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Product Recommendation System is a machine learning-based project that provides personalized product recommendations to users based on their interaction history, similar users, and also the popularity of products.
This repository contains a Product Recommendation System built with Python, utilizing TF-IDF vectorization and cosine similarity to provide accurate recommendations based on product descriptions. It features an interactive Streamlit app for user-friendly input and real-time recommendations.
Using algorithms such as collaborative filtering, content-based filtering, or hybrid methods, this recommendation engine offers personalized suggestions to users, enhancing their shopping or browsing experience.
Product Recommendation System is a machine learning-based project that provides personalized product recommendations to users based on their interaction history, similar users, and also the popularity of products.
Personalized recommender system for Sephora's cosmetics e-commerce platform. Using content-based filtering, with TF-IDF Vectorizer to extract product features and cosine similarity to recommend similar items based on user preferences. And collaborative filtering with SVD for identifying user patterns and recommending highly-rated products.
I have improved the demo by using Azure OpenAI’s Embedding model (text-embedding-ada-002), which has a powerful word embedding capability. This model can also vectorize product key phrases and recommend products based on cosine similarity, but with better results. You can find the updated repo here.
Robust product recommendations using topological data analysis. 4-week project completed during Insight Fellows Program, AI Silicon Valley 2020 B Cohort
The sample code repository leverages Azure Text Analytics to extract key phrases from the product description as additional product features. And perform text relationship analysis with TF-IDF vectorization and Cosine Similarity for product recommendation.
This is a code sample repository for online retail product recommendations using Collaborative Filtering (Memory-Based, aka History-Based). The source data used the famous Online Retail Data Set from UCI Machine Learning Repository.