Recommendation Systems

AI-Powered E-commerce: Building Recommendation Systems and Personalization Note: This guide is based on established recommendation system algorithms documented in RecSys research papers, scikit-learn documentation, and production patterns from e-commerce platforms like Amazon, Netflix, and Shopify. All code examples use documented machine learning libraries and follow industry best practices for recommendation systems. AI has transformed e-commerce from generic shopping experiences to hyper-personalized customer journeys. Recommendation systems—the technology behind “Customers who bought this also bought” and personalized homepages—drive 35% of Amazon’s revenue and 75% of Netflix viewing. ...

Implementing Responsible AI in Recommendation Systems: A Step-by-Step Guide Introduction Recommendation systems are ubiquitous in modern applications, influencing everything from our social media feeds to our online shopping experiences. However, these systems can perpetuate biases and lack transparency, leading to unintended consequences. In this article, we’ll explore the importance of responsible AI in recommendation systems and provide a step-by-step guide on implementing strategies for mitigating bias and ensuring transparency. Prerequisites Basic understanding of recommendation systems and their applications Familiarity with machine learning concepts and Python programming language Access to a dataset for experimentation (e.g., MovieLens, Book-Crossing) Identifying and Understanding Bias in Recommendation Systems Bias in recommendation systems refers to the unfair or discriminatory treatment of certain groups or individuals. There are several types of bias that can occur in recommendation systems, including: ...