Implementing user-based and item-based collaborative filtering algorithms on MovieLens dataset and comparing the results.
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Updated
Dec 21, 2020 - Jupyter Notebook
Implementing user-based and item-based collaborative filtering algorithms on MovieLens dataset and comparing the results.
deep learning project
Recommedation of movies to a user based on user rating data.
TensorFlow2 Implementation of "Neural Attentive Item Similarity Model for Recommendation"
Game Recommendation using Collaborative filtering with K-Nearest Neighbor
A collection of diverse recommendation system projects, spanning collaborative filtering, content-based methods, and hybrid approaches.
A web application to recommend music to users based on machine learning algorithms such as item-based & user-based collaborative filtering and kNN.
This repo contains many real-world case-studies of machine learning
Built a Book Recommendation System by using the Item-based collaborative technique.
Recommender systems
Personalised and popularity-based movie recommendations.
Training of machine learning algorithms in order to produce the best model for average rating predictions of a book.
Hybrid RecSys, CF-based RecSys, Model-based RecSys, Content-based RecSys, Finding similar items using Jaccard similarity
Used User-based and Item-based Collaborative Filtering techniques to build a personalized Book Recommendation System
Combines user-based and item-based recommendation systems to deliver personalized movie suggestions, utilizing user preferences and film characteristics.
This repo contains all files needed for building a recommender system based on 2019 Yelp Challenge Datasets. This is the No.1 solution in USC Viterbi Data Mining Competition.
Projects of thesis codes I helped for some master students.
Basic movie recommender system using item based collaborative filtering
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