A customer segmentation and product recommendation system using RFM Analysis, Market Basket Analysis & Item Based Collaborative Filter
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Updated
Jun 10, 2024 - Jupyter Notebook
A customer segmentation and product recommendation system using RFM Analysis, Market Basket Analysis & Item Based Collaborative Filter
USC DSCI 553 - Foundations & Applications of Data Mining - Spring 2024 - Prof. Wei-Min Shen
基于ItemCF与Springboot的图书商城系统-前端页面
基于ItemCF与Springboot的图书商城系统
Combines user-based and item-based recommendation systems to deliver personalized movie suggestions, utilizing user preferences and film characteristics.
This project developed and optimized a hybrid recommendation system that processes over 450,000 training data points and 142,000 validation data points. The system combines user ratings, merchant details, and user reviews to predict users' ratings for restaurants they have not visited.
The assignment comprises two main tasks: implementing LSH to identify similar businesses based on user ratings and developing various collaborative filtering recommendation systems to predict user ratings for businesses.
in this section will be item based recommender on movies and ratings dataset
in this section will be item based recommender on movielans dataset
Recommendation algorithms
Using the MovieLens 20 Million review dataset, this project aims to explore different ways to design, evaluate, and explain recommender systems algorithms. Different item-based and user-based recommender systems are showcased as well as a hybrid algorithm using a modified page-rank algorithm.
A collection of diverse recommendation system projects, spanning collaborative filtering, content-based methods, and hybrid approaches.
Building a collaborative filtering recommender systems on books dataset
The project's goal is to create diverse recommendation systems that predict user-item ratings
Collaborative project on Content-based Recommendation System Development of NYC Airbnb Open Data.
TMDB_5000_Movie_recommendation_system is a repository for a hybrid movie recommendation system. Discover personalized movie recommendations based on user preferences and movie features using the TMDB 5000 Movies dataset.
Recommendation System for an Online Beer Company
Used User-based and Item-based Collaborative Filtering techniques to build a personalized Book Recommendation System
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