A step-by-step tutorial on developing a practical recommendation system (retrieval and ranking) using TensorFlow Recommenders and Keras.
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Updated
Jun 20, 2024 - Jupyter Notebook
A step-by-step tutorial on developing a practical recommendation system (retrieval and ranking) using TensorFlow Recommenders and Keras.
Recommendation Systems (Collaborative Filtering) Experiments on MovieLens Datasets
Movies Recommendation Systems with Personalization
Recommendation Engine powered by Matrix Factorization.
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
This project involves using Pyspark to create a recommendation system on the Google Cloud Platform
Data analysis and movie recommendation of OpenMovie dataset by using the shell, Python, Cosine Similarity algorithm, Apache PySpark, and Apache Hadoop.
Implementing user-based and item-based collaborative filtering algorithms on MovieLens dataset and comparing the results.
Amazon Personalize / MovieLens inference utility/demo scripts
Movie Recommended System- From Movielens dataset we need to recommend the most rated movie as well as the average rating of the movie.
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