CONTEXTUALIZED RECOMMENDATION BASED ON REALITY MINING FROM MOBILE SUBSCRIBERS

It is difficult to be aware of the personal context for providing a mobile recommendation, because each person's activities and preferences are ambiguous and depend upon numerous unknown factors. In order to solve this problem, we have focused on a reality mining to discover social relationships (e.g., family, friends, etc.) between people in the real world. We have assumed that the personal context for any given person is interrelated with those of other people, and we have investigated how to take into account a person's neighbor's contexts, which possibly have an important influence on his or her personal context. This requires that given a dataset, we have to discover the hidden social networks which express the contextual dependencies between people. In this paper, we propose a semiautomatic approach to build meaningful social networks by repeating interactions with human experts. In this research project, we have applied the proposed system to discover the social networks among mobile subscribers. We have collected and analyzed a dataset of approximately two million people.     

Ubiquitous conference management system for mobile recommendation services based on mobilizing social networks: A case study of u-conference

In this paper, we propose a social network-based mechanism to be aware of user contexts and to provide contextually relevant mobile services to users. A social network among users is regarded as the channel for exchanging and propagating their contexts. To efficiently discover the contexts of a certain users the contexts of his neighbors can be fused to provide mobile recommendation service to mobile subscribers. However, since the social network of a user has been fragmented into more than one, it is difficult to put the number of contexts from the fragmented social networks together (Jung, 2009b). Thereby, we mobilize all possible on- and off-line social networks to build an ego-centric social network. We have implemented the proposed system, which is called u-conference system, by collecting the social network dataset from online (e.g., Facebook, Twitter, CyWorld, and co-authoring patterns in major Korean journals) and offline (e.g., co-participation patterns in a number of Korean domestic conferences). Once we have implemented the system, we have provided mobile services to the conference participants by sending text messages about time schedule of relevant presentations.     

Leveraging personal photos to inferring friendships in social network services

Social network services have become widely used as an important tool to share rich information; in such networks, making new friends is the most basic functionality to enable users to take advantage of their social networks. In this paper we look into personal photos as an additional source for social network analysis and analyze the potential of people tags in the photos for friend recommendations. We also propose a new compact data structure, collectively called Face Co-Occurrence Networks (FCON), which stores crucial and quantitative information about people’s appearance in photos. We discover strong associative relationships among people and recommend reliable social friends by utilizing FCON. Experimental results demonstrate the effectiveness and efficiency of our method for recommending friends in social network services.     

A comparative study of collaboration-based reputation models for social recommender systems

Today, people increasingly leverage their online social networks to discover meaningful and relevant information, products and services. Thus, the ability to identify reputable online contacts with whom to interact has become ever more important. In this work we describe a generic approach to modeling user and item reputation in social recommender systems. In particular, we show how the various interactions between producers and consumers of content can be used to create so-called collaboration graphs, from which the reputation of users and items can be derived. We analyze the performance of our reputation models in the context of the HeyStaks social search platform, which is designed to complement mainstream search engines by recommending relevant pages to users based on the past experiences of search communities. By incorporating reputation into the existing HeyStaks recommendation framework, we demonstrate that the relevance of HeyStaks recommendations can be significantly improved based on data recorded during a live-user trial of the system.     

An unsupervised approach to modeling personalized contexts of mobile users

Mobile context modeling is a process of recognizing and reasoning about contexts and situations in a mobile environment, which is critical for the success of context-aware mobile services. While there are prior works on mobile context modeling, the use of unsupervised learning techniques for mobile context modeling is still under-explored. Indeed, unsupervised techniques have the ability to learn personalized contexts, which are difficult to be predefined. To that end, in this paper, we propose an unsupervised approach to modeling personalized contexts of mobile users. Along this line, we first segment the raw context data sequences of mobile users into context sessions where a context session contains a group of adjacent context records which are mutually similar and usually reflect the similar contexts. Then, we exploit two methods for mining personalized contexts from context sessions. The first method is to cluster context sessions and then to extract the frequent contextual feature-value pairs from context session clusters as contexts. The second method leverages topic models to learn personalized contexts in the form of probabilistic distributions of raw context data from the context sessions. Finally, experimental results on real-world data show that the proposed approach is efficient and effective for mining personalized contexts of mobile users.     

