Real-Time News Recommender System

Workflow model has been successfully applied to the traditional computing environment. Currently, there are many studies trying to apply workflow to ubiquitous computing environment. In ubiquitous computing environment, a service must be based on the user's context information to be executed. Now, however, there is no workflow system has a pervasive computing environment to support context-aware services. We intend to establish a pervasive computing environment which can be used in smart home workflow model. The system model based on the user's current context information to provide services for the user's current context, and use the Petri net model tools for workflow modeling. Thus parallel to the workflow model and to visualize formal verification correctness. Workflow using Petri net model which is a graphical and mathematical sets in one, is an ideal tool for formal modeling. We intend to apply the system model to a specific smart home, to validate the correctness of our proposed system.

This paper considers a context similarity algorithm based on ontology semantic which recommends commodities to users by the similarity between current user's context and source context recorded. First, we divide the context ontology tree into many subtrees with subtree cutting algorithm. According to calculating the similarity of subtrees with the weight sum, we obtain the similarity between the source context and target context. Meanwhile, during calculating the similarity of subtrees, this paper allows for ontology semantic similarity, and adds the structure similarity degree algorithm on the basis of the old ontology similarity degree algorithm, which makes the result more accord with actual, and finally the experiment is verified.

With continuous increase of available online spatial data, requirements for the adaptation of spatial content to user's context becomes increasingly important. Location-based services (LBS) deliver to the user information specific to a given location preferably using mobile devices. Personalizing the information provided to the user to take into account her/his information preferences and current location, the problem of inundating the user with irrelevant information is avoided. The research presented in this paper introduces an approach which implicitly monitors the user's activity and generates a user profile reflecting her/his information preferences based on the interactions of the user with the system, and her/his physical location and movements. The system recognizes the user profile and adapts accordingly both to provide suitable information, and to prioritize the functionality appropriate to the user's current context.

With the increasing number of mobile devices and the development of all kinds of mobile technology, the volume of data is getting larger and larger. People have to face huge amounts of data. At the same time, as the continuous development of various mobile devices providing more data acquisition technology, the dimension of data have a bigger growth too. It's not enough for the existing technology in the treatment of huge amounts and high dimension data. User's interests in different time have been taken into equal consideration with the method being used, which leads to lack of effectiveness in the given period of time. In order to solve this problem, personalized recommendation is faced with great challenge. What's more, timeliness which is getting more and more urgent in people's need is very important. Towards the problem of recommendation based on context-aware, this paper propose a personalized recommendation algorithm based on user context clustering, with the consideration of timeliness. This algorithm uses the idea of contextual pre-filtering. A method of fuzzy clustering is used on the users' context in history data set first to construct the user set which is similar with current user context, and then combined with user-based collaborative filtering algorithm for personalized recommendation.

In an open source cloud computing platform such as OpenStack, operators use Role-Based Access Control (RBAC) model to grant access to cloud resources. However, these user-level role-based access control techniques fail to include comprehensive user context. We believe a situational aware framework will improve security by bringing in user's context to such cloud systems. In this paper, we create a semantically rich context-sensitive access control system for OpenStack by incorporating the user's current context attributes like location, time, etc. In a proof-of-concept implementation, we integrate a knowledge graph with our own access control system to express and enforce the contextual-situation policies in OpenStack. The proposed system provides enhanced, flexible access control while minimizing the overhead of altering the existing access control framework. We also discuss various use cases, to highlight the benefits of our system and show enforcement results.

We analyze preferences and the reading flow of users of a popular Austrian online newspaper. Unlike traditional news filtering approaches, we postulate that a user's preference for particular articles depends not only on the topic and on propositional contents, but also on the user's current context and on more subtle attributes. Our assumption is motivated by the observation that many people read newspapers because they actually enjoy the process. Such sentiments depend on a complex variety of factors. The present study is part of an ongoing effort to bring more advanced personalization to online media. Towards this end, we present a systematic evaluation of the merit of contextual and non-propositional features based on real-life clickstream and postings data. Furthermore, we assess the impact of different recommendation strategies on the learning performance of our system.

Context prediction approaches forecast future contexts based on known context patterns to adapt e.g., services in advance. In the case of the user's context history not providing suitable context information for the observed context pattern, to the best of our knowledge context prediction algorithms will fail to forecast the appropriate future context. To overcome the gap of missing context information in the user's context history, we propose the Collaborative Context Prediction (CCP) approach. CCP utilises the collaborative characteristics of existing recommendation systems of social networks. To evaluate the CCP method an experimental comparison of the proposed method against the local Alignment context predictor is carried out.

In recent years, numerous sensing devices and wireless networks are immersed into our living environments, creating the Internet of Things (IoT) integrating the cyber and physical objects. Searching for objects in IoT is a challenging problem because the context relationships among IoT objects are various and complex. The traditional web search approaches cannot work well in the IoT search domain because they miss the critical characteristics of the context relationships. In addition, a user's dynamic and changing context affects the user's information needs, and an IoT search system should exploit context relationship in IoT for retrieving relevant information suitable for the user's current context. In this paper, we present a context-aware search system for IoT, which aims to search objects and related information with more suitable results. We construct a hierarchical context model based on ontology to represent the IoT objects and their contextual relationships. Then, searches are executed with consideration of users' context that is recognized by a context-aware hidden Markov model. Experimental results confirmed that users could obtain more suitable and reasonable search results than with a typical web or map search system.

