Learning User Preferences in Online Dating

Online dating presents a rich source of information for preference learning. Due to the desire to nd the right partner, users are willing to provide very speci c details about themselves and about the people they are looking for. The user can describe his/her ideal partner by specifying values for a set of prede ned attributes. This explicit preference model is quite rigid and may not re ect reality, as users' actions are often contrary to their stated preferences. In this respect learning implicit user preferences from the users' past contact history may be a more successful approach for building user preference models for use in recommender systems. In this study, we analyse the di erences between the implicit and explicit preferences and how they can complement each other to form the basis for a recommender system for online dating.