Predictive User Modeling with Actionable Attributes

Different machine learning techniques have been proposed and used for modeling individual and group user needs, interests and preferences. In the traditional predictive modeling instances are described by observable variables, called attributes. The goal is to learn a model for predicting the target variable for unseen instances. For example, for marketing purposes a company consider profiling a new user based on her observed web browsing behavior, referral keywords or other relevant information. In many real world applications the values of some attributes are not only observable, but can be actively decided by a decision maker. Furthermore, in some of such applications the decision maker is interested not only to generate accurate predictions, but to maximize the probability of the desired outcome. For example, a direct marketing manager can choose which type of a special offer to send to a client (actionable attribute), hoping that the right choice will result in a positive response with a higher probability. We study how to learn to choose the value of an actionable attribute in order to maximize the probability of a desired outcome in predictive modeling. We emphasize that not all instances are equally sensitive to changes in actions. Accurate choice of an action is critical for those instances, which are on the borderline (e.g. users who do not have a strong opinion one way or the other). We formulate three supervised learning approaches for learning to select the value of an actionable attribute at an instance level. We also introduce a focused training procedure which puts more emphasis on the situations where varying the action is the most likely to take the effect. The proof of concept experimental validation on two real-world case studies in web analytics and e-learning domains highlights the potential of the proposed approaches

Evaluating 'Prefer not to say' Around Sensitive Disclosures

As people's offline and online lives become increasingly entwined, the sensitivity of personal information disclosed online is increasing. Disclosures often occur through structured disclosure fields (e.g., drop-down lists). Prior research suggests these fields may limit privacy, with non-disclosing users being presumed to be hiding undesirable information. We investigated this around HIV status disclosure in online dating apps used by men who have sex with men. Our online study asked participants (N=183) to rate profiles where HIV status was either disclosed or undisclosed. We tested three designs for displaying undisclosed fields. Visibility of undisclosed fields had a significant effect on the way profiles were rated, and other profile information (e.g., ethnicity) could affect inferences that develop around undisclosed information. Our research highlights complexities around designing for non-disclosure and questions the voluntary nature of these fields. Further work is outlined to ensure disclosure control is appropriately implemented around online sensitive information disclosures