A personalized and context-aware news offer for mobile devices

For classical domains, such as movies, recommender systems have proven their usefulness. But recommending news is more challenging due to the short life span of news content and the demand for up-to-date recommendations. This paper presents a news recommendation service with a content-based algorithm that uses features of a search engine for content processing and indexing, and a collaborative filtering algorithm for serendipity. The extension towards a context-aware algorithm is made to assess the information value of context in a mobile environment through a user study. Analyzing interaction behavior and feedback of users on three recommendation approaches shows that interaction with the content is crucial input for user modeling. Context-aware recommendations using time and device type as context data outperform traditional recommendations with an accuracy gain dependent on the contextual situation. These findings demonstrate that the user experience of news services can be improved by a personalized context-aware news offer

A task-driven design model for collaborative AmI systems

Proceedings of the CAISE*06 Workshop on Ubiquitous Mobile Information and Collaboration Systems UMICS '06. Luxemburg, June 5-9, 2006.The proceedings of this workshop also appeared in printed version In T. Latour and M. Petit (eds), Proceedings of Workshops and Doctoral Consortium, The 18th International Conference on Advanced Information Systems Engineering - Trusted Information Systems (CAiSE'06), June 5-9, 2006, Presses Universitaires de Namur, 2006, ISBN 2-87037-525.Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073)Ambient intelligence (AmI) is a promising paradigm for humancentred interaction based on mobile and context-aware computing, natural interfaces and collaborative work. AMENITIES (a conceptual and methodological framework based on task-based models) has been specially devised for collaborative systems and is the starting point for a new design proposal for application to AmI systems. This paper proposes a task-based model for designing collaborative AmI systems, which attempts to gather the computational representation of the concepts involved (tasks, laws, etc.) and the relationships between them in order to develop a complete functional environment in relation with the features of AmI systems (collaborative, context-aware, dynamic, proactive, etc.). The research has been applied to an e-learning environment and is implemented using a blackboard model.This research is partially supported by a Spanish R&D Project TIN2004-03140, Ubiquitous Collaborative Adaptive Training (U-CAT)

Designing Contextualized Learning

Specht, M. (2008). Designing Contextualized Learning. In H. H. Adelsberger, Kinshuk, J. M. Pawlowski & D. Sampson (Eds.), Handbook on Information Technologies for Education and Training (2th ed., pp. 101-111). Springer, Berlin Heidelberg 2008: International Handbook on Information Systems Series.Contextualized and ubiquitous learning are relatively new research areas that combine the latest developments in ubiquitous and context aware computing with pedagogical approaches relevant to structure more situated and context aware learning support. Searching for different backgrounds of mobile and contextualized learning authors have identified the relations between existing educational paradigms and new classes of mobile appli- cations for education (Naismith, Lonsdale, Vavoula, & Sharples, 2004). Furthermore best practices of mobile learning applications have been iden- tified and discussed in focused workshops (Stone, Alsop, Briggs, & Tomp- sett, 2002; Tatar, Roschelle, Vahey, & Peunel, 2002). Especially in the area of educational field trips (Equator Project, 2003; RAFT, 2003) in the last years innovative approaches for intuitive usage of contextualized mo- bile interfaces have been developed. The following paper describes the motivation and background for con- textualizing learning and illustrates the implementation of a service based and flexible learning toolkit developed in the RAFT project for supporting contextualized collaborative learning support

