Statistics for topic recommendation-system

Best Practices on Recommendation Systems

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

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.

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）

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

[WSDM 2024 Oral] This is our Pytorch implementation for the paper: "Intent Contrastive Learning with Cross Subsequences for Sequential Recommendation".

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.

RecTools - library to build Recommendation Systems easier and faster than ever before

A Lighting Pytorch Framework for Recommendation Models, Easy-to-use and Easy-to-extend.

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

Best Practices on Recommendation Systems

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

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）

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.

[WSDM 2024 Oral] This is our Pytorch implementation for the paper: "Intent Contrastive Learning with Cross Subsequences for Sequential Recommendation".

Transforming skincare recommendations: our hybrid system combines KNN, CNN, and EfficientNet B0 for personalized advice. Published in IEEE, with 80% validation accuracy and 87.10% training accuracy.

implement basic and contextual MAB algorithms for recommendation system

基于深度学习的商品推荐系统，高性能，可承受高并发，可跨平台

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.

Best Practices on Recommendation Systems

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

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）

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.

Local recommendation system

[Pytorch] Generative retrieval model using semantic IDs from "Recommender Systems with Generative Retrieval"

Transforming skincare recommendations: our hybrid system combines KNN, CNN, and EfficientNet B0 for personalized advice. Published in IEEE, with 80% validation accuracy and 87.10% training accuracy.

基于深度学习的商品推荐系统，高性能，可承受高并发，可跨平台

Enhancing Recommendation Systems with Large Language Models (RAG - LangChain - OpenAI)

[Pytorch] Generative retrieval model using semantic IDs from "Recommender Systems with Generative Retrieval"

Enhancing Recommendation Systems with Large Language Models (RAG - LangChain - OpenAI)

Best Practices on Recommendation Systems

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

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

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）

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.

[WSDM 2024 Oral] This is our Pytorch implementation for the paper: "Intent Contrastive Learning with Cross Subsequences for Sequential Recommendation".

A collection of personally developed projects contributing towards the advancement of Artificial General Intelligence(AGI)

[WSDM'2024 Oral] "LLMRec: Large Language Models with Graph Augmentation for Recommendation"

Food/Diet Recommendation system using machine learning

A curated list of awesome machine learning libraries for marketing, including media mix models, multi touch attribution, causal inference and more