Attention metadata: collection and management
Jehad Najjar, Erik Duval, Martin Wolpers · Lirias · 2006
This paper discusses how attention metadata enables collection of rich usage data. We use this attention to enhance users' models, predict usage patterns and feed personalization and recommender systems. We argue that attention metadata extends the information on user attention which can be derived from current log services. Furthermore, frameworks and schemas that enable tracking and merging rich and detailed user attention metadata in different applications are necessary to obtain a more complete picture on how the user handles digital content. We illustrate our approach by tracking user attention in learning systems as an example. For our application domain we want to collect large volumes of attention metadata, for statistical analysis that enables us to identify relevant learning paths.