Combines user-based and item-based recommendation systems to deliver personalized movie suggestions, utilizing user preferences and film characteristics.
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Updated
Apr 1, 2024 - Python
Combines user-based and item-based recommendation systems to deliver personalized movie suggestions, utilizing user preferences and film characteristics.
deep learning project
Implementing user-based and item-based collaborative filtering algorithms on MovieLens dataset and comparing the results.
Hybrid RecSys, CF-based RecSys, Model-based RecSys, Content-based RecSys, Finding similar items using Jaccard similarity
TensorFlow2 Implementation of "Neural Attentive Item Similarity Model for Recommendation"
Recommedation of movies to a user based on user rating data.
Association Rule Learning, Content Based Recommendation, Item Based Collaborative, Filtering User Based Collaborative Filtering, Model Based Matrix Factorization projects i've done about
Books recommendation system by collaborative filtering and certain visualization are done on data.
Training of machine learning algorithms in order to produce the best model for average rating predictions of a book.
Created Recommender systems using TMDB movie dataset by leveraging the concepts of Content Based Systems and Collaborative Filtering.
Personalised and popularity-based movie recommendations.
基于ItemCF与Springboot的图书商城系统
Used User-based and Item-based Collaborative Filtering techniques to build a personalized Book Recommendation System
This project aims to build a Book recommendation system using methods such as Model, Collaborative, and Content-based filtering.
Recommender system for board games built on data collected from major board game forum, BoardGameGeek.
Recommendation algorithms
This repo contains many real-world case-studies of machine learning
USC DSCI 553 - Foundations & Applications of Data Mining - Spring 2024 - Prof. Wei-Min Shen
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