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 which allows the privacy of a user to be preserved by means of 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 (UEx) suitable for Persona-based recommendations; ii) Presenting a UEx collection model which enhances 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 combination with our UEx model, achieves the same or better results, compared to state-of-the-art systems, in terms of rating prediction accuracy, without sacrificing user’s privacy.
|Journal||CEUR Workshop Proceedings|
|Number of pages||8|
|Publication status||Published - 2019|
|Event||27th ACM International Conference on Information and Knowledge Management (CIKM 2018) - Torino, Italy|
Duration: 22 Oct 2018 → 22 Oct 2018
|Conference||27th ACM International Conference on Information and Knowledge Management (CIKM 2018)|
|Period||22/10/2018 → 22/10/2018|