Personalization and customization are considered increasingly important elements of Web applications. The terms usually refer to using information about a user (be it a customer, a Web site visitor, an individual, or a group) to better design products and services tailored to that user. One way to define personalization is by describing how it is implemented and used.
Factual and Behavioral Profiles
One of the problems with many rule discovery methods is the large number of rules generated, many of which, although statistically acceptable, are spurious, irrelevant, or trivial. Post-analysis is usually used to filter out irrelevant and spurious rules. Several data mining systems perform rule validation by letting a domain expert inspect the rules on a one-by-one basis and reject unacceptable rules. Such an approach is not scalable to large numbers of rules and customer profiles. To solve the problem, Adomavicius and Tuzhilin (2001) proposed collective rule validation. Rules are collected in a single set to which several rule validation operators are applied iteratively.
Explicit Versus Implicit Profiling
Data for user profiling can be collected implicitly or explicitly. Explicit collection usually requires the user’s active participation, thereby allowing the user to control the information in his profile. Explicit profiling can take different forms. The user may fill out a form, take part in a survey, fill out a questionnaire, submit personal information at the time of registration, provide a ranking or rating of products, etc. This method has the advantage of letting the customers tell a Web site directly what they need and how they need it.
Overview of filtering technologies
Although necessary, user profile management (creating, updating, and maintaining user profiles) is not sufficient for providing personalized services. Information in user profiles has to be analyzed in order to infer users’ needs and preferences. In this section we will briefly explain the most popular personalization techniques: rule-based filtering, collaborative filtering, and content-based filtering. All these techniques are used to predict customers’ interests and make recommendations.
Web usage analysis for personalization
Some problems of collaborative and content-based filtering can be solved by Web usage analysis. Web usage analysis studies how Web sites are used by visitors in general and by each user in particular. Web usage analysis includes statistics such as page access frequency, common traversal paths through a Web site, session length, and top exit pages. Usage information can be stored in user profiles for improving the interaction with visitors. Web usage analysis is usually performed using various data mining techniques such as association rule generation and clustering
A user session consists of all activities performed by a user during a single visit to a Web site. Because a user may visit a Web site more than once, a server log may contain multiple sessions for a given user. Automatic session identification can be performed by partitioning log entries belonging to a single user into sequences of entries corresponding to different visits of the same user. Brandt, MO basher, Spiliopoulou, and Wiltshire (2001) distinguish between time-oriented and navigation-oriented session zing. Time-oriented session zing is based on timeout. If the duration of a session or the time spent on a particular Web page exceeds some predefined threshold, it is assumed that the user has started a new session.
Personalization and customization are among the fastest growing segments of the Internet economy. They provide several advantages to both businesses and customers. Customers benefit from personalization by receiving customized experience, reduced information overload, and personalized products and services. Businesses benefit from the ability to learn consumers’ behavior, provide one-to-one marketing, increase customer retention, optimize product selection, and provide build-on-demand services.