Improving YouTube Recommender Systems for Big Screen Users
Dive into the complexities of YouTube's recommendation systems, exploring challenges and solutions for delivering personalized content to billions of users across different platforms.
Vector Search for Content-Based Video Recommendation
Discover how to implement vector search for video recommendations using Qdrant, exploring practical applications, system architecture, and performance optimization techniques for high-scale platforms.
Understanding AI Recommendation Engines - From Collaborative Filtering to Personalization
Discover how recommendation engines use machine learning to power personalized experiences, from collaborative filtering to content-based approaches that shape our online interactions.
Inference of Probabilistic Programs with Moment-Matching Gaussian Mixtures
Explore a novel approach to probabilistic program inference using Gaussian mixtures, offering improved accuracy and efficiency for complex models and distributions.
Author Interview - Memory-Assisted Prompt Editing to Improve GPT-3 After Deployment
Explore a method to improve GPT-3's performance post-deployment by maintaining interaction memory and dynamically adapting prompts, enhancing accuracy without retraining the model.
Let's Build a Music Recommender!
Build a music recommender using AudioSet dataset, embedding generation, and ANNOY for efficient similarity search. Learn to process audio data and create a recommendation engine.
Evaluation Measures for Search and Recommender Systems
Learn popular offline metrics for evaluating search and recommender systems, including Recall@K, MRR, MAP@K, and NDCG@K, with Python demonstrations and practical insights for improving information retrieval systems.
Personalized Machine Learning
Explore principles of personalized machine learning across recommender systems, language processing, and computer vision. Learn about techniques, applications, and ethical considerations in tailoring predictions to individual users.
Building Recommender System in PHP8
Develop a fully functional Movie Recommendation Engine using PHP8 and Kaggle datasets. Learn recommendation system basics and create a customizable solution for personalized movie suggestions.
Recommender Systems - Beyond Machine Learning with Joe Konstan
Explore advanced recommender systems beyond machine learning, focusing on user behavior and experience. Learn about successes, failures, and innovative approaches to personalization and evaluation.
Matrix Factorization Techniques for Recommender Systems
Explore matrix factorization techniques for building effective recommender systems, covering key concepts and practical implementation strategies.
Paper Time - The Netflix Recommender System
Explore Netflix's innovative recommender system, its algorithms, and impact on user experience in this in-depth analysis of personalized content delivery.
Design of Recommendation Systems - Recommender Systems Using Deep Learning Explained
Comprehensive overview of recommender systems, covering types, models, performance metrics, and design considerations. Equips learners with knowledge to create and evaluate effective recommendation systems.
Machine Learning Techniques and Applications in Finance, Healthcare and Recommendation Systems
Explore state-of-the-art machine learning techniques and their applications in finance, healthcare, and recommendation systems, focusing on ensemble methods, collaborative filtering, and portfolio construction.
Poisson Random Fields for Dynamic Feature Models
Explore dynamic feature allocation models using Poisson random fields. Learn about the Wright-Fisher Indian Buffet Process and its application to topic modeling for time-stamped documents, with a focus on NIPS papers.
Better Decisions with Machine Learning - Peter Flach
Explore machine learning techniques for better decision-making, including ROC curves, calibration methods, and precision-recall-gain curves. Learn to adapt models to deployment contexts and measure ML performance.
Spotify - Personalising the Listening Experience - Mounia Lalmas
Explore Spotify's approach to personalizing music recommendations using machine learning, focusing on user intents, relevance, diversity, and satisfaction to enhance the listening experience.
Reinforcement Learning in Recommender Systems - Some Challenges
Explore challenges in applying reinforcement learning to recommender systems, including scale, stochastic action sets, and user learning over long horizons. Gain insights on addressing these issues.
Deep Learning for Search and Recommender Systems in Practice
Explore deep learning techniques for search and recommendation systems, covering document retrieval, ranking, personalization, and evaluation metrics. Learn about pointwise, pairwise, and listwise ranking approaches.
Deep Learning for Search and Recommender Systems in Practice - Part 2
Explore advanced techniques in deep learning for search and recommender systems, focusing on practical implementation and real-world applications at LinkedIn.
Deep Learning for Search and Recommender Systems in Practice - Part 1
Explore practical applications of deep learning in search and recommender systems, focusing on query understanding, candidate retrieval, and ranking techniques.