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An item-based recommender model that computes cosine similarity for each item pairs using the item factors matrix generated by Spark MLlib’s ALS algorithm and recommends top 5 items based on the selected item.
Robust product recommendations using topological data analysis. 4-week project completed during Insight Fellows Program, AI Silicon Valley 2020 B Cohort
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.