TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations

Last update: Oct 16, 2022

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Deep Learning TEA

Overview

TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations

Requirements

python 3.6
torch 1.9
numpy 1.19

Quick Start

The experiment uses the Yelp and Epinions data sets. Data preprocessing is required before training. Here uses yelp as an example

python my_preprocess_yelp.py

TEA can be trained afterwards

python run_gatmeta_tea_gat_yelp.py #TEA-A
python run_gatmeta_tea_gsage_yelp.py #TEA-S

The training method of the baseline in the paper is similar

Experiment Result

Epinions

Yelp

Owner

DMIRLAB

DMIRLAB Public Repository

GitHub Repository

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TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations

Related tags

Overview

TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations

Requirements

Quick Start

Experiment Result

Epinions

Yelp

Owner

DMIRLAB

Navigating StyleGAN2 w latent space using CLIP

Time-Optimal Planning for Quadrotor Waypoint Flight

A real world application of a Recurrent Neural Network on a binary classification of time series data

RobustVideoMatting and background composing in one model by using onnxruntime.

CVPR 2021 Challenge on Super-Resolution Space

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Second-Order Neural ODE Optimizer, NeurIPS 2021 spotlight

Pytorch implementation of MaskGIT: Masked Generative Image Transformer

Processed, version controlled history of Minecraft's generated data and assets

Code Release for Learning to Adapt to Evolving Domains

Code for "Learning Canonical Representations for Scene Graph to Image Generation", Herzig & Bar et al., ECCV2020

DL & CV-based indicator toolset for the vehicle drivers via live dash-cam footage.

A simple rest api serving a deep learning model that classifies human gender based on their faces. (vgg16 transfare learning)

Implementation of fast algorithms for Maximum Spanning Tree (MST) parsing that includes fast ArcMax+Reweighting+Tarjan algorithm for single-root dependency parsing.

Code from PropMix, accepted at BMVC'21

CUAD

VLGrammar: Grounded Grammar Induction of Vision and Language

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