Create recommender systems testing various algorithms
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Updated
May 23, 2024 - Jupyter Notebook
Create recommender systems testing various algorithms
A Comparative Framework for Multimodal Recommender Systems
Versatile End-to-End Recommender System
Scraping publicly-accessible Letterboxd data and creating a movie recommendation model with it that can generate recommendations when provided with a Letterboxd username
We are proud to introduce our new book recommendation system, book.io. This system uses the user-to-user collaborative filtering model to recommend books to users based on their preferences and ratings.
A recommender system built from scratch using the collaboration filtering algorithm and NumPy library
This project developed two wine recommendation models using the XWines dataset, employing collaborative filtering and content-based techniques. It leveraged Python, Numpy, Pandas, Jupyter Notebook, VSCode, and Scikit-learn.
Code associated with "Benchmarking collaborative filtering approaches to drug repurposing"
This repository contains a recommendation system implemented using the Apriori algorithm for frequent itemset mining and association rule generation. The recommendation system aims to suggest relevant products to users based on their past purchase history.
Movie Recommendation System using Collaborative Method (User - User similarity , Item-Item similarity)
This repository contains various recommender systems which I have implemented so far.
Food Recommendations System With Content Based Filtering and Collaborative Filtering
The topic is about product matching via Machine Learning. This involves using various machine learning techniques such as natural language processing, image recognition, and collaborative filtering algorithms to match similar products together.
Movie Recommender (School Project)
Collaborative and hybrid recommendation systems
Collaborative Filtering: Item-Item collaborative filtering and User-User collaborative filtering
A Comprehensive Framework for Building End-to-End Recommendation Systems with State-of-the-Art Models
USC DSCI 553 - Foundations & Applications of Data Mining - Spring 2024 - Prof. Wei-Min Shen
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