Overview

Deep Learning Based Recommendation Systems are at the forefront of digital innovation, transforming how users discover new products, music, or even the best routes to take. This research work dives deep into the various techniques and technologies that make up these sophisticated systems, providing a unified repository of common recommendation methods aimed at solving the collaborative filtering problem. It's an exciting exploration that showcases not only foundational concepts like matrix factorization but also the latest advancements in deep learning architectures.

By utilizing the MovieLens1M Dataset, this work leverages a rich collection of real-world user ratings to develop and evaluate a series of models. From classic techniques to cutting-edge approaches, the research is poised to contribute significantly to the recommendation systems field.

Features

Diverse Recommendation Methods: Offers a coherent repository featuring classic matrix factorization to modern deep neural networks, addressing varied recommendation scenarios.
Comprehensive Dataset: Utilizes the well-known MovieLens1M Dataset, consisting of over a million anonymous ratings, providing a solid ground for experiments.
Multiple Model Variants: Includes a variety of models across Matrix Factorization, Multilayer Perceptron, Autoencoders, and Boltzmann Machines, enabling extensive research possibilities.
API-Driven Architecture: Built on a Django web service, it supports numerous API endpoints, each able to host various research algorithms in different versions.
Continuous Integration: Features fast deployments and robust tests for both server and research code, promoting a seamless development experience.
Scalability: Designed for scalability through container deployment, ensuring the system can handle increased load effectively.
Monitoring and Diagnostics: Supports monitoring and algorithm diagnostics, including capabilities for A/B testing to elevate performance analysis.
Informative Blog Posts: Accompanied by a series of blog posts detailing the experimental process, making the research accessible and informative for others in the field.

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