Collecting user access patterns for building user profiles and collaborative filtering

The paper proposes a new learning mechanism to extract user preferences transparently for a World Wide Web recommender system. The general idea is that we use the entropy of the page being accessed to determine its interestingness based on its occurrence probability following a sequence of pages accessed by the user. The probability distribution of the pages is obtained by collecting the access patterns of users navigating on the Web. A finite context-model is used to represent the usage information. Based on our proposed model, we have developed an autonomous agent, named ProfBuilder, that works as an online recommender system for a Web site. ProfBuilder uses the usage information as a base for content-based and collaborative filtering.