Combining Qualitative Author preference and Quantitative Viewer Profile for Content Customization
Original Publication Date: 2002-Aug-01
Included in the Prior Art Database: 2003-Jun-21
Problem Description: A general problem for an entity (called a “web site”) offering content on the internet is how to customize content efficiently for the site’s viewer (user) and at the same time incorporate the inputs from both parties. Existing methods for the problem include quantitative methods that build numeric models of a user’s interest and qualitative methods that encode non-numeric preferences over the content of the site. Some issues with existing methods are poor performance when data is scarce (as will happen when a site starts service), limited capability to include domain-knowledge and author (merchant in B2C) intent, and lack of scalability with site size and number of users. This invention proposes a technique for content customization by combining qualitative author preference among classes of presentation components (example, coupons, ads, news items, etc.) on a site over quantitative viewer profiles built on components within a class. Prior Art: There is much literature on quantitative methods for content customization which employ user data to learn user characteristics and build models on them. Specifically, the server acquires frequency (and probability) distribution information from the viewership data (training data) and builds a prediction model using machine learning tools like association rule mining, classification, collaborative filtering, clustering or their combination. The model is validated with test (viewership) data, and used for online prediction and corresponding content customization.