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Mobile or cellular phones can record various types of context data related to a user's phone call activities. In this paper, we present an approach to discovering individualized behavior rules for mobile users from their phone call records, based on the temporal context in which a user accepts, rejects or misses a call. One of the determinants of an individual's phone behavior is the various activities...
Just-In-Time Recommender Systems involve all systems able to provide recommendations tailored to the preferences and needs of users in order to help them access useful and interesting resources within a large data space. The user does not need to formulate a query, this latter is implicit and corresponds to the resources that match the user's interests at the right time. In this paper, we propose...
This paper presents a new method for automatically extracting smartphone users' contextual behaviors from the digital traces collected during their interactions with their devices. Our goal is in particular to understand the impact of users' context (e.g., location, time, environment, etc.) on the applications they run on their smartphones. We propose a methodology to analyze digital traces and to...
Users context is exploited by an increased number of applications, especially mobile ones, in order to provide their clients with context-oriented services. With our work we try to extend the usage context to laptop and desktop applications and environments. This paper aims analyzing user behavior on three different habitats: at work, at school and at home. The main purpose of this paper is to find...
By utilizing captured characteristics of surrounding contexts through widely used Bluetooth sensor, user-centric social contexts can be effectively sensed and discovered by dynamic Bluetooth information. At present, state-of-the-art approaches for building classifiers can basically recognize limited classes trained in the learning phase; however, due to the complex diversity of social contextual behavior,...
In this paper, we present an approach that extracts attributes of open-domain named entities for the Chinese language. The approach contains two steps. The first step consists in an unsupervised technique which captures high frequency attributes from online encyclopedias. The second step discovers uncommon attributes with low frequency. Lastly, an integrated framework is proposed to obtain attributes...
This demo presents a framework for personalizing data access on the basis of the users' context and of the preferences they show while in that context. The system is composed of (i) a server application, which “tailors” a view over the available data on the basis of the user's contextual preferences, previously inferred from log data, and (ii) a client application running on the user's mobile device,...
While a substantial amount of big data are consumed via mobile devices, accessing content via wireless data connections on mobile devices has its own set of challenges. Among these challenges, speed of data transfer is usually our first priority. Although there are many fast data connections available for Web surfing (3G, LTE etc.), the actual connection speed could vary significantly among different...
As the world moves towards the reality of “intelligent infrastructures,” many avenues open up for research on sensor - based intelligent and ubiquitous systems. Healthcare is one such application area, where sensors and mobile platforms are becoming more useful and hence the idea of analyzing the data feeds from sensors to extract useful meanings is gaining in popularity. Various data-mining techniques...
This paper discusses context-aware data browsing and data retrieval for mobile subscribers. We describe existing models as well as provide a new description for our SpotEx approach. Our model for context-aware data discovery uses mobile phones as proximity sensors. In our concept, any existing or even specially created wireless network node could be used as a presence sensor that can open (discover)...
Mobile end user context has gained increasing attention in the mobile services industry. Context information is seen as an important component in developing new, more personalized, mobile services and applications. This paper studies the effect of end user context on smart phone usage sessions. Smart phone usage sessions are used to depict user behavior and usage habits of smart phone users on a high...
The commonly used phone communication is designed on the assumption that caller actively telephones callee without any knowledge of callee's situation. Along with the development of computing and sensor technology, context information of user can be sensed, inferred from pervasive environment. Caller can determine in what situation callee locates with context information of callee. Some research work...
Recently, inferring or sharing of mobile contexts has been actively investigated as cell phones have become more than a communication device. However, most of them focused on utilizing the contexts on social network services, while the means in mining or managing the human network itself were barely considered. In this paper, the SmartPhonebook, which mines users' social connections to manage their...
Users often visit many stores while they compare merchandise in order to purchase the merchandise or merchandise related to it. Given a service providing information such as location of these stores, they can reduce their time and effort spent by wandering around them because they can go straight to these stores. And they can obtain new purchase opportunities because they can know what kinds of related...
An accurate method of estimating the tie strengths of mobile phone users will enable the development of socially-aware mobile applications. The strength of ties between mobile phone users can be estimated for large numbers of users by using the aggregated call duration as a proxy for tie strength. This paper investigates appropriate proxies for tie strength in a network of less than one hundred people...
The development of mobile network technology provides a great potential for social networking services. This paper studied data mining for social network analysis purpose, which aims at find people's social network patterns by analyzing the information about their mobile phone usage. In this research, the real database of MIT's Reality Mining project is employed. The classification model presented...
This study investigates the adoption and appropriation of mobile phone technologies by building on the technology adoption and appropriation theories on different age group and gender in the Malaysian society. In order to achieve the objectives of the study, a nationwide survey using computer assisted telephone interviews (CATI) was conducted on 1710 randomly selected mobile phone users in Malaysia...
In this paper, we target the problem of the situation-aware application (task) recommendation on mobile devices. To tackle this problem, we develop both supervised and unsupervised approaches. We use Naive Bayesian as a supervised approach, and co-clustering and vector quantization (VQ) as unsupervised approaches. We evaluate the performance of the proposed approaches with both synthetic and actual...
Detecting and understanding Web search intention have considerably gained in interest during the last years. This is challenging in mobile environments due to the impact of ubiquity on the mining process: changing user's context influences the information needs, resource constraints affect the process execution. Moreover, in mobile devices it is possible to find situations where the analysis of user's...
In this paper, we present a new method to extract product entity from Chinese customer reviews. The approach requires no segmentation, no domain dictionary and little prior domain knowledge, which is more suitable for domain with resource-limited. Quite different from the previous work, the proposed method first get the entity candidates use a general version bootstrapping algorithm and then distribute...
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