Entertainment Personalization Mechanism through Cross-Domain User Modeling

Abstract. The growth of available entertainment information services, such as movies and CD listings, or travels and recreational activities, raises a need for personalization techniques for filtering and adapting contents to customer's interest and needs. Personalization technologies rely on users data, represented as User Models (UMs). UMs built by specific services are usually not transferable due to commercial competition and models ' representation heterogeneity. This paper focuses on the second obstacle and discusses architecture for mediating UMs across different domains of entertainment. The mediation facilitates improving the accuracy of the UMs and upgrading the provided personalization. 1

Interaction Design in a Mobile Food Recommender System

Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) One of the most important steps in building a recommender system is the interaction design process, which de nes how the recommender system interacts with a user. It also shapes the experience the user gets, from the point she registers and provides her preferences to the system, to the point she receives recommendations generated by the system. A proper interaction design may improve user experience and hence may result in higher usability of the system, as well as, in higher satisfaction. In this paper, we focus on the interaction design of a mo- bile food recommender system that, through a novel interac- tion process, elicits users' long-term and short-term prefer- ences for recipes. User's long-term preferences are captured by asking the user to rate and tag familiar recipes, while for collecting the short-term preferences, the user is asked to select the ingredients she would like to include in the recipe to be prepared. Based on the combined exploitation of both types of preferences, a set of personalized recommendations is generated. We conducted a user study measuring the us- ability of the proposed interaction. The results of the study show that the majority of users rates the quality of the rec- ommendations high and the system achieves usability scores above the standard benchmark

Data Obfuscation for Privacy-Enhanced Collaborative Filtering

Collaborative Filtering (CF) is an attractive and reliable recommendation technique. CF is typically implemented using a centralized storage of user profiles and this is a severe privacy danger, since an attack to this central repository can endanger the quality of the recommendations and result in a leak of personal data. This work investigates how a decentralized distributed storage of user profiles combined with data obfuscation techniques can mitigate the above dangers. In an experimental evaluation we initially show that relatively large parts of the profiles can be obfuscated with a minimal increase of Mean Average Error (MAE). This contradictory result motivates further experiments where we measured the increase in prediction error in two cases: a) when a more complex prediction task is considered, i.e., a data set containing more diverse (extreme) rating values; b) when only ratings with specific values are obfuscated. The results of these experiments clarify the roles of various rating values and will help to better implement an effective obfuscation policy

Ubiquitous User Modeling in Recommender Systems

Abstract. The existing personalization services usually base on proprietary and partial user models. This work attempts at evolving inference-based mediation mechanism that will facilitate integrating user models coming from different sources, such as repositories of other service providers and user's personal devices. This will allow obtaining more information about the users and providing more accurate personalization. The efficiency of the above approach will be demonstrated using the techniques from Recommender Systems domain. 1 Better Personalization with Ubiquitous User Modeling Nowadays, the quantity of the available information rapidly grows and exceeds our limited processing capabilities. This is regarded in the literature as the 'Information Overload ' problem [5]. As a result, there is a pressing need for intelligent systems that provide services according to user's personal needs and interests, and deliver tailored information in a way that will be most appropriate and valuable to the user. The stateof-the-art personalization techniques basically overcome the Information Overload by filtering the irrelevant information reaching the user

Decentralized Mediation of User Models for a Better Personalization

Abstract. The growth of available personalization services and the heterogeneity in content and representation of therein exploited User Models (UMs), raise a need for a mechanism allowing to aggregate partial UMs generated by other services. Such a mechanism will allow reuse of partial UMs in multiple personalization services that may need it. This paper discusses the details of a decentralized mediator for cross-domain and cross-technique translation and aggregation of partial UMs. The mediator facilitates enriching UMs managed by personalization services and improving the quality of the provided personalization.

On the Potential of Recommendation Technologies for Efficient Content Delivery Networks

During the last decade, we have witnessed a substantial change in content delivery networks (CDNs) and user access paradigms. If previously, users consumed content from a central server through their personal computers, nowadays they can reach a wide variety of repositories from virtually everywhere using mobile devices. This results in a considerable time-, location-, and event-based volatility of content popularity. In such a context, it is imperative for CDNs to put in place adaptive content management strategies, thus, improving the quality of services provided to users and decreasing the costs. In this paper, we introduce predictive content distribution strategies inspired by methods developed in the Recommender Systems area. Specifically, we outline different content placement strategies based on the observed user consumption patterns, and advocate their applicability in the state of the art CDNs