What is Rule-Based Personalization

Hundreds of thousands of companies worldwide are using the Web as a major channel to interact with their customers for brand promotion, product marketing, order fulfillment, and after-sales support. Competition is extremely keen among online merchants.1 In doing business online, the question that lurks in the back of their mind is, are we maximizing our business opportunities? With the high interactivity of e-commerce, online merchants now adopt various differentiating strategies to attract and retain customers in the hope of remaining competitive. To provide a differentiated service, online merchants first identify each individual, and then acquire more information about each individual’s interests. Then, they can tailor Web content directly to a specific user by having the user provide information to the Web site either directly or through tracking devices on the site. The software can then modify the content to the needs of the user. Ultimately, highly focused and relevant products or services are delivered to each customer, who is treated in a unique way to fit marketing and advertising with his or her needs. This process is generally named personalization. There is a wide range of personalization strategies used nowadays. For instance, My Yahoo! provides a personalized “space” for each user. It automatically generates personalized content (e.g., information on the horoscope for the correct star sign) matched with users’ profiles (e.g., a person’s date of birth). Apart from automatic personalization, it also presents the users with an array of choices and allows the users to select what is of interest to them. The users can personalize not only the content (e.g., weather, finance) but also the layout (e.g., color, background). My Yahoo! was considered to be one of the forerunners among the growing number of personalized Web sites that have been springing up on the Internet over the last few years (Manber, Patel, & Robison, 2000). Amazon.com greets returning customers with a personalized message and offers a hyperlink to book recommendations congruent with their past purchases. These recommendations are generated based on the customers’ previous purchases and the preferences of like-minded people, and there is no extra work imposed on the customers. Amazon continues to establish its personalization system, and more filtering mechanisms are being added to make the book recommendations be more relevant and useful. Recently, there has been the introduction of a personalized search engine, A9.com by Amazon.com, which recommends relevant Web sites to each individual by analyzing his or her browsing history and bookmarks. Expedia.com asks users for their desired destinations and then e-mails them information about special discounts to the place where they like to travel. It is expected that corporate investment in personalization technologies will continue to surge in the future (Awad & Krishnan, 2006; Poulin, Montreuil, & Martel, 2006; Rust & Lemon, 2001). Given the proliferation of personalization, this chapter will address the key issues related to personalization and provide definitions to some keywords, such as rule-based personalization and collaborative filtering.