Unified Relevance Feedback for Multi-Application User Interest Modeling

Abstract

A user often interacts with multiple applications while working on a task. User models can be developed individually at each of the individual applications, but there is no easy way to come up with a more complete user model based on the distributed activity of the user. To address this issue, this research studies the importance of combining various implicit and explicit relevance feedback indicators in a multi-application environment. It allows different applications used for different purposes by the user to contribute user activity and its context to mutually support users with unified relevance feedback. Using the data collected by the web browser, Microsoft Word and Microsoft PowerPoint, combinations of implicit relevance feedback with semi-explicit relevance feedback were analyzed and compared with explicit user ratings. Our results are two-fold: first we demonstrate the aggregation of implicit and semi-explicit user interest data across multiple everyday applications using our Interest Profile Manager (IPM) framework. Second, our experimental results show that incorporating implicit feedback with semi-explicit feedback for page-level user interest estimation resulted in a significant improvement over the content-based models.