Basic movie recommender system using item based collaborative filtering
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Updated
Jul 23, 2019 - Jupyter Notebook
Basic movie recommender system using item based collaborative filtering
deep learning project
Training of machine learning algorithms in order to produce the best model for average rating predictions of a book.
Recommedation of movies to a user based on user rating data.
This repo contains many real-world case-studies of machine learning
Personalised and popularity-based movie recommendations.
Movie Recommendation System using Item-Based
Built a Book Recommendation System by using the Item-based collaborative technique.
TensorFlow2 Implementation of "Neural Attentive Item Similarity Model for Recommendation"
An application that uses the algorithm of user-based collaborative filtering and item-based collaborative filtering to recommend new movies
Game Recommendation using Collaborative filtering with K-Nearest Neighbor
Implementing user-based and item-based collaborative filtering algorithms on MovieLens dataset and comparing the results.
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 i…
Recommender system for board games built on data collected from major board game forum, BoardGameGeek.
This repo has an implementation of popular recommendation techniques like user-based and item-based collaborative filtering techniques for recommending books and music.
Item Based movie Recommendation System.
Hybrid RecSys, CF-based RecSys, Model-based RecSys, Content-based RecSys, Finding similar items using Jaccard similarity
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