Personalized online systems for Web search, news recommendation, and e-commerce are developed. The process of personalization of online systems consists of three main steps: determining a user's needs, classifying products or services, and matching the user's needs with suitable products or services. A multi-feature based method to automatically classify Web pages into categories of topics hierarchically representing the Web pages is proposed. An approach to modeling and quantifying a user's interests and preferences using the user's Web navigational data is presented. The approach is based on the premise that frequently visiting certain types of content or Web sites indicates that the user is interested in related content or retrieving information from those sites. A personalized search system utilizing a Web user's interest, preference and search context models is developed. A Web user's interest and preference models are constructed and updated by analyzing the user's navigational data and automatically classifying Web pages. A user's search context model is used to determine how the user's interest and preference models impact on his or her search behavior. An algorithm to re-rank search results generated by a conventional search engine is designed to provide a personalized Web search service. A hybrid recommender system of personalized recommendation of news on the Web is developed. Based on the similarities between Web pages and users' models of interest and preference, the Web pages are recommended to the users who are likely interested in the related topics. Moreover, the technique of collaborative filtering is employed, which aims to choose the trusted users and incorporate machine intelligence combined with human efforts. Once trusted users are determined, their behavior on the Web is considered as the manual recommendation part of the system. A method of classifying Web customers for planning customized e-marketing is proposed. The proposed e-marketing approach can be divided into four steps: determining a customer's general interest model, ascertaining a customer's local browsing model, classifying Web customers, and designing a personalized marketing and promotion plan for e-commerce based on the customer classification. Various experiments are carried out to demonstrate the effectiveness of the proposed approaches and systems.