Recommender Systems & Personalization

Share Your Expertise

The field of recommender systems continues to evolve rapidly, with new approaches and applications emerging regularly. Ready Tensor provides a platform for practitioners to showcase their innovations, from novel algorithmic approaches to practical implementations across diverse domains. Share your work to inspire others and gain recognition for your contributions to this dynamic field.

Areas of Interest

Core Approaches:

• Collaborative filtering implementations

• Content-based recommendation systems

• Hybrid recommendation approaches

• Deep learning for recommendations

• Session-based recommendation systems

• Context-aware recommender systems

Advanced Techniques:

• Matrix factorization methods

• Neural collaborative filtering

• Self-attention mechanisms

• Multi-armed bandits for recommendations

• Knowledge graph-based systems

• Cross-domain recommendation systems

Application Domains:

• E-commerce product recommendations

• Content streaming and media suggestions

• Social network connections

• Job and recruitment matching

• Educational resource recommendations

• Travel and hospitality suggestions

We welcome all innovative approaches in recommendation systems, from theoretical advances to practical implementations solving real-world challenges.

Featured Publications Program

Each month, we select outstanding recommender system publications to feature on our platform. Featured publications receive:

• Prominent visibility on Ready Tensor's homepage and recommendation systems category

• Social media promotion to our community of AI professionals

• Digital certificate of recognition

• Cash prize of $200 for exceptional publications

Publications are evaluated based on technical innovation, implementation quality, and practical impact.

All public recommender system publications are automatically considered for this recognition program.

Note: Ready Tensor employees and contractors may contribute publications but are not eligible for cash prizes.

Types of Publications

We welcome publications at all levels of complexity - from student projects to cutting-edge research. What matters is the clarity of presentation and potential value to the community. The following are various types of publications we welcome.

Research & Development:

• Research papers presenting novel recommendation approaches

• Research summaries synthesizing recent advances in the field

• Benchmark studies comparing different recommendation models

• Dataset contributions with annotated recommendation data

• Replication studies of notable papers

Implementation & Applications:

• Industrial applications and deployment case studies

• Implementation guides and best practices

• System architecture and scalability studies

• Kaggle competition solution write-ups and analyses

Open Source & Independent Projects:

• Open-source recommendation libraries and packages

• Tools and utilities for recommender systems

• Personal exploration projects and experiments

• Side projects testing novel approaches

• Customized implementations of research papers

Educational & Academic:

• Student projects and academic implementations

• Course projects and assignments

• Educational resources and learning materials

• Practical tutorials and hands-on guides

How to Publish

1. Create a free Ready Tensor account if you don't already have one.

2. To create a new publication, click on 'Create New' and then 'New Publication' from the left menu.

3. Follow the intuitive workflow. You can copy/paste content in markdown style, and even upload content from your Jupyter Notebooks. It's fast and easy.

4. Tag your publication with relevant keywords like 'recommender-systems', 'collaborative-filtering', or 'personalization' to help others discover your work.

5. Preview your work and click 'Publish' to share it with the community.

Maximize Your Chances of Being Featured

To create a publication that stands out in the recommender systems space, we highly recommend reviewing our comprehensive best practices guide before starting your publication. The guide will help you:

• Select the most suitable format for your RecSys project, whether it's a novel algorithm implementation, evaluation framework, or real-world case study

• Document your recommendation pipeline effectively, from data preprocessing and feature engineering to model architecture and evaluation metrics

• Demonstrate crucial aspects like scalability, real-time performance, and handling of cold-start problems

• Present offline and online evaluation results clearly, including key metrics like NDCG, MAP, and user engagement statistics

• Effectively communicate your system's impact on business metrics and user experience

Access our complete best practices guide here: Engage and Inspire: Best Practices for Publishing on Ready Tensor

Remember: Great RecSys publications often balance technical depth with practical insights. Share not just your model architecture, but also the challenges you encountered and how you addressed them in production.