MCORE: a context-sensitive recommendation system for the mobile Web

Abstract: Recommendation systems for the mobile Web have focused on endorsing particular types of content to users. Today, mobile service providers have a more direct recommendation channel, namely the short messaging service. Therefore, mobile service providers should consider both the timing and context of recommendation messages (push messages) that are sent to users. Mobile service providers can learn context-specific user preferences by analysing mobile Web use logs and user responses to push messages. In this paper, we present a context-sensitive recommendation system that can be used to select the optimal context in which to send recommendation messages. We call this system the mobile context recommender system (MCORE). We compared user responses to push messages delivered in and out of suitable contexts as determined by MCORE. The precision of push messages delivered within a suitable context was higher than that of messages delivered outside of one.     

Correlation in mobile call networks from structure perspective

Researchers have done considerable work on the structure of social network recently, but mostly neglected the correlation between two connected nodes. In this paper, our primary goal is to acquire users’ structural properties in mobile call networks. We take a novel perspective—structure correlation between two connected users perspective to study the structural properties. To investigate the structural properties in static and dynamic mobile call networks, we define some metrics which are based on the clique size vectors of mobile call users. By exploring several realworldmobile call networks, which contain hundreds of thousands of mobile call users respectively, we find that people tend to communicate with the one who has a similar structure in static mobile call networks. Moreover, It is found that the connected people have similar structural changes on the whole in dynamicmobile call networks, and the structures of some two connected persons both have growing or shrinking trends. We use a visualization toolkit to give a view of the growing or shrinking scenarios temporally.     

Correlation in mobile call networks from structure perspective

Researchers have done considerable work on the structure of social network recently, but mostly neglected the correlation between two connected nodes. In this paper, our primary goal is to acquire users’ structural properties in mobile call networks. We take a novel perspective-structure correlation between two connected users perspective to study the structural properties. To investigate the structural properties in static and dynamic mobile call networks, we define some metrics which are based on the clique size vectors of mobile call users. By exploring several real-world mobile call networks, which contain hundreds of thousands of mobile call users respectively, we find that people tend to communicate with the one who has a similar structure in static mobile call networks. Moreover, It is found that the connected people have similar structural changes on the whole in dynamicmobile call networks, and the structures of some two connected persons both have growing or shrinking trends. We use a visualization toolkit to give a view of the growing or shrinking scenarios temporally.     

Personal mobile services

Ubiquitous information access through mobile devices has become a typical practice in everyday life. The mobile service paradigm shifts the role of mobile devices from consumers to providers, opening up new opportunities for a multitude of collaborative services and applications ranging from sharing personal information to collaborative participatory sensing. Although many basic principles of the standard Web service approach continue to apply, the inherent constraints of mobile devices and broadband wireless access render the deployment of the standard architecture in mobile environments inefficient. This paper introduced personal services, a user-centric paradigm that enables service-oriented interactions among mobile devices that are controlled via user-specified authorization policies. Personal services exploit the user’s contact list (ranging from phonebook to social lists) in order to publish and discover Web services while placing users in full control of their own personal data and privacy. Experimental validation demonstrates the ability of personal services to foster a new generation of collaborative mobile services. Performance evaluation results show that the publication and discovery through contact lists are efficient and that service announcements and discovery requests can reach a huge number of users in a few seconds. Results also support a conclusion that resources-constrained devices can collaborate to carry out functionalities beyond the ability of their resources limitations.     

Deploying personalized mobile services in an agent-based environment

The mobile Internet allows users to obtain digitized contents and services from wired and wireless networks virtually anywhere at any time via different handheld mobile devices. However, due to the distinct features of mobile users, mobile devices and wireless networks, deploying mobile services is not as straightforward as generally expected. To ensure the success of mobile services, this paper presents a multi-agent framework that considers different contexts to support personalized services on wireless networks. In the proposed approach, client users, content providers, and service providers are all considered as software agents. They interoperate on the same platform to request and deliver mobile services. The most important issues related to agent operations and context awareness in an agent world are also discussed and analyzed. To verify our framework, different application services are developed accordingly on a publicly available middleware platform. Experiments are conducted for both services to evaluate their corresponding performance. The preliminary results show that our multi-agent approach to personalization is promising and efficient in the deployment of mobile services.     

