Web personalization is the process of customizing a website to the needs of specific users by analyzing their navigational behavior and other data such as content and structure. This article surveys the use of web mining for web personalization, introducing the modules that make up a web personalization system, with a focus on the web usage mining module. It reviews common methods, technical issues, and popular tools and applications. The article also presents important research initiatives in web usage mining and personalization. Web personalization involves categorizing and preprocessing web data, extracting correlations between different data types, and determining actions to recommend. Web data include content, structure, usage, and user profile data. The process of usage-based web personalization consists of five modules: user profiling, log analysis and web usage mining, content management, web site publishing, and information acquisition and searching. User profiling involves gathering information about each visitor, either explicitly or implicitly. Log analysis and web usage mining process server logs to extract statistical information and discover usage patterns. Content management classifies website content into semantic categories. Web site publishing presents content in a uniform way to users. Information acquisition and searching involves searching for content from various sources and classifying it. Web usage mining is the process of applying statistical and data mining methods to web log data to discover useful patterns. Techniques include association rule mining, sequential pattern discovery, clustering, and classification. Web usage mining is used to personalize websites by analyzing user behavior and preferences. User profiling involves collecting information about users, either explicitly through registration forms or implicitly through navigational behavior. Privacy issues arise in user profiling, and P3P is a standard that enables websites to express privacy practices in a standardized format. Log analysis and web usage mining involve processing server logs to extract statistical information and discover usage patterns. Web data abstractions include web site, user, session, pageviews, and clickstreams. Data preprocessing involves cleaning log data, filtering out irrelevant information, and identifying users and sessions. Web usage mining is a powerful tool for corporations in the e-business sector, helping them gain business intelligence by analyzing consumer behavior. Web usage mining techniques can help smaller organizations improve their systems and customize their websites. There are many tools and applications available for web usage mining, ranging from free traffic analysis tools to integrated CRM solutions. Research initiatives in web usage mining and personalization focus on extracting useful patterns and rules using data mining techniques to understand user behavior. These initiatives combine methods such as user profiling, web usage mining, content management, and publishing mechanisms to provide more integrated views of website usage and more effective personalization.Web personalization is the process of customizing a website to the needs of specific users by analyzing their navigational behavior and other data such as content and structure. This article surveys the use of web mining for web personalization, introducing the modules that make up a web personalization system, with a focus on the web usage mining module. It reviews common methods, technical issues, and popular tools and applications. The article also presents important research initiatives in web usage mining and personalization. Web personalization involves categorizing and preprocessing web data, extracting correlations between different data types, and determining actions to recommend. Web data include content, structure, usage, and user profile data. The process of usage-based web personalization consists of five modules: user profiling, log analysis and web usage mining, content management, web site publishing, and information acquisition and searching. User profiling involves gathering information about each visitor, either explicitly or implicitly. Log analysis and web usage mining process server logs to extract statistical information and discover usage patterns. Content management classifies website content into semantic categories. Web site publishing presents content in a uniform way to users. Information acquisition and searching involves searching for content from various sources and classifying it. Web usage mining is the process of applying statistical and data mining methods to web log data to discover useful patterns. Techniques include association rule mining, sequential pattern discovery, clustering, and classification. Web usage mining is used to personalize websites by analyzing user behavior and preferences. User profiling involves collecting information about users, either explicitly through registration forms or implicitly through navigational behavior. Privacy issues arise in user profiling, and P3P is a standard that enables websites to express privacy practices in a standardized format. Log analysis and web usage mining involve processing server logs to extract statistical information and discover usage patterns. Web data abstractions include web site, user, session, pageviews, and clickstreams. Data preprocessing involves cleaning log data, filtering out irrelevant information, and identifying users and sessions. Web usage mining is a powerful tool for corporations in the e-business sector, helping them gain business intelligence by analyzing consumer behavior. Web usage mining techniques can help smaller organizations improve their systems and customize their websites. There are many tools and applications available for web usage mining, ranging from free traffic analysis tools to integrated CRM solutions. Research initiatives in web usage mining and personalization focus on extracting useful patterns and rules using data mining techniques to understand user behavior. These initiatives combine methods such as user profiling, web usage mining, content management, and publishing mechanisms to provide more integrated views of website usage and more effective personalization.