This paper presents a contextual bandit approach for personalized news article recommendation. The authors model the problem as a contextual bandit, where a learning algorithm sequentially selects articles based on user and article context, and adapts its strategy based on user feedback to maximize total clicks. The key contributions are: (1) a new efficient contextual bandit algorithm, LinUCB, with strong regret bounds; (2) an offline evaluation method using previously recorded traffic; and (3) successful application of LinUCB to a Yahoo! Front Page dataset, achieving a 12.5% click lift over a standard context-free bandit algorithm. The paper addresses the challenges of dynamic content pools and scalability in web services. It formulates the problem as a K-armed contextual bandit, where each article is an arm and user context is used to select the most relevant article. The authors propose LinUCB, which extends the UCB approach to linear models, and show it outperforms other algorithms in both offline and online settings. They also introduce a hybrid model that combines shared and arm-specific features, improving learning efficiency. The paper evaluates the performance of LinUCB and other algorithms on a real-world dataset of over 33 million events. Results show that LinUCB achieves a 12.5% click lift compared to a standard context-free bandit algorithm, with even greater advantages when data is scarce. The authors also propose an offline evaluation method that allows reliable assessment of bandit algorithms using previously recorded traffic, which is crucial for evaluating algorithms in real-world scenarios where live testing is infeasible. The paper highlights the importance of balancing exploration and exploitation in recommendation systems, and demonstrates how contextual bandit methods can be applied to personalize news recommendations. The proposed LinUCB algorithm is shown to be computationally efficient and effective in both offline and online settings, with strong theoretical guarantees. The hybrid model further improves learning efficiency by leveraging shared features across articles. The results demonstrate the effectiveness of contextual bandit methods in personalized news recommendation, and the importance of using offline evaluation for algorithm development in real-world applications.This paper presents a contextual bandit approach for personalized news article recommendation. The authors model the problem as a contextual bandit, where a learning algorithm sequentially selects articles based on user and article context, and adapts its strategy based on user feedback to maximize total clicks. The key contributions are: (1) a new efficient contextual bandit algorithm, LinUCB, with strong regret bounds; (2) an offline evaluation method using previously recorded traffic; and (3) successful application of LinUCB to a Yahoo! Front Page dataset, achieving a 12.5% click lift over a standard context-free bandit algorithm. The paper addresses the challenges of dynamic content pools and scalability in web services. It formulates the problem as a K-armed contextual bandit, where each article is an arm and user context is used to select the most relevant article. The authors propose LinUCB, which extends the UCB approach to linear models, and show it outperforms other algorithms in both offline and online settings. They also introduce a hybrid model that combines shared and arm-specific features, improving learning efficiency. The paper evaluates the performance of LinUCB and other algorithms on a real-world dataset of over 33 million events. Results show that LinUCB achieves a 12.5% click lift compared to a standard context-free bandit algorithm, with even greater advantages when data is scarce. The authors also propose an offline evaluation method that allows reliable assessment of bandit algorithms using previously recorded traffic, which is crucial for evaluating algorithms in real-world scenarios where live testing is infeasible. The paper highlights the importance of balancing exploration and exploitation in recommendation systems, and demonstrates how contextual bandit methods can be applied to personalize news recommendations. The proposed LinUCB algorithm is shown to be computationally efficient and effective in both offline and online settings, with strong theoretical guarantees. The hybrid model further improves learning efficiency by leveraging shared features across articles. The results demonstrate the effectiveness of contextual bandit methods in personalized news recommendation, and the importance of using offline evaluation for algorithm development in real-world applications.