Conventional recommender systems provide personalized recommendations by collecting and retaining user data, relying on a centralized architecture. Hence, user privacy is undermined by the volume of information required to support the personalized experience. In this work, we propose a User Experience model witch allows the privacy preservation of a user by a decentralized architecture, enabling the Service Provider to offer recommendations without the need of storing individual user data. We advance the current state of the art by: i) Proposing a model of User Experience suitable for Persona-based recommendations; ii) Presenting a User Experience collection model able to enhance the user privacy towards the service provider while keeping the quality of her preferences predictions; and iii) Assessing the existence of the Persona profiles, which are needed for generating and addressing the recommendations. We perform several experiments using a real-world complete dataset from a medium-sized service provider, composed of more than 14,000 unique users and 33,000 content titles collected over a period of two years. We show that our architecture, in com- bination with our User Experience model, achieves the same results or better, in terms of rating prediction accuracy, as the state of the art systems, without sacrificing user’s privacy.
|Number of pages||9|
|Publication status||Published - 2018|
|Event||SIR: Workshop on Social Interaction-based Recommendation: The 27th International Conference on Information and Knowledge Management (CIKM 2018) - Turin, Italy|
Duration: 22 Oct 2018 → 22 Oct 2018
|Workshop||SIR: Workshop on Social Interaction-based Recommendation|
|Period||22/10/2018 → 22/10/2018|
Servizi, V., Kosta, S., Hammershøj, A. D., & Olesen, H. (2018). A User Experience Model for Privacy and Context Aware Over-the-Top (OTT) TV Recommendations. Paper presented at SIR: Workshop on Social Interaction-based Recommendation, Turin, Italy.