Visual Alignment — Official PyTorch Implementation Paper | Project Page | Video This repo contains training, evaluation and visua

944 Jan 07, 2023
Code for the Paper: Conditional Variational Capsule Network for Open Set Recognition

Conditional Variational Capsule Network for Open Set Recognition This repository hosts the official code related to "Conditional Variational Capsule N

Guglielmo Camporese 35 Nov 21, 2022