Pragmatic metadata matters: How data about the usage of data effects semantic user models

Online social media such as wikis, blogs or message boards enable large groups of users to generate and socialize around content. With increasing adoption of such media, the number of users interacting with user-generated content grows and as a result also the amount of pragmatic metadata i.e. data about the usage of content grows. The aim of this work is to compare different methods for learning topical user profiles from Social Web data and to explore if and how pragmatic metadata has an effect on the quality of semantic user models. Since accurate topical user profiles are required by many applications such as recommender systems or expert search engines, learning such models by observing content and activities around content is an appealing idea. To the best of our knowledge, this is the first work that demonstrates an effect between pragmatic metadata on one hand, and the quality of semantic user models based on user-generated content on the other. Our results suggest that not all types of pragmatic metadata are equally useful for acquiring accurate semantic user models, and some types of pragmatic metadata can even have detrimental effects.