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Bringing Characters to Life with Computer Brains in Unity
Service Classification based on Service Description
[WWW'2024] "RLMRec: Representation Learning with Large Language Models for Recommendation"
Use ChatGPT to summarize the arXiv papers. 全流程加速科研，利用chatgpt进行论文全文总结+专业翻译+润色+审稿+审稿回复
Scira (Formerly MiniPerplx) is a minimalistic AI-powered search engine that helps you find information on the internet and cites it too. Powered by Vercel AI SDK! Search with models like xAI's Grok 3.
"AutoAgent: Fully-Automated and Zero-Code LLM Agent Framework"
🚀🚀 「大模型」2小时完全从0训练26M的小参数GPT！🌏 Train a 26M-parameter GPT from scratch in just 2h!
📱 Display and control your Android device graphically with scrcpy.
👾 Fast and simple video download library and CLI tool written in Go
【TSC 2022】 Mashup-Oriented Web API Recommendation via Multi-Model Fusion and Multi-Task Learning
An Open-source Toolkit for Deep Learning based Recommendation with Tensorflow.
Neural Graph Collaborative Filtering, SIGIR2019
Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding
KDD2019-Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation
Source code for CIKM 2021 paper “Topic-aware Heterogeneous Graph Neural Network for Link Prediction”
[MM 2022] Official Tensorflow implementation for "TopicVAE: Topic-aware Disentanglement Representation Learning for Enhanced Recommendation".
Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach, AAAI2020
Codes for papers: 1. Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters (ICML). 2. Less is More: Exploring Simple and Powerful Low-pass Graph Convolutional Network f…
DHGNN source code for IJCAI19 paper: "Dynamic Hypergraph Neural Networks"
[SIGIR'22] Knowledge Graph Contrastive Learning for Recommendation
[ICLR'2023] "LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation"
[WSDM'2023] "HGCL: Heterogeneous Graph Contrastive Learning for Recommendation"
The latest research progress of Contrastive Learning(CL), Data Augmentation(DA) and Self-Supervised Learning(SSL) in Recommender Systems