Recommendation Systems This is a workshop on using Machine Learning and Deep Learning Techniques to build Recommendation Systesm Theory: ML & DL Formulation, Prediction vs. Ranking, Similiarity, Biased vs. Unbiased Paradigms: Content-based, Collaborative filtering, Knowledge-based, Hybrid and Ensembles Data: Tabular, Images, Text (Sequences) Models: (Deep) Matrix Factorisation, Auto-Encoders, Wide & Deep, Rank-Learning, Sequence Modelling Methods: Explicit vs. implicit feedback, User-Item matrix, Embeddings, Convolution, Recurrent, Domain Signals: location, time, context, social, Process: Setup, Encode & Embed, Design, Train & Select, Serve & Scale, Measure, Test & Improve Tools: python-data-stack: numpy, pandas, scikit-learn, keras, spacy, implicit, lightfm Notes & Slides Basics: Deep Learning AI Conference 2019: WhiteBoard Notes | In-Class Notebooks Notebooks Movies - Movielens 01-Acquire 02-Augment 03-Refine 04-Transform 05-Evaluation 06-Model-Baseline 07-Feature-extractor 08-Model-Matrix-Factorization 09-Model-Matrix-Factorization-with-Bias 10-Model-MF-NNMF 11-Model-Deep-Matrix-Factorization 12-Model-Neural-Collaborative-Filtering 13-Model-Implicit-Matrix-Factorization 14-Features-Image 15-Features-NLP Ecommerce - YooChoose 01-Data-Preparation 02-Models News - Hackernews Product - Groceries Python Libraries Deep Recommender Libraries Tensorrec - Built on Tensorflow Spotlight - Built on PyTorch TFranking - Built on TensorFlow (Learning to Rank) Matrix Factorisation Based Libraries Implicit - Implicit Matrix Factorisation QMF - Implicit Matrix Factorisation Lightfm - For Hybrid Recommedations Surprise - Scikit-learn type api for traditional alogrithms Similarity Search Libraries Annoy - Approximate Nearest Neighbour NMSLib - kNN methods FAISS - Similarity search and clustering Learning Resources Reference Slides Deep Learning in RecSys by Balázs Hidasi Lessons from Industry RecSys by Xavier Amatriain Architecting Recommendation Systems by James Kirk Recommendation Systems Overview by Raimon and Basilico Benchmarks MovieLens Benchmarks for Traditional Setup Microsoft Tutorial on Recommendation System at KDD 2019 Algorithms & Approaches Collaborative Filtering for Implicit Feedback Datasets Bayesian Personalised Ranking for Implicit Data Logistic Matrix Factorisation Neural Network Matrix Factorisation Neural Collaborative Filtering Variational Autoencoders for Collaborative Filtering Evaluations Evaluating Recommendation Systems

Scalable, cloud-native recommender system with end-to-end MLOps for building, training, and deploying models in research and production

A Recommender System for E-Commerce using Collaborative filtering

Recommendation of E-commerce products & Sentimental analysis of Customer Reviews Using NLP

A machine learning-based recommendation system designed for an e-commerce platform.

Designed a graph-based recommender system using FAISS for similarity search, for e-commerce applications based on user purchase and search history.

Built a recommender system, recommending the best products for eCommerce shoppers based on their purchase and search history

Multi-Objective Recommender System

A recommender system is used to recommend items to a user based on the judgement whether they would prefer the item or not. This is done by predicting the future preferences of the user on the basis of past preferences and the preferences of similar users. The goal of our project is to create such a recommender system for movies using collaborative filtering approach. For an interactive experience for online users, we are planning to create a data application using open source frameworks (such as Streamlit). We will host the application as a Docker container on GCP and deploy it to Kubernetes cluster using GKE. Our primary goal is to implement the recommender system for movies. Our stretch goal is to extend this system to work with other datasets such as music, ecommerce, etc.

E-Commerce recommender demo with real-time data and a graph database

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Production-grade Hybrid Recommender System combining Sequential Transformers (SASRec) and Offline RL (DQN) with Sim-to-Real training. Features sub-50ms inference via TFLite/FastAPI, Dockerized deployment, and CI/CD pipelines

Sistem rekomendasi produk e-commerce menggunakan pendekatan content based filtering dan collaborative filtering

The motive of this project is to build a recommender system on Ecommerce implicit feedback data and to give you a formulation to represent the same in the Industry.

🛍️RetailBox - eCommerce Recommender System using Machine Learning

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This project is an e-commerce recommendation engine that uses Neo4j and Graph Data Science to provide personalized product recommendations. It includes various recommendation models such as popular products, collaborative filtering, and embedding-based similarity. The project also features a Streamlit app for an interactive user interface.

Recommender System for an Ecommerce App using Collaborative Filtering

A collabarative filtering based recommender system which also addresses the cold start problem and give accurate recommendations to the user based on their previous interactions and purchasing history.

The project develops a recomender system to offer personalized product suggestions based on user specs, enhancing online shopping by simplifying decisions and boosting customer satisfaction.

🛒 AI-Powered E-commerce Product Recommender — A FastAPI web app that suggests products based on user queries and explains each recommendation using Google Gemini (LLM). Includes database integration, modern UI, and live deployment on Render Cloud.

A retrieval based recommendation system with two tower architecture and FAISS

recommendation de site de commerce a unutilisateur en fonction des ses besoins

AI-powered E-commerce Product Recommender with LLM explanations

A Comprehensive CLI-Based E-commerce Platform: Sequential Patterns, Queued Order Management, and Personalized Recommendations

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A production grade ecommerce website with a recommender system pipeline