Ever wondered if your recommender system is genuinely valuable to users, even if it's highly accurate? My new post, "Recommender System Evaluation (Part 2)," uncovers the "Accuracy Paradox" and dives into essential user experience metrics. Learn to measure diversity (within lists and across users), assess catalog coverage to combat popularity bias using the Gini coefficient, and quantify novelty and serendipity – guiding users to unexpected, relevant discoveries! 🤩 This article provides clear examples and actionable insights for building systems that truly engage and delight users long-term. It's vital for anyone looking to move beyond simple predictions towards creating beloved experiences. Check it out to elevate your recommender system evaluation strategy! 👇 https://fanyangmeng.blog/recommender-system-evaluation-part-2/

Ever wondered if your recommender system is truly useful, or just technically "accurate"? This deep dive into recommender system evaluation (Part 1!) unpacks why relying solely on traditional accuracy metrics like MAE and RMSE can be a major trap. You can have amazing prediction accuracy but still recommend useless items, like suggesting action movies to a classic drama lover! 🤦‍♀️ We explore the three fundamental questions for evaluation: prediction accuracy, ranking quality, and user experience. This first part meticulously breaks down crucial ranking quality metrics, including Precision@K, Recall@K, Mean Average Precision (MAP), and the gold standard, Normalized Discounted Cumulative Gain (NDCG). You'll learn not just how to calculate these, but when they truly matter for user satisfaction and engagement. Don't fall into the "obviously it's good" trap—get the foundation right for building genuinely useful systems. Check out the post: https://fanyangmeng.blog/recommender-system-evaluation-part-1/

Hybrid recommendation systems are the secret sauce behind successful platforms—no single algorithm can handle real-world complexity alone. The blog post dives into three ways to combine approaches: monolithic hybrids (built into one algorithm), parallelized hybrids (independent systems combined), and pipelined hybrids (sequential processing). Each has its strengths, and the real challenge lies in orchestrating them effectively. 🚀 Discover how to leverage these techniques for your projects here: https://fanyangmeng.blog/hybrid-recommendation-systems/ #RecommendationSystems #MachineLearning

Knowledge-based recommendation systems are a powerful alternative to traditional collaborative filtering, especially in scenarios where user data is scarce or decisions are high-stakes (think buying a house or selecting industrial equipment). 🏠⚙️ These systems use domain knowledge and logical reasoning to provide precise, explainable recommendations. They excel in cold-start situations and complex domains with clear constraints. Want to learn more about how they work and when to use them? Check out this comprehensive guide: https://fanyangmeng.blog/knowledge-based-recommendation-systems/ #AI #Tech #RecommendationSystems

Struggling with the cold start problem in recommendation systems? Content-based approaches are here to save the day! Unlike collaborative filtering, which relies on user behavior, content-based systems analyze item features to make recommendations. This makes them perfect for new platforms or niche content. 🚀📊 Key techniques include TF-IDF for text representation and Naive Bayes for classification. Want to learn more about how these systems work and when to use them? Dive into the full blog post here: https://fanyangmeng.blog/content-based-recommendation-systems/ #RecommendationSystems #MachineLearning #ContentBased

Struggling with the limitations of collaborative filtering? Matrix Factorization (MF) was the game-changer that transformed recommendation systems by focusing on hidden user preferences and item characteristics instead of direct similarities. 🚀 From tackling scalability issues to improving accuracy, MF’s approach of learning latent factors has become foundational in modern recommendation engines. Whether you’re dealing with sparse data or aiming for better generalization, understanding MF is key. Dive into the full post to explore how this breakthrough works and why it’s still relevant today. #MachineLearning #RecommendationSystems https://fanyangmeng.blog/learning-recommendation-systems-matrix-factorization-fundamentals/

Ever wondered how Netflix and Amazon always seem to know what you'll love? The answer lies in Collaborative Filtering (CF), a cornerstone of modern recommendation systems. 🚀 This post takes you through the basics of CF, from user-based and item-based approaches to tackling challenges like sparsity and scalability. Whether you're a software engineer or just curious about the tech behind your favorite platforms, this guide has you covered. Dive in and discover the magic behind personalized recommendations! #RecommendationSystems #MachineLearning #Tech https://fanyangmeng.blog/learning-recommendation-systems-collaborative-filtering/

Struggling with Ghost CDN cache delays? Here's a game-changer for BunnyCDN users! 🚀✨ Automate cache purging with BunnyCDN Edge Scripts—no extra servers or complex setups required. The script detects Ghost's X-Cache-Invalidate header and instantly clears the cache for updated content, ensuring your visitors always see the latest version. It's lightweight, efficient, and easy to implement. Dive into the details and give it a try: https://fanyangmeng.blog/ghost-bunnycdn-cache-purging-middleware-scripts/ #GhostCMS #CDN #Automation

Ever tried finding the minimum depth of a binary tree? 🌳 It’s not as simple as it sounds! The challenge lies in correctly identifying the shortest path to a leaf node (one with no children). This post dives into the nuances, common pitfalls, and multiple solution approaches—recursive and iterative—to tackle this problem effectively. Whether you're a coding enthusiast or prepping for interviews, this is a gem! Read more here: https://fanyangmeng.blog/111-minimum-depth-of-binary-tree/ #LeetCode #BinaryTree #Coding

Rewriting my Ghost MCP server from Python to TypeScript was a transformative experience! 🚀 The new version is not only more stable but also easier to maintain and deploy. By switching to TypeScript, I leveraged the official @tryghost/admin-api library, which simplified authentication, request handling, and error management. The result? A cleaner, more reliable codebase with full API coverage. 💡 Key lesson: Official libraries can save you tons of time and headaches. If you're working with Ghost CMS or integrating AI tools, give this a read! https://fanyangmeng.blog/from-python-to-typescript-improving-ghost-mcp-server/