Introduction to KubeFlow: Using and Use Cases
Explore KubeFlow for machine learning on Kubernetes, covering components, use cases, and a practical recommender system example with collaborative filtering and TensorFlow.
Collaborative Top Distribution Identifications with Limited Interaction
Explore collaborative learning strategies for efficient multi-armed bandit problem-solving, focusing on top-m identifications with limited interactions and error reduction techniques.
Machine Learning and Link Prediction
Explore how combining machine learning with graph data structures enhances prediction accuracy by incorporating contextual information and relationships, demonstrated through academic collaboration analysis.
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.
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
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.
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.
Building Recommendation Systems with Vector Search - Women of Search
Explore building recommendation systems using vector search, focusing on Ref2Vec for user representation and bipartite graphs for user-product connections. Learn to enhance e-commerce experiences through advanced search techniques.
Personalizing Search Using Multimodal Latent Behavioral Embeddings
Explore personalized search using multimodal latent behavioral embeddings. Learn to integrate user behavior into modern search retrieval pipelines for RAG and end-user search, enhancing relevance and user experience.