Personalized recommendation driven by information flow

We propose that the information access behavior of a group of people can be modeled as an information flow issue, in which people intentionally or unintentionally influence and inspire each other, thus creating an interest in retrieving or getting a specific kind of information or product. Information flow models how information is propagated in a social network. It can be a real social network where interactions between people reside; it can be, moreover, a virtual social network in that people only influence each other unintentionally, for instance, through collaborative filtering. We leverage users’ access patterns to model information flow and generate effective personalized recommendations. First, an early adoption based information flow (EABIF) network describes the influential relationships between people. Second, based on the fact that adoption is typically category specific, we propose a topic-sensitive EABIF (TEABIF) network, in which access patterns are clustered with res...

• Type: Conference
• Year: 2006
• Where: SIGIR
• Authors: Xiaodan Song, Belle L. Tseng, Ching-Yung Lin, Ming-Ting Sun
• Keywords: Access Patterns, Information Flow, SIGIR 2006, Social Network