BrainAPI is a knowledge graph–powered AI memory layer that transforms unstructured data into structured knowledge, enabling intelligent search, recommendations, and contextual memory for AI agents and applications.
cosmetic recommendation system using image analysis to identify skin concerns and suggest suitable products.
An interactive, chat-driven learning system and production reference architecture for recommendation-system engineering, built with FastAPI, LangGraph, Ollama, and pgvector, and fronted by a modern Next.js 16 / React 19 App Router lab UI.
Full-stack AI-powered wallpaper gallery featuring personalized recommendations, colour palette search, shareable collections, Firebase auth and a taste profile that learns from every like, view and download.
DeepShelf is your AI-powered book buddy 📚🤖 — type what you’re in the mood for, and it finds the perfect novel using smart search + ranking ✨🔍
Simple recommendation system powered by reinforcement learning
Two-stage sequential recommender on MovieLens 1M: BERT4Rec retrieval + DIN reranking + RRF fusion. Full-corpus HR@10 = 0.2974, NDCG@10 = 0.1692. Built from scratch in PyTorch.
Creating an Product Recommender System with Apriori and FPGrowth.
AniZenith is an intelligent chatbot that provides personalized anime recommendations based on user preferences. It features a production-ready full-stack MLOps architecture, including a FastAPI backend, decoupled frontend services, automated CI/CD pipelines, and RAG-MCP–based reasoning.
FRUDRERA is an AI-powered recipe recommender that suggests recipes based on the ingredients detected in a photo of your fridge. It utilizes object detection and OCR to identify ingredients and recommend recipes accordingly.
A social music app to share music experiences with friends
A full-stack movie recommendation system built with FastAPI, Streamlit, and Machine Learning. The application combines a TF-IDF content-based recommendation engine with live TMDB movie data to provide personalized movie suggestions, rich movie details, posters, ratings, and genre-based recommendations.
⚡ Blazing-fast graph processing engine in Rust with ML-powered optimization. 50-200x faster than Python/JS. Self-tuning algorithms, real-time anomaly detection, and sub-10ms queries on million-node graphs. 🚀🧠
A Python-based social network analysis project that cleans JSON data and implements friend and page recommendation systems using mutual connections and shared interests.
An AI-based inventory optimization system that leverages machine learning to predict demand, recommend menu items, and streamline stock management for restaurants and food service businesses.. — all deployed through a real-time Stream lit web app.
This project was done to fulfil the Machine Learning Terapan 2nd assignment submission on Dicoding. The domain used in this project is book recommendation.
This is a collaborative filtering based books recommender system & a streamlit web application that can recommend various kinds of similar books based on an user interest.
SOEN471 Project - Team 10 - Winter 2024
AI-powered skincare recommendation system using NLP, TF-IDF vectorization, cosine similarity, and explainable AI for personalized skincare suggestions.
An intelligent activity-based music recommendation platform that enhances music discovery by aligning songs with users’ real-world contexts.
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