Exploring Feedback Models in Interactive Tagging

One of the cornerstones of the Social Web is informal user-generated metadata (or tags) for annotating web objects like pages, images, and videos. However, many real-world domains are currently left out of the social tagging phenomenon due to the lack of a wide-scale tagging-savvy audience - domains like the personal desktop, enterprise intranets, and digital libraries. Hence in this paper, we propose a lightweight interactive tagging framework for providing high-quality tag suggestions for the vast majority of untagged content. One of the salient features of the proposed framework is its incorporation of user feedback for iteratively refining tag suggestions. Concretely, we describe and evaluate three feedback models - Tag-Based, Term-Based, and Tag Co-location. Through extensive user evaluation and testing, we find that feedback can significantly improve tag quality with minimal user involvement.