Effect of use contexts on the continuous use of mobile services: the case of mobile games

As mobile services become more popular and people can use them virtually anywhere, research on the effect of use contexts is gaining more attention. This research presents results from a study of continuous use of mobile services in different use contexts as defined by task and consumption place. Using mobile games as an example, the authors proposed a research model that augments current technology adoption theories to fit the hedonic nature of mobile games. The results from conducting an online survey indicate that contextual factors have significant moderating effect on the intention to play mobile games. The diverse lifestyles of users also have different attitudes and concerns regarding using mobile services. The findings suggest that service providers need to take into account the impact of use contexts and the needs of specific users when they design mobile services.     

在线社交网络中的位置服务研究进展与趋势

在基于位置的社交网络中, 用户通过发布嵌入了位置数据的媒体信息获得服务, 如位置或好友推荐、旅行路线推荐等。用户和位置都是网络的主体, 位置作为桥梁将用户的虚拟网络世界与现实世界联系起来。综述了基于位置的在线社交网络中的位置信息获取、用户识别、位置识别、信息的共享与传播及位置隐私的相关研究, 预测了基于位置的在线社交网络未来的研究趋势。     

Leveraging social networks in the adoption of mobile technologies for collaboration

Mobile devices such as tablets, smart phones, and portable computers are connecting users in a myriad of contexts. Social networks can be a benefit for individuals in the knowledge acquisition process, and group dynamics disclosed by Social Network Analysis provides a good basis for studying how mobile collaboration is affected by social networks. This study focuses on two aspects of social network mechanism, namely eigenvector centrality and network reciprocity. The assumption is that social networks influence mobile technology use behaviors, and can be optimized to leverage mobile collaboration. A series of controlled field experiments involving 327 groups were conducted to evaluate the differences between two modes of mobile collaborative settings: individual non-collaborative vs. group collaborative. Results indicate that by leveraging social networks in a mobile platform, study participants were able to positively heighten their collaborative knowledge acquisition process through enhanced group interactions and enjoyment.     

The dynamic competitive recommendation algorithm in social network services

As the number of Twitter users exceeds 175 million and the scale of social network increases, it is facing with a challenge to how to help people find right people and information conveniently. For this purpose, current social network services are adopting personalized recommender systems. Existing recommendation algorithms largely depend on one of content-based algorithm, collaborative filtering, or influential ranking analysis. However, these algorithms tend to suffer from the performance fluctuation phenomenon in common whenever an active user changes, and it is due to the diversities of personal characteristics such as the local social graph size, the number of followers, or sparsity of profile content. To overcome this limitation and to provide consistent and stable recommendation in social networks, this study proposes the dynamic competitive recommendation algorithm based on the competition of multiple component algorithms. This study shows that it outperforms previous approaches through performance evaluation on actual Twitter dataset.     

Understanding User Behavior of Asking Location-Based Questions on Microblogs

With the prevalence of mobile social network services, people can post location-based questions on their social networks to satisfy their needs anytime anywhere. In this article, the authors study location-based questions that people post on microblogs, which is a popular form of social network service. The authors collected posts with geo-tags from Sina Weibo and conducted the study based on about a thousand location-based questions. Their results reveal unique characteristics of location-based questions by analyzing what people ask, how they ask, why they ask, and the context when asking. Location-based questions are closely related to people’s offline activities. Spatial restriction, subjectivity, interactivity, and propagation are the main characteristics that people value for choosing social networks to ask location-based questions. People also apply different phrasing skills to different types of questions. The questions people ask in different contexts also have different focuses. Based on their findings, the authors discuss practical design implications for social networks, location-based Q&A systems, and other applications with location-based features.     

