TensorFlow2 Implementation of "Neural Attentive Item Similarity Model for Recommendation"
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Updated
Nov 6, 2020 - Python
TensorFlow2 Implementation of "Neural Attentive Item Similarity Model for Recommendation"
Recommedation of movies to a user based on user rating data.
deep learning project
Implementing user-based and item-based collaborative filtering algorithms on MovieLens dataset and comparing the results.
A collection of diverse recommendation system projects, spanning collaborative filtering, content-based methods, and hybrid approaches.
Game Recommendation using Collaborative filtering with K-Nearest Neighbor
A web application to recommend music to users based on machine learning algorithms such as item-based & user-based collaborative filtering and kNN.
Built a Book Recommendation System by using the Item-based collaborative technique.
Recommender systems
This repo contains many real-world case-studies of machine learning
Personalised and popularity-based movie recommendations.
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.
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
Projects of thesis codes I helped for some master students.
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.
Basic movie recommender system using item based collaborative filtering
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