Content Recommendation System Based on Private Dynamic User Profile - Ting Chen; Wei-Li Han; Hai-Dong Wang; Yi-Xun Zhou; Bin Xu; Bin-Yu Zang (2007 International Conference on Machine Learning and Cybernetics, 2007)
Internet is overwhelming, personalized content recommendation system offers spam filtering service and suggests useful information to the end users. It is a hotspot in the research area of content management on WWW. Traditional recommendation systems do the data mining on web access logs, discover user's access patterns, and filter the information on behalf of the user at the server side. One critical limitation of traditional recommendation system is the lack of user's private daily data, such as schedules, favorite websites and personal emails. The reason for this limitation is the privacy leak issue when the server holds much more private user data. To solve this problem, this paper presents an agent-based personalized recommendation method called Content REcommendation System based on private Dynamic User Profile (CRESDUP). The system collects and mines the private data of user at the client side, discovers, stores and updates private Dynamic User Profile (DUP) at the client side. The system fetches preferred message from the content server according to DUP. An important usage of this technology is a personalized advertising system in the RSS (Rich Site Summary, or RDF Site Summary) reader application. Our experiment shows that the system can utilize DUP to identify the customers' potential preferences and deliver the more preferred messages, especially the advertisements, to people who are interested.