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The Ad-papers repository is a curated collection of influential research papers focused on the fields of advertising technology, recommendation systems, and applied machine learning in online platforms. The repository organizes academic and industry papers that explore how machine learning algorithms can be used to improve ad targeting, user modeling, click-through rate prediction, and personalized recommendation systems. These papers represent key developments in large-scale industrial machine learning systems used by digital advertising platforms. The repository categorizes papers by topic and provides links to research publications, allowing readers to easily explore the evolution of machine learning techniques in advertising and recommendation domains. Many of the included papers originate from major technology companies and research institutions that have contributed foundational work in applied machine learning systems.
Features
- Curated list of research papers related to advertising technology
- Coverage of recommendation systems and user behavior modeling
- Research on click-through rate prediction algorithms
- Collection of industry and academic machine learning papers
- Organized categories for exploring different advertising ML topics
- Reference resource for studying large-scale recommendation systems
Project Samples
