There are 1 repository under item-based-recommendation topic.
Combines user-based and item-based recommendation systems to deliver personalized movie suggestions, utilizing user preferences and film characteristics.
deep learning project
Hybrid RecSys, CF-based RecSys, Model-based RecSys, Content-based RecSys, Finding similar items using Jaccard similarity
Association Rule Learning, Content Based Recommendation, Item Based Collaborative, Filtering User Based Collaborative Filtering, Model Based Matrix Factorization projects i've done about
TensorFlow2 Implementation of "Neural Attentive Item Similarity Model for Recommendation"
Implementing user-based and item-based collaborative filtering algorithms on MovieLens dataset and comparing the results.
Recommedation of movies to a user based on user rating data.
Created Recommender systems using TMDB movie dataset by leveraging the concepts of Content Based Systems and Collaborative Filtering.
Books recommendation system by collaborative filtering and certain visualization are done on data.
A dashboard to discover and search for Korean TV series. Built using React, Flask, SCSS, Sklearn and Docker.
Training of machine learning algorithms in order to produce the best model for average rating predictions of a book.
Personalised and popularity-based movie recommendations.
基于ItemCF与Springboot的图书商城系统
This repo contains many real-world case-studies of machine learning
This project aims to build a Book recommendation system using methods such as Model, Collaborative, and Content-based filtering.
In this repository, I implement a recommender system using matrix factorization. Here, two types of RS are implemented. First, use the factorized matrix for user and item. and second, rebuild the Adjacency matrix. both approaches are acceptable and implemented in this repo. To factorized the matrix, funk-svd Algorithm is used. you can find his implementation on this link: https://github.com/gbolmier/funk-svd
Recommendation System for an Online Beer Company
Recommendation algorithms
Building a collaborative filtering recommender systems on books dataset
Collaborative project on Content-based Recommendation System Development of NYC Airbnb Open Data.
Used User-based and Item-based Collaborative Filtering techniques to build a personalized Book Recommendation System
Recommender system for board games built on data collected from major board game forum, BoardGameGeek.
in this section will be item based recommender on movies and ratings dataset
In this section, I will create a item-based recommender on the movie dataset
An application that uses the algorithm of user-based collaborative filtering and item-based collaborative filtering to recommend new movies
A collection of diverse recommendation system projects, spanning collaborative filtering, content-based methods, and hybrid approaches.
The project's goal is to create diverse recommendation systems that predict user-item ratings
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.
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.
基于ItemCF与Springboot的图书商城系统-前端页面
USC DSCI 553 - Foundations & Applications of Data Mining - Spring 2024 - Prof. Wei-Min Shen
A book recommendation system made using item-based collaborative filtering
in this section will be item based recommender on movielans dataset