Joint contexts optimization for mobile grid

This paper presents joint contexts optimization in mobile grid. The paper describes device context information for context-aware services in the mobile device collaboration. The objective of the paper is to dynamically deliver services to mobile grid users according to current context of mobile grid environment. A utility function is used as objective function that expresses values for the current contexts. The optimization is carried out by the joint context parameter optimizer with respect to an objective function. A joint contexts optimization algorithm is proposed which decomposes mobile grid system optimization problem into sub-problems. In the experiment, the performance evaluation of joint contexts optimization algorithm is conducted.     

Discovering Typed Communities in Mobile Social Networks

Mobile social networks,which consist of mobile users who communicate with each other using cell phones,are reflections of people’s interactions in social lives.Discovering typed communities(e.g.,family communities or corporate communities) in mobile social networks is a very promising problem.For example,it can help mobile operators to determine the target users for precision marketing.In this paper we propose discovering typed communities in mobile social networks by utilizing the labels of relationships between users.We use the user logs stored by mobile operators,including communication and user movement records,to collectively label all the relationships in a network,by employing an undirected probabilistic graphical model,i.e.,conditional random fields.Then we use two methods to discover typed communities based on the results of relationship labeling:one is simply retaining or cutting relationships according to their labels,and the other is using sophisticated weighted community detection algorithms.The experimental results show that our proposed framework performs well in terms of the accuracy of typed community detection in mobile social networks.     

A cloud-based platform to develop context-aware mobile applications by domain experts

Context-awareness enables the personalization of computer systems according to the users' needs and their particular situation at a given time. The personalization capabilities are usually implemented by programmers due to the complex processes that are involved. However, an important trend in software development is that more and more software systems are being implemented not only by programmers but also by people with expertise in other domains. Since most of the existing context-aware development toolkits are designed for programmers, non-technical users cannot develop these kinds of systems. The design of tools to create context-aware systems by users that do not have programming skills but are experts in the domain where the system is going to be deployed, will contribute to speed up the adoption of these kinds of services by the society. This paper presents a cloud-based platform to ease the development of context-aware mobile applications by people without programming skills. The platform has been designed to be used in a tourism domain. This way, tourism experts can send tourist information to mobile users according to their context data (indoor/outdoor location, language, and date and time range). An energy-efficient mobile app has been developed in order to obtain context data from the user's device and to receive personalized information in real time based on these data. The architecture and implementation details of the system are presented and the evaluation of the platform by tourism domain experts is discussed.     

User needs for location-aware mobile services

Mobile contexts of use vary a lot, and may even be continuously changing during use. The context is much more than location, but its other elements are still difficult to identify or measure. Location information is becoming an integral part of different mobile devices. Current mobile services can be enhanced with location-aware features, thus providing the user with a smooth transition towards context-aware services. Potential application fields can be found in areas such as travel information, shopping, entertainment, event information and different mobile professions. This paper studies location-aware mobile services from the user's point of view. The paper draws conclusions about key issues related to user needs, based on user interviews, laboratory and field evaluations with users, and expert evaluations of location-aware services. The user needs are presented under five main themes: topical and comprehensive contents, smooth user interaction, personal and user-generated contents, seamless service entities and privacy issues.     

Design and development of a mobile peer-to-peer social networking application

The proliferation of wireless and mobile devices such as personal digital assistants and mobile phones has created a large demand for mobile software applications such as social networking software. In addition, the realization and widespread usage of peer-to-peer (P2P) networking have drastically increased the number of applications utilizing these technologies. The convergence of mobile and P2P networking have generated increasing interest in the mobile peer-to-peer (MP2P) community. In this paper, we describe the design and development of a mobile social software (MoSoSo) based on a P2P network architecture using Juxtapose (JXTA) and Juxtapose for Java MicroEdition (JXME). The MoSoSo application allows users to discover, communicate and share resources with one another. We present three facets of designing the MoSoSo: object-oriented software design, network infrastructure design, and user-interface design. The software has been fully implemented and tested on a variety of mobile devices for use in a campus setting. By studying the design and implementation of the MoSoSo, we hope to benefit the entire mobile application development community by providing common models and insights into developing MP2P software.