Biblio

Personalization, recommendations, and user modeling can be powerful tools to improve people's experiences with technology and to help them find information. However, we also know that people underestimate how much of their personal information is used by our technology and they generally do not understand how much algorithms can discover about them. Both privacy and ethical technology have issues of consent at their heart. While many personalization systems assume most users would consent to the way they employ personal data, research shows this is not necessarily the case. This talk will look at how to consider issues of privacy and consent when users cannot explicitly state their preferences, The Creepy Factor, and how to balance users' concerns with the benefits personalized technology can offer.

Personalization, recommendations, and user modeling can be pow- erful tools to improve people?s experiences with technology and to help them nd information. However, we also know that people underestimate how much of their personal information is used by our technology and they generally do not understand how much algorithms can discover about them. Both privacy and ethical tech- nology have issues of consent at their heart. This talk will look at how to consider issues of privacy and consent when users cannot explicitly state their preferences, The Creepy Factor, and how to balance users? concerns with the bene ts personalized technology can o er.

Social recommendation takes advantage of the influence of social relationships in decision making and the ready availability of social data through social networking systems. Trust relationships in particular can be exploited in such systems for rating prediction and recommendation, which has been shown to have the potential for improving the quality of the recommender and alleviating the issue of data sparsity, cold start, and adversarial attacks. An appropriate trust inference mechanism is necessary in extending the knowledge base of trust opinions and tackling the issue of limited trust information due to connection sparsity of social networks. In this work, we offer a new solution to trust inference in social networks to provide a better knowledge base for trust-aware recommender systems. We propose using a semiring framework as a nonlinear way to combine trust evidences for inferring trust, where trust relationship is model as 2-D vector containing both trust and certainty information. The trust propagation and aggregation rules, as the building blocks of our trust inference scheme, are based upon the properties of trust relationships. In our approach, both trust and distrust (i.e., positive and negative trust) are considered, and opinion conflict resolution is supported. We evaluate the proposed approach on real-world datasets, and show that our trust inference framework has high accuracy, and is capable of handling trust relationship in large networks. The inferred trust relationships can enlarge the knowledge base for trust information and improve the quality of trust-aware recommendation.