Deep-Recommender-System

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deep-recommender-system is an open-source project that provides key Deep Learning engineering tricks for recommendation systems. It includes implementations of various models like Supervised Contrastive Learning and BERT4Rec.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is deep-recommender-system?

deep-recommender-system is an open-source GitHub project dedicated to showcasing key Deep Learning engineering tricks for recommendation systems. It offers implementations and overviews of various advanced models, including Supervised Contrastive Learning, Multi-Interest Network with Dynamic Routing for Recommendation at Tmall, BERT4Rec for sequential recommendation, and Deep Neural Networks for YouTube Recommendations. The project also covers foundational concepts like Wide & Deep Learning and Collaborative Deep Learning. It serves as a valuable resource for understanding and applying deep learning techniques in real-world recommendation scenarios, with detailed explanations and practical insights.

Best used for

Ideal for developers and data scientists who need to implement advanced deep learning models for recommendation systems, understand user behavior sequences, and improve search ranking. Especially valuable for those looking for practical engineering tricks and implementations of state-of-the-art recommender algorithms.

Common actions

implement recommendation systems

learn deep learning models

explore recommender algorithms

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Capabilities

Key features

Supervised Contrastive Learning
BERT4Rec implementation
Multi-Interest Network
YouTube DNN recommendations
Wide & Deep Learning
Collaborative Deep Learning

Target Audience

developer

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of deep learning models are covered in deep-recommender-system?

The project covers a range of deep learning models relevant to recommendation systems, including Supervised Contrastive Learning, BERT4Rec, Multi-Interest Network with Dynamic Routing, Deep Neural Networks for YouTube Recommendations, Wide & Deep Learning, and Collaborative Deep Learning. It provides insights into their implementation and application.

Is deep-recommender-system suitable for beginners in deep learning?

While the project offers detailed explanations, the content focuses on advanced engineering tricks and implementations of complex models. Users should have a solid understanding of deep learning fundamentals and recommendation system concepts to fully leverage the resources provided.

Can I contribute to the deep-recommender-system project?

As an open-source project hosted on GitHub, contributions are generally welcome. Users can contribute by creating an account on GitHub and following the standard procedures for submitting issues, pull requests, or suggesting improvements to the existing code and documentation.

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