The vision of pervasive environments is being realized more than ever with the proliferation of services and computing resources located in our surrounding environments. Identifying those services that deserve the attention of the user is becoming an increasingly-challenging task. In this paper, we present an adaptive multi-criteria decision making mechanism for recommending relevant services to the mobile user. In this context, “Relevance” is determined based on a user-centric approach that combines both the reputation of the service, the user's current context, the user's profile, as well as a record of the history of recommendations. Our decision making mechanism is adaptive in the sense that it is able to cope with users' contexts that are changing and drifts in the users' interests, while it simultaneously can track the reputations of services, and suppress repetitive notifications based on the history of the recommendations. The paper also includes some brief but comprehensive results concerning the task of tracking service reputations by analyzing and comprehending Word-of-Mouth communications, as well as by suppressing repetitive notifications. We believe that our architecture presents a significant contribution towards realizing intelligent and personalized service provisioning in pervasive environments.

Context-aware recommender systems have been widely investigated in both academia and industry because they can make recommendations based on a user's current context (e.g., location, time). However...

The interest of users in handheld devices is strongly related to their location. Therefore, the user location is important, as a user context, for news article recommendation in a mobile environment. This paper proposes a novel news article recommendation that reflects the geographical context of the user. For this purpose, we propose the Explicit Localized Semantic Analysis (ELSA), an ESA-based topical representation of documents. Every location has its own geographical topics, which can be captured from the geo-tagged documents related to the location. Thus, not only news articles but locations are also represented as topic vectors. The main advantage of ELSA is that it stresses only the topics that are relevant to a given location, whereas all topics are equally important in ESA. As a result, geographical topics have different importance according to the user location in ELSA, even if they come from the same article. Another advantage of ELSA is that it allows a simple comparison of the user location and news articles, because it projects both locations and articles onto an identical space composed of Wikipedia topics. In the evaluation of ELSA with the New York Times corpus, it outperformed two simple baselines of Bag-Of-Words and LDA as well as two ESA-based methods. Rt10 of ELSA was improved up to 46.25% over other methods, and its [email protected] was always higher than those of the others regardless of k.

In this paper, we develop a novel real-time, interactive, automatic multimodal exploratory environment that dynamically adapts the media presented, to user context. There are two key contributions of this paper-(a) development of multimodal user-context model and (b) modeling the dynamics of the presentation to maximize coherence. We develop a novel user-context model comprising interests, media history, interaction behavior and tasks, that evolves based on the specific interaction. We also develop novel metrics between media elements and the user context. The presentation environment dynamically adapts to the current user context. We develop an optimal media selection and display framework that maximizes coherence, while constrained by the user-context, user goals and the structure of the knowledge in the exploratory environment. The experimental results indicate that the system performs well. The results also show that user-context models significantly improve presentation coherence

We propose an acquisitional context engine (ACE), a middleware that supports continuous context-aware applications while mitigating sensing costs for inferring contexts. The ACE provides user's current context to applications running on it. In addition, it dynamically learns relationships among various context attributes (e.g., whenever the user is Driving, he is not AtHome). The ACE exploits these automatically learned relationships for two powerful optimizations. The first is inference caching that allows the ACE to opportunistically infer one context attribute (AtHome) from another already-known attribute (Driving), without acquiring any sensor data. The second optimization is speculative sensing that enables the ACE to occasionally infer the value of an expensive attribute (e.g., AtHome) by sensing cheaper attributes (e.g., Driving). Our experiments with two real context traces of 105 people and a Windows Phone prototype show that the ACE can reduce sensing costs of three context-aware applications by about 4.2 times, compared to a raw sensor data cache shared across applications, with a very small memory and processing overhead.

With rapid growth of egocentric videos from wearable devices, the need for instant video event detection is emerging. Different from conventional video event detection, it requires more considerations on real-time event detection and immediate video recording due to the computational cost on wearable devices (e.g., Google Glass). Conventional work of video event detection analyzed video content in an offline process and it is time-consuming for visual analysis. Observing that wearable devices are usually along with sensors, we propose a novel approach for instant event detection in egocentric videos by leveraging sensor-based motion context. We compute statistics of sensor data as features. Next, we predict the user's current motion context by a hierarchical model, and then choose the corresponding ranking model to rate the importance score of the timestamp. With importance score provided in real-time, camera on the wearable device can dynamically record micro-videos without wasting power and storage. In addition, we collected a challenging daily-life dataset called EDS (Egocentric Daily-life Videos with Sensor Data), which contains both egocentric videos and sensor data recorded by Google Glass of different subjects. We evaluate the performance of our system on the EDS dataset, and the result shows that our method outperforms other baselines.

Dynamic adaptation is to create proper information from the context of the end user exposed to a ubiquitous computing environment. Thereby, we propose a Context Ontology-based personalized recommendation in the ubiquitous computing Environment (CORALE) framework to process dynamic adaptation considering social context as a kind of tacit knowledge. The CORALE uses two types of reasoning for the dynamic adaptation, (i) we create a case including an end user's context and decision-making result and reuse the case to recommend a newly requested context, (ii) we adopt constraint satisfaction problem solving algorithm when additional adaptation is needed according to the change in the current context or end user' preference. To prove the necessity of the CORALE framework, we illustrate motivating scenarios.