A step-by-step tutorial on developing a practical recommendation system (retrieval and ranking) using TensorFlow Recommenders and Keras.
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Updated
Mar 30, 2023 - Jupyter Notebook
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.
Movie Recommended System- From Movielens dataset we need to recommend the most rated movie as well as the average rating of the movie.
Data analysis and movie recommendation of OpenMovie dataset by using the shell, Python, Cosine Similarity algorithm, Apache PySpark, and Apache Hadoop.
This project involves using Pyspark to create a recommendation system on the Google Cloud Platform
Amazon Personalize / MovieLens inference utility/demo scripts
Recommendation Engine powered by Matrix Factorization.
Movies Recommendation Systems with Personalization
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.
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
Recommendation Systems (Collaborative Filtering) Experiments on MovieLens Datasets
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