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"
Priveedly: A django-based content reader and recommender for personal and private use
TrialMatchAI aims to seamlessly match cancer patients to clinical trials based on their unique genomic and clinical profiles using AI
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)
personalized recommendation
Personalized Visual Art Recommendation by Learning Latent Semantic Representations
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.
It describes the features of AWS personalize.
MoodRiser is a web application created during a 24-hour hackathon at the CodeForAll Fullstack Programming Bootcamp. Utilizing HTML, CSS, JavaScript, Python with Flask, and various APIs including Spotify and Google Books, and OpenAI, this SPA helps users manage their emotions through personalized content recommendations based on their current mood.
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/
A demo app to show how the implementation results look like when AWS Personalize is trained with movie lens dataset.
Analyzing Temporal, Spatial, and Historical Data in Rating Prediction Algorithms: A Comparative Study
An intuitive movie recommendation system leveraging genre similarity with TF-IDF and cosine similarity for a personalized film discovery experience.
Movie Recommendation Anytime Anywhere
Use the Scikit-Network for PageRank algorithms including Topic-specific PR and improve the performance of various recommendation-systems using Surprise library
Add a description, image, and links to the personalized-recommendation topic page so that developers can more easily learn about it.
To associate your repository with the personalized-recommendation topic, visit your repo's landing page and select "manage topics."