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

System-oriented IR evaluations are limited to rather abstract understandings of real user behavior

Bayesian Optimization using GPflow

Single Image Deraining Using Bilateral Recurrent Network (TIP 2020)

Optimized primitives for collective multi-GPU communication

Fake-user-agent-traffic-geneator - Python CLI Tool to generate fake traffic against URLs with configurable user-agents

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A Python 3 package for state-of-the-art statistical dimension reduction methods

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Pseudo lidar - (CVPR 2019) Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving

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Gym Threat Defense

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