Abstract
The incredible appeals of smartphones and the unprecedented progress in the development of mobile and wireless networks in recent years have enabled ubiquitous availability of myriad media contents. Consequently, it has become problematic for mobile users to find relevant media items. However, context awareness has been proposed as a means to help mobile users find relevant media items anywhere and at any time. The contribution of this paper is the presentation of a context-aware media recommendation framework for smart devices (CAMR). CAMR supports the integration of context sensing, recognition, and inference, using classification algorithms, an ontology-based context model and user preferences to provide contextually relevant media items to smart device users. This paper describes CAMR and its components, and demonstrates its use to develop a context-aware mobile movie recommendation on Android smart devices. Experimental evaluations of the framework, via an experimental context-aware mobile recommendation application, confirm that the framework is effective, and that its power consumption is within acceptable range. © 2014 Springer International Publishing Switzerland.

Abstract
The emergence of pervasive computing, the rapid advancements in broadband and mobile networks and the incredible appeals of smart devices are driving unprecedented universal access and delivery of online-based media resources. As more and more media services continue to flood the Web, mobile users will continue to waste invaluable time, seeking content of their interest. To deliver relevant media items offering richer experiences to mobile users, media services must be equipped with contextual knowledge of the consumption environment as well as contextual preferences of the users. This article investigates context-aware recommendation techniques for implicit delivery of contextually relevant online media items. The proposed recommendation services work with a contextual user profile and a context recognition framework, using case base reasoning as a methodology to determine user's current contextual preferences, relying on a context recognition service, which identifies user's dynamic contextual situation from device's built-in sensors. To evaluate the proposed solution, we developed a case-study context-aware application that provides personalized recommendations adapted to user's current context, namely the activity he/she performs and consumption environment constraints. Experimental evaluations, via the case study application, real-world user data, and online-based movie metadata, demonstrate that context-aware recommendation techniques can provide better efficacy than the traditional approaches. Additionally, evaluations of the underlying context recognition process show that its power consumption is within an acceptable range. The recommendations provided by the case study application were assessed as effective via a user study, which demonstrates that users are pleased with the contextual media recommendations.

Abstract

Abstract
With the overwhelming volume of online multimedia content and increasing ubiquity of Internet-enabled mobile devices, pervasive use of the Web for content sharing and consumption has become our everyday routines. Consequently, people seeking online access to content of interest are becoming more and more frustrated. Thus, deciding which content to consume among the deluge of available alternatives becomes increasingly difficult. Context-aware personalization, having the capability to predict user's contextual preferences, has been proposed as an effective solution. However, some existing personalized systems, especially those based on collaborative filtering, rely on rating information explicitly obtained from users in consumption contexts. Therefore, these systems suffer from the so-called cold-start problem that occurs as a result of personalization systems' lack of adequate knowledge of either a new user's preferences or of a new item rating information. This happens because these new items and users have not received or provided adequate rating information respectively. In this paper, we present an analysis and design of a context-aware personalized system capable of minimizing new user cold-start problem in a mobile multimedia consumption scenario. The article emphasizes the importance of similarity between contexts of consumption based on the traditional k-nearest neighbor algorithm using Pearson Correlation model. Experimental validation, with respect to quality of personalized recommendations and user satisfaction in both contextual and non-contextual scenarios, shows that the proposed system can mitigate the effect of user-based cold-start problem.

Abstract
Multimedia adaptation depends on several factors, such as the content itself, the consumption device and its characteristics, the transport and access networks and the user. An adaptation decision engine, in order to provide the best possible Quality of Experience to a user, needs to have information about all variables that may influence its decision. For the aforementioned factors, we implement content classification, define device classes, consider limited bandwidth scenarios and categorize user preferences based on a subjective quality evaluation test. The results of these actions generate vital information to pass to the adaptation decision engine so that its operation may provide the indication of the most suitable adaptation to perform that delivers the best possible outcome for the user under the existing constraints.

Abstract
Technological advances are pushing into the mass market innovative wearable devices featuring increasing processing and sensing capacity, non-intrusiveness and ubiquitous use. Sensors built-in those devices, enable acquiring different types of data and by taking advantage of the available processing power, it is possible to run intelligent applications that process the sensed data to offer added -value to the user in multiple domains. Although not new to the modern society, it is unquestionable that the present exercise boom is rapidly spreading across all age groups. However, in a great majority of cases, people perform their physical activity on their own, either due to time or budget constraints and may easily get discouraged if they do not see results or perform exercises inadequately. This paper presents an application, running on a wearable device, aiming at operating as a personal trainer that validates a set of proposed exercises in a sports' session. The developed solution uses inertial sensors of an Android Wear smartwatch and, based on a set of pattern recognition algorithms, detects the rate of success in the execution of a planned workout. The fact that all processing can be executed on the device is a differentiator factor to other existing solutions.