two-tower-model

🐊 Anime recommendation web app: enter your MyAnimeList username, get a personalized ranked list. Two-stage recommender: two-tower retrieval (PyTorch) + LightGBM ranking.

Transformer-enhanced two-tower recommender on MovieLens-25M with CL-EPIDTN-style contrastive learning for sparse-user and long-tail robustness. FAISS retrieval, neural reranker, FastAPI serving, MLflow experiment tracking, and Ollama-powered recommendation explanations.

Building Recommendation Systems in Python and JAX — a fork migrated from TensorFlow/JAX/Flax to pure PyTorch (Python 3.11+). Three sub-projects: Pinterest Shop-the-Look (two-tower CNN, triplet loss), Spotify Million Playlist (embedding-based retrieval, contrastive learning), Wikipedia NLP (GloVe co-occurrence + LSTM text-to-URL).

Movie recommendation app that utilizes machine-learning to recommend new movies.

A multimodal hybrid movie recommendation engine that combines Collaborative Filtering, Content-Based Filtering, and a Hybrid Fusion layer, with genuine multimodal features via OpenAI CLIP (poster images + text metadata).

An extended ML portfolio evolving from Monash FIT5196 coursework. Features an award-winning EDA report and a Multimodal Sentiment Classifier (RoBERTa + Swin + LoRA) trained on 5.7M Google Maps reviews.

Interactive RecSys & Time Series Machine Learning portfolio built with Streamlit and Docker

Scalable dual-tower retrieval pipeline for movie recommendations, implementing in-batch negative sampling and ultra-fast FAISS vector search.