Neural Graph Collaborative Filtering, SIGIR2019

👕 Open-source course on architecting, building and deploying a real-time personalized recommender for H&M fashion articles.

Disentagnled Graph Collaborative Filtering, SIGIR2020

[ACMMM 2021] PyTorch implementation for "Mining Latent Structures for Multimedia Recommendation"

Code and dataset for CVPR 2019 paper "Learning Binary Code for Personalized Fashion Recommendation"

🌟 국내 스타트업 목록 및 설명 🌟

Developed a hybrid-filtering personalized news articles recommendation system which can suggest articles from popular news service providers based on reading history of twitter users who share similar interests (Collaborative filtering) and content similarity of the article and user’s tweets (Content-based filtering)

Priveedly: A django-based content reader and recommender for personal and private use

Unleash the Power of Music with Personalized Concert Recommendations.

Personalized Visual Art Recommendation by Learning Latent Semantic Representations

personalized recommendation

TrialMatchAI aims to seamlessly match cancer patients to clinical trials based on their unique genomic and clinical profiles using AI

Full-stack hybrid book recommendation system combining Collaborative Filtering and Content-Based Filtering with weighted hybrid scoring, modular data pipelines, and model persistence. Deployed via Flask with responsive HTML/CSS UI and integrated CI/CD for production-ready, scalable, and interactive recommendations.

A collaborative platform for creating and curating personalized tech learning paths. Powered by Django, it tailors content based on users' skills and preferences, integrating external educational resources. http://34.72.154.173/

This repo presents codes that helps patients to get informed about diverticulitis from food meal image with help of Ollama and MedGemma with VLM in local setup

A book search engine with support for title search, author search, and multilingual query and result; results tailored to each user given one's past book ratings and to-read list.

Movie Recommendation Anytime Anywhere

A demo app to show how the implementation results look like when AWS Personalize is trained with movie lens dataset.

Use the Scikit-Network for PageRank algorithms including Topic-specific PR and improve the performance of various recommendation-systems using Surprise library

This system helps users discover personalized audiobook recommendations based on their preferred genre, author, or book title, backed by data-driven insights and visualizations.