Popular GitHub Repositories for Poi Recommendation

OpenVINO? is an open source toolkit for optimizing and deploying AI inference

深度学习面试宝典（含数学、机器学习、深度学习、计算机视觉、自然语言处理和SLAM等方向）

Easy-to-use,Modular and Extendible package of deep-learning based CTR models .

A Python scikit for building and analyzing recommender systems

AI + Data, online. https://vespa.ai

Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG

A Python implementation of LightFM, a hybrid recommendation algorithm.

Recommendation Algorithm大规模推荐算法库，包含推荐系统经典及最新算法LR、Wide&Deep、DSSM、TDM、MIND、Word2Vec、Bert4Rec、DeepWalk、SSR、AITM，DSIN，SIGN，IPREC、GRU4Rec、Youtube_dnn、NCF、GNN、FM、FFM、DeepFM、DCN、DIN、DIEN、DLRM、MMOE、PLE、ESMM、ESCMM, MAML、xDeepFM、DeepFEFM、NFM、AFM、RALM、DMR、GateNet、NAML、DIFM、Deep Crossing、PNN、BST、AutoInt、FGCNN、FLEN、Fibinet、ListWise、DeepRec、ENSFM，TiSAS，AutoFIS等，包含经典推荐系统数据集criteo 、movielens等

A unified, comprehensive and efficient recommendation library

This repository contains Deep Learning based articles , paper and repositories for Recommender Systems

Pytorch domain library for recommendation systems

A framework for large scale recommendation algorithms.

Awesome Deep Learning papers for industrial Search, Recommendation and Advertisement. They focus on Embedding, Matching, Ranking (CTR/CVR prediction), Post Ranking, Large Model (Generative Recommendation, LLM), Transfer learning, Reinforcement Learning and so on.

推荐/广告/搜索领域工业界经典以及最前沿论文集合。A collection of industry classics and cutting-edge papers in the field of recommendation/advertising/search.

Deep learning for recommender systems

QRec: A Python Framework for quick implementation of recommender systems (TensorFlow Based)

Machine Learning Platform and Recommendation Engine built on Kubernetes

基于金融-司法领域(兼有闲聊性质)的聊天机器人，其中的主要模块有信息抽取、NLU、NLG、知识图谱等，并且利用Django整合了前端展示,目前已经封装了nlp和kg的restful接口

A TensorFlow recommendation algorithm and framework in Python.

Superlinked is a Python framework for AI Engineers building high-performance search & recommendation applications that combine structured and unstructured data.