Privacy-preserving recommendations in context-aware mobile environments

© Emerald Publishing Limited. Purpose - This paper aims to address privacy concerns that arise from the use of mobile recommender systems when processing contextual information relating to the user. Mobile recommender systems aim to solve the information overload problem by recommending products or services to users of Web services on mobile devices, such as smartphones or tablets, at any given point in time and in any possible location. They use recommendation methods, such as collaborative filtering or content-based filtering and use aconsiderable amount of contextual information to provide relevant recommendations. However, because of privacy concerns, users are not willing to provide the required personal information that would allow their views to be recorded and make these systems usable. Design/methodology/approach - This work is focused on user privacy by providing a method for context privacy-preservation and privacy protection at user interface level. Thus, a set of algorithms that are part of the method has been designed with privacy protectionin mind, which isdone byusing realistic dummy parameter creation. Todemonstrate the applicability of the method, arelevant context-aware data set has been used to run performance and usability tests. Findings - The proposed method has been experimentally evaluated using performance and usability evaluation tests and is shown that with a small decrease in terms of performance, user privacy can be protected. Originality/value - This is a novel research paper that proposed a method for protecting the privacy of mobile recommender systems users when context parameters are used

Travel recommendations in a mobile tourist information system

An advanced mobile tourist information system delivers information about sights and events on a tourists travel route. The system should be personalized in its interaction with the tourist. Data that can be used for personalization are: the tourists interest profile, an analysis of their travel history, and the tourists feedback about sights. Existing mobile information systems for tourists do not tailor their information delivery to the tourists interests. In this paper, we propose the use of personalised recommendations that consider all of the personal information a tourist provides. We adopt and modify techniques from recommended systems to the new application area of mobile tourist information. We propose a number of methods for personalised recommendations; and select a subset of these for implementation. This paper then presents the implemented recommended component of our TIP system for mobile tourist informatio

Advanced recommendations in a mobile tourist information system

An advanced tourist information provider system delivers information regarding sights and events on their users' travel route. In order to give sophisticated personalized information about tourist attractions to their users, the system is required to consider base data which are user preferences defined in their user profiles, user context, sights context, user travel history as well as their feedback given to the sighs they have visited. In addition to sights information, recommendation on sights to the user could also be provided. This project concentrates on combinations of knowledge on recommendation systems and base information given by the users to build a recommendation component in the Tourist Information Provider or TIP system. To accomplish our goal, we not only examine several tourist information systems but also conduct the investigation on recommendation systems. We propose a number of approaches for advanced recommendation models in a tourist information system and select a subset of these for implementation to prove the concept

AndroMedia : Towards a Context-aware Mobile Music Recommender

Portable music players have made it possible to listen to a personal collection of music in almost every situation, and they are often used during some activity to provide a stimulating audio environment. Studies have demonstrated the effects of music on the human body and mind, indicating that selecting music according to situation can, besides making the situation more enjoyable, also make humans perform better. For example, music can boost performance during physical exercises, alleviate stress and positively affect learning. We believe that people intuitively select different types of music for different situations. Based on this hypothesis, we propose a portable music player, AndroMedia, designed to provide personalised music recommendations using the user's current context and listening habits together with other user's situational listening patterns. We have developed a prototype that consists of a central server and a PDA client. The client uses Bluetooth sensors to acquire context information and logs user interaction to infer implicit user feedback. The user interface also allows the user to give explicit feedback. Large user interface elements facilitate touch-based usage in busy environments. The prototype provides the necessary framework for using the collected information together with other user's listening history in a context- enhanced collaborative filtering algorithm to generate context-sensitive recommendations. The current implementation is limited to using traditional collaborative filtering algorithms. We outline the techniques required to create context-aware recommendations and present a survey on mobile context-aware music recommenders found in literature. As opposed to the explored systems, AndroMedia utilises other users' listening habits when suggesting tunes, and does not require any laborious set up processes

Factors Influencing the Quality of the User Experience in Ubiquitous Recommender Systems

The use of mobile devices and the rapid growth of the internet and networking infrastructure has brought the necessity of using Ubiquitous recommender systems. However in mobile devices there are different factors that need to be considered in order to get more useful recommendations and increase the quality of the user experience. This paper gives an overview of the factors related to the quality and proposes a new hybrid recommendation model.Comment: The final publication is available at www.springerlink.com Distributed, Ambient, and Pervasive Interactions Lecture Notes in Computer Science Volume 8530, 2014, pp 369-37