The article discusses the application of web usage mining to improve the scalability, accuracy, and flexibility of recommender systems in e-commerce and beyond. It highlights the importance of tracking user browsing behavior to achieve "mass customization," where vendors can personalize product messages for individual customers on a large scale. The authors categorize current personalization systems into three main types: manual decision rule systems, collaborative filtering systems, and content-based filtering agents. They emphasize the growing trend of incorporating pattern discovery techniques from web usage data to enhance personalization. The process of web personalization is divided into two components: offline data preparation and usage mining tasks, followed by the discovery of usage profiles through various data mining algorithms. The article also details the challenges and techniques involved in data preparation, such as user and session identification, pageview identification, and path completion. It introduces the concept of representing usage profiles as weighted collections of URIs, which allows for flexible and dynamic profiles that capture overlapping interests and significant pageviews. The authors present the WebPersonalizer system, which uses session clustering and association rule hypergraph partitioning to derive usage profiles and provide personalized recommendations. The system's architecture and experimental results are discussed, demonstrating its effectiveness in capturing user behavior and making relevant recommendations. The conclusion emphasizes the potential of web usage mining to eliminate subjectivity and keep profile data updated, enhancing the web experience for users.The article discusses the application of web usage mining to improve the scalability, accuracy, and flexibility of recommender systems in e-commerce and beyond. It highlights the importance of tracking user browsing behavior to achieve "mass customization," where vendors can personalize product messages for individual customers on a large scale. The authors categorize current personalization systems into three main types: manual decision rule systems, collaborative filtering systems, and content-based filtering agents. They emphasize the growing trend of incorporating pattern discovery techniques from web usage data to enhance personalization. The process of web personalization is divided into two components: offline data preparation and usage mining tasks, followed by the discovery of usage profiles through various data mining algorithms. The article also details the challenges and techniques involved in data preparation, such as user and session identification, pageview identification, and path completion. It introduces the concept of representing usage profiles as weighted collections of URIs, which allows for flexible and dynamic profiles that capture overlapping interests and significant pageviews. The authors present the WebPersonalizer system, which uses session clustering and association rule hypergraph partitioning to derive usage profiles and provide personalized recommendations. The system's architecture and experimental results are discussed, demonstrating its effectiveness in capturing user behavior and making relevant recommendations. The conclusion emphasizes the potential of web usage mining to eliminate subjectivity and keep profile data updated, enhancing the web experience for users.