TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations

Last update: Oct 16, 2022

Related tags

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

Using this codebase as a tool for my own research. Making some modifications to the original repo for my own purposes.

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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

Using this codebase as a tool for my own research. Making some modifications to the original repo for my own purposes.

wmctrl ported to Python Ctypes

Source code and dataset of the paper "Contrastive Adaptive Propagation Graph Neural Networks forEfficient Graph Learning"

Diffgram - Supervised Learning Data Platform

Self-training with Weak Supervision (NAACL 2021)

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Offline Reinforcement Learning with Implicit Q-Learning

CRISCE: Automatically Generating Critical Driving Scenarios From Car Accident Sketches

PyTorch - Python + Nim

Codebase for ECCV18 "The Sound of Pixels"

A simplified framework and utilities for PyTorch

Reverse engineer your pytorch vision models, in style

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