Transparent user models for personalization

Personalization is a ubiquitous phenomenon in our daily online experience. While such technology is critical for helping us combat the overload of information we face, in many cases, we may not even realize that our results are being tailored to our personal tastes and preferences. Worse yet, when such a system makes a mistake, we have little recourse to correct it. In this work, we propose a framework for addressing this problem by developing a new user-interpretable feature set upon which to base personalized recommendations. These features, which we call badges, represent fundamental traits of users (e.g., “vegetarian” or “Apple fanboy”) inferred by modeling the interplay between a user’s behavior and selfreported identity. Specifically, we consider the microblogging site Twitter, where users provide short descriptions of themselves in their profiles, as well as perform actions such as tweeting and retweeting. Our approach is based on the insight that we can define bad...

• Type: Journal
• Year: 2012
• Where: KDD
• Authors: Khalid El-Arini, Ulrich Paquet, Ralf Herbrich, Jurgen Van Gael, Blaise Agüera y Arcas
• Keywords: Data Mining, Fundamental Traits, KDD 2012, Microsoft Research Cambridge, Ubiquitous Phenomenon