A step-by-step tutorial on developing a practical recommendation system (retrieval and ranking) using TensorFlow Recommenders and Keras.
Implementing user-based and item-based collaborative filtering algorithms on MovieLens dataset and comparing the results.
Recommendation Engine powered by Matrix Factorization.
Movies Recommendation Systems with Personalization
Movie Recommended System- From Movielens dataset we need to recommend the most rated movie as well as the average rating of the movie.
This project involves using Pyspark to create a recommendation system on the Google Cloud Platform
The repository consists of a recommendation engine that suggests movies to the users based on the genre and ratings previously received. Under the hood, a neural collaborative filtering technique has been implemented
Data analysis and movie recommendation of OpenMovie dataset by using the shell, Python, Cosine Similarity algorithm, Apache PySpark, and Apache Hadoop.
Amazon Personalize / MovieLens inference utility/demo scripts
This is a project made as a part of my data science master's program to analyze and draw inference from Movielens data.
.Personalized recommendation system built on top of a multiplicative LSTM.
Recommendation Systems (Collaborative Filtering) Experiments on MovieLens Datasets
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