swap-253/Recommender-Systems-Using-ML-DL-And-Market-Basket-Analysis

This repository consists of collaborative filtering Recommender systems like Similarity Recommenders, KNN Recommenders, using Apple's Turicreate, A matrix Factorization system from scratch and a Deep Learning Recommender System which learns using embeddings. Besides this Market Basket Analysis using Apriori Algorithm has also been done. Deployment of Embedding Based Recommender Systems have also been done on local host using Streamlit, Fast API and PyWebIO.

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This project helps businesses and content platforms recommend items to users, like movies, products, or articles. By analyzing user ratings and preferences, it takes in historical interaction data and outputs a personalized list of suggestions. Anyone responsible for improving user engagement or sales through recommendations would find this useful, such as e-commerce managers, content strategists, or product owners.

No commits in the last 6 months.

Use this if you need to build or understand how different recommendation systems work, from basic similarity models to deep learning approaches and market basket analysis.

Not ideal if you're looking for a production-ready, highly scalable, and fully managed recommendation service without needing to delve into the underlying code and algorithms.

e-commerce content-personalization customer-engagement product-recommendation data-driven-marketing
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 8 / 25
Community 15 / 25

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Last pushed

Feb 11, 2024

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