Context-aware Sequential Recommendation

Since sequential information plays an important role in modeling user behaviors, various sequential recommendation methods have been proposed. Methods based on Markov assumption are widely-used, but independently combine several most recent components. Recently, Recurrent Neural Networks (RNN) based methods have been successfully applied in several sequential modeling tasks. However, for real-world applications, these methods have difficulty in modeling the contextual information, which has been proved to be very important for behavior modeling. In this paper, we propose a novel model, named Context-Aware Recurrent Neural Networks (CA-RNN). Instead of using the constant input matrix and transition matrix in conventional RNN models, CA-RNN employs adaptive context-specific input matrices and adaptive context-specific transition matrices. The adaptive context-specific input matrices capture external situations where user behaviors happen, such as time, location, weather and so on. And the adaptive context-specific transition matrices capture how lengths of time intervals between adjacent behaviors in historical sequences affect the transition of global sequential features. Experimental results show that the proposed CA-RNN model yields significant improvements over state-of-the-art sequential recommendation methods and context-aware recommendation methods on two public datasets, i.e., the Taobao dataset and the Movielens-1M dataset.Comment: IEEE International Conference on Data Mining (ICDM) 2016, to apea

Adaptive sampling in context-aware systems: a machine learning approach

As computing systems become ever more pervasive, there is an increasing need for them to understand and adapt to the state of the environment around them: that is, their context. This understanding comes with considerable reliance on a range of sensors. However, portable devices are also very constrained in terms of power, and hence the amount of sensing must be minimised. In this paper, we present a machine learning architecture for context awareness which is designed to balance the sampling rates (and hence energy consumption) of individual sensors with the significance of the input from that sensor. This significance is based on predictions of the likely next context. The architecture is implemented using a selected range of user contexts from a collected data set. Simulation results show reliable context identification results. The proposed architecture is shown to significantly reduce the energy requirements of the sensors with minimal loss of accuracy in context identification

System Support for Managing Invalid Bindings

Context-aware adaptation is a central aspect of pervasive computing applications, enabling them to adapt and perform tasks based on contextual information. One of the aspects of context-aware adaptation is reconfiguration in which bindings are created between application component and remote services in order to realize new behaviour in response to contextual information. Various research efforts provide reconfiguration support and allow the development of adaptive context-aware applications from high-level specifications, but don't consider failure conditions that might arise during execution of such applications, making bindings between application and remote services invalid. To this end, we propose and implement our design approach to reconfiguration to manage invalid bindings. The development and modification of adaptive context-aware applications is a complex task, and an issue of an invalidity of bindings further complicates development efforts. To reduce the development efforts, our approach provides an application-transparent solution where the issue of the invalidity of bindings is handled by our system, Policy-Based Contextual Reconfiguration and Adaptation (PCRA), not by an application developer. In this paper, we present and describe our approach to managing invalid bindings and compare it with other approaches to this problem. We also provide performance evaluation of our approach

A context-aware adaptive feedback agent for activity monitoring and coaching

A focus in treatment of chronic diseases is optimizing levels of physical activity. At Roessingh Research and Development, a system was developed, consisting of a Smartphone and an activity sensor, that can measure a patient’s daily activity behavior and provide motivational feedback messages. We are currently looking into ways of increasing the effectiveness of motivational messages that aim to stimulate sustainable behavioral change, by adapting its timing and content to individual patients in their current context of use

Integrated context-aware and cloud-based adaptive home screens for android phones

This is the post-print version of this Article. The official published version can be accessed from the link below - Copyright @ 2011 Springer VerlagThe home screen in Android phones is a highly customizable user interface where the users can add and remove widgets and icons for launching applications. This customization is currently done on the mobile device itself and will only create static content. Our work takes the concept of Android home screen [3] one step further and adds flexibility to the user interface by making it context-aware and integrated with the cloud. Overall results indicated that the users have a strong positive bias towards the application and that the adaptation helped them to tailor the device to their needs by using the different context aware mechanisms

Context-Aware Framework for Performance Tuning via Multi-action Evaluation

Context-aware systems perform adaptive changes in several ways. One way is for the system developers to encompass all possible context changes in a context-aware application and embed them into the system. However, this may not suit situations where the system encounters unknown contexts. In such cases, system inferences and adaptive learning are used whereby the system executes one action and evaluates the outcome to self-adapts/self-learns based on that. Unfortunately, this iterative approach is time-consuming if high number of actions needs to be evaluated. By contrast, our framework for context-aware systems finds the best action for unknown context through concurrent multi-action evaluation and self-adaptation which reduces significantly the evolution time in comparison to the iterative approach. In our implementation we show how the context-aware multi-action system can be used for a context-aware evaluation for database performance tuning

Q-CAD: QoS and Context Aware Discovery framework for adaptive mobile systems

This paper presents Q-CALl, a resource discovery framework that enables pervasive computing applications to discover and select the resource(s) best satisfying the user needs, taking the current execution context and quality-ofservice (QoS} requirements into account. The available resources are first screened, so that only those suirable to the current execution context of the application will be considered; the shortlisted resources are then evaluated against the QoS needs of the application, and a binding is established to the best available

Context-Aware Adaptive Biometrics System using Multiagents

Traditional biometric systems are designed and configured to operate in predefined circumstances to address the needs of a particular application. The performance of such biometrics systems tend to decrease because when they encounter varying conditions as they are unable to adapt to such variations. Many real-life scenarios require identification systems to recognise uncooperative people in uncontrolled environments. Therefore, there is a real need to design biometric systems that are aware of their context and be able to adapt to changing conditions. The context-awareness and adaptation of a biometric system are based on a set of factors that include: the application (e.g. healthcare system, border control, unlock smart devices), environment (e.g. quiet/noisy, indoor/outdoor), desired and pre-defined requirements (e.g. speed, usability, reliability, accuracy, robustness to high/low quality samples), user of the system (e.g. cooperative or non-cooperative), the chosen modality (e.g. face, speech, gesture signature), and used techniques (e.g. pre-processing to normalise and clean biometrics data, feature extraction and classification). These factors are linked and might affect each other, hence the system has to work adaptively to meet its overall aim based to its operational context. The aim of this research is to develop a multiagent based framework to represent a context-aware adaptive biometric system. This is to improve the decision making process at each processing step of traditional biometric identification systems. Agents will be used to provide the system with intelligence, adaptation, flexibility, automation, and reliability during the identification process. The framework will accommodate at least five agents, one for each of the five main processing steps of a typical biometric system (i.e. data capture, pre-processing, feature extraction, classification and decision). Each agent can contribute differently towards its designated goal to achieve the best possible solution by selecting/ applying the best technique. For example, an agent can be used to assess the quality of the input biometric sample to ensure the important features can be extracted and processed in further steps. Another agent can be used to pre-process the biometric sample if necessary. A third agent is used to select the appropriate set of features followed by another to select a suitable classifier that works well in a given condition