Overview and analysis of personal and social tagging context to construct user models

Abstract

The quality and user acceptance of personalized services such as personalized information retrieval and navigation or content recommendation depends depends besides the personalization mechanism on the quality, validity and accuracy of the employed user model. In literature a variety of user model construction methods based on tagging activity in social tagging systems (STS) are presented, relying on different user contexts, e.g., the personal or social context. But up to now there is neither a concise overview of existing construction methods available nor a deeper analysis and discussion of the differences between these models. Such an analysis would for example ease evaluation but also enable system designers to choose the most appropriate one. Our work tackles this problem by providing a short overview of state-of-the art user model construction methods which employ social tags. This is followed by a statistical comparison of four different user model construction methods for STS based on tag-frequency. This analysis unveils that depending on the method chosen (based user's personal tagging behavior as well as community-based social strategies), the user model consists of different tags and tag frequency rankings, thus services employing different models will lead to different results. Copyright 2012 ACM.