Predicting the Future with Machine Learning
Explore machine learning's power to predict future events, from flight delays to sentiment analysis, with practical steps for building and deploying predictive models using Azure tools.
Principles and Practices of Building Machine Learning Systems
Explore challenges and solutions in deploying machine learning models to production, focusing on infrastructure, monitoring, and tools, with insights from large-scale recommender systems at Storytel.
The Effect of Explanations and Algorithmic Accuracy on Visual Recommender Systems of Artistic Images
Explores impact of explanations and algorithm accuracy on user perception of artistic image recommender systems, revealing interplay between interface design and underlying algorithms in user experience.
Using Text Embedding Algorithms in Recommender Systems
Explore text embedding algorithms' surprising effectiveness in recommendation systems, outperforming state-of-the-art models while offering efficiency for large-scale applications.
Deep Learning for Recommender Systems
Explore state-of-the-art deep learning techniques for recommender systems, including collaborative filtering, content-based methods, and complex architectures like autoencoders and RNNs for personalized recommendations.
Speeding Up Search With Locality Sensitive Hashing
Explore locality sensitive hashing for faster search and similarity comparisons, with practical Python implementations and real-world applications in image search and collaborative filtering.
Building the Perfect Personalised Menu Using Python
Explore how Gousto uses Python for menu planning optimization and personalized recommendations, leveraging genetic algorithms and graph databases to enhance customer experience and operational efficiency.
Build a Recommender System in Apache Spark and Integrate It Using Akka
Learn to build and integrate a recommender system using Apache Spark and Akka HTTP. Discover the components of recommender systems and implement one with machine learning techniques in a practical, hands-on session.
Build a Recommender System in Apache Spark and Integrate it Using Akka
Learn to build and integrate a recommender system using Apache Spark and Akka HTTP. Discover the basics of machine learning and how to implement it in your applications for personalized recommendations.
Exploration of Novel Visualizations for Information Security Data
Explore innovative data visualization techniques for information security, covering ELQ Stack, organic visualization, machine learning, anomaly detection, and similarity engines.
Telco Data 기반 멀티 도메인 추천 엔진 - 협력적 게임이론 기반 순차 추천모델
Discover SK텔레콤의 혁신적인 멀티 도메인 추천 엔진 개발 과정과 구현 방법을 통해 데이터 기반 순차적 추천 시스템의 실제 적용 사례와 성과를 살펴보는 기술 세션
From Ethics to Law in the Metaverse - Recommender Systems as Common Good
Explore the ethical and legal implications of recommender systems in the metaverse, examining their role as a public resource and their impact on digital society.
Unmasking Privacy: A Reproduction and Evaluation Study of Perturbation Techniques for Collaborative Filtering
Explore privacy-preserving techniques in collaborative filtering systems through a comprehensive reproduction study evaluating obfuscation methods and their effectiveness in data protection.
Latent User Intent Modeling in Recommender Systems
Explore latent variable models for capturing user intents in recommender systems, enhancing understanding beyond item-level interactions.
Building Recommender Systems with Large Language Models
Explore recent approaches to building recommender systems using LLMs, focusing on language understanding and generation templates for various recommendation tasks.
RAG and RecSys - Parallels in Retrieval Quality Improvement
Explore parallels between RAG and recommender systems, focusing on improving retrieval quality through hybrid reranking techniques using LanceDB in this concise AI production talk.
Vector Search Over Complex Semi-Structured Data
Explore production-grade vector search systems combining unstructured and structured data, with focus on user objectives and feedback loops for improved results in various applications.
How Does Netflix Recommend Movies? Matrix Factorization
Explore matrix factorization in recommender systems, focusing on Netflix's movie recommendation algorithm. Learn about dependencies, error functions, and predicting user ratings.
Optimizing the Performance of Machine Learning in Enterprise Java SaaS with GraalVM, Python and CUDA
Enhance Java SaaS with GraalVM: integrate Python ML libraries, leverage GPU acceleration for model training, and implement recommender systems in enterprise applications.
Inside TensorFlow - TF-Agents
Explore TF-Agents, TensorFlow's reinforcement learning library, with experts demonstrating its capabilities, architecture, and applications in game playing, bandits, and recommender systems.
Building Decision-Making Assistants: Introduction to Recommendation Systems
Explore recommendation systems from basic classification to advanced Netflix-style matrix factorization, covering content filtering, collaboration models, SVD, and PCA techniques.