Personalization for unobtrusive service interaction

Increasingly, mobile devices play a key role in the communication between users and the services embedded in their environment. With ever greater number of services added to our surroundings, there is a need to personalize services according to the user needs and environmental context avoiding service behavior from becoming overwhelming. In order to prevent this information overload, we present a method for the development of mobile services that can be personalized in terms of obtrusiveness (the degree in which each service intrudes the user's mind) according to the user needs and preferences. That is, services can be developed to provide their functionality at different obtrusiveness levels depending on the user by minimizing the duplication of efforts. On the one hand, we provide mechanisms for describing the obtrusiveness degree required for a service. On the other hand, we make use of Feature Modeling techniques in order to define the obtrusiveness level adaptation in a declarative manner. An experiment was conducted in order to put in practice the proposal and evaluate the user acceptance for the personalization capabilities provided by our approach. © Springer-Verlag London Limited 2011.This work has been developed with the support of MICINN under the project EVERYWARE TIN2010-18011 and co-financed with ERDF, in the grants program FPU.Gil Pascual, M.; Giner Blasco, P.; Pelechano Ferragud, V. (2012). Personalization for unobtrusive service interaction. Personal and Ubiquitous Computing. 16(5):543-561. https://doi.org/10.1007/s00779-011-0414-0S543561165Abrams M, Phanouriou C, Batongbacal AL, Williams SM, Shuster JE (1999) Uiml: an appliance-independent xml user interface language. In: WWW ’99. Elsevier, North-Holland, pp 1695–1708Ballagas R, Borchers J, Rohs M, Sheridan JG (2006) The smart phone: a ubiquitous input device. 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A Personalized System for Conversational Recommendations

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system

A Framework for Exploiting Internet of Things for Context-Aware Trust-based Personalized Services

In the last years, we have witnessed the introduction of Internet of Things as an integral part of the Internet with billions of interconnected and addressable everyday objects. On the one hand, these objects generate massive volume of data that can be exploited to gain useful insights into our day-to-day needs. On the other hand, context-aware recommender systems (CARSs) are intelligent systems that assist users to make service consumption choices that satisfy their preferences based on their contextual situations. However, one of the major challenges in developing CARSs is the lack of functionality providing dynamic and reliable context information required by the recommendation decision process based on the objects that users interact with in their environments. Thus, contextual information obtained from IoT objects and other sources can be exploited to build CARSs that satisfy users’ preferences, improve quality of experience and recommendation accuracy. This article describes various components of a conceptual IoT based framework for context-aware personalized recommendations. The framework addresses the weakness whereby CARSs rely on static and limited contextual information from user’s mobile phone, by providing additional components for reliable and dynamic contextual information, using IoT context sources. The core of the framework consists of context recognition and reasoning management, dynamic user profile model incorporating trust to improve accuracy of context-aware personalized recommendations. Experimental evaluations show that incorporating context and trust in personalized recommendations can improve its accuracy

A literature review of personalized learning and the Cloud

In order to provide effective application of the Cloud in education it is essential to know how the learning should and could – if needed – be adapted. In this respect the concept of ‘personalising learning’ is frequently used. But what exactly is personalising learning. And how can it be implemented in using the cloud? The aim of WG3 i-Learner of the School on the Cloud network is to investigate this from the point of view of the learner, whereas WG2 i-Teacher looks on the role of the educators, and WG4 i-Future on the technology. The document has two parts: - The first part starts with an evaluation and synthesis of the definitions of personalized learning (Ch. 3), followed by an analysis of how this is implemented in learning style (e-learning vs. i-learning, m-learning and u-learning, Ch. 4) and learning approach (Ch. 5). To implement this an appropriate pedagogy (Ch. 6) is needed. - The second part is an attempt on how to implement this onto the learner of the future (Ch. 7), as well to the learning process and to the learning place. Recommendations are made in Ch. 8

A personalized system for conversational recommendations

technical reportIncreased computing power and theWeb have made information widely accessible. In turn, this has encouraged the development of recommendation systems that help users find items of interest, such as books or restaurants. Such systems are more useful when they personalize themselves to each user?s preferences, thus making the recommendation process more efficient and effective. In this paper, we present a new type of recommendation system that carries out a personalized dialogue with the user. This system ? the Adaptive Place Advisor ? treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. The system incorporates a user model that contains item, attribute, and value preferences, which it updates during each conversation and maintains across sessions. The Place Advisor uses both the conversational context and the user model to retrieve candidate items from a case base. The system then continues to ask questions, using personalized heuristics to select which attribute to ask about next, presenting complete items to the user only when a few remain. We report experimental results demonstrating the effectiveness of user modeling in reducing the time and number of interactions required to find a satisfactory item

Deliverable D4.1 Specification of user profiling and contextualisation

This deliverable presents a comprehensive research of past work in the field of capturing and interpreting user preferences and context and an overview of relevant digital media-specific techniques, aiming to provide insights and ideas for innovative context-aware user preference learning and to justify the user modelling strategies considered within LinkedTV’s WP4. Based on this research and a study over the specific technical and conceptual requirements of LinkedTV, a prototypical design for profiling and contextualizing user needs in a linked media environment is specified

Layered evaluation of interactive adaptive systems : framework and formative methods

Peer reviewedPostprin