Location-based Mobile Social Networks Research Articles

Review and Analysis on Trajectory Big Data

A trajectory can be seen as the imprint left in space by a moving object over time. In recent years, with the widespread use of civil GPS and other positioning devices on mobile terminals and the development and popularity of location-based services and mobile social networks, a large amount of trajectory data is increasingly accumulated in daily life and served by different types of applications. A large amount of trajectory data is generated in the fields of traffic, weather, ecology and mobile services. The effective understanding and utilization of these data requires not only automatic and efficient analysis methods, but also intuitive and vivid visualization, and this paper summarizes the application of trajectory data in cities.

Selective Attention to Commercial Information Displays in Globally Available Mobile Application

The prevalence of mobile applications has revived research interest in examining the issue of how commercial information (e.g., adverts) can be displayed to yield click-throughs. Mixed perspectives are proposed over whether or not adverts should be integrated into the primary media content. By anchoring on the load theory of selective attention, this research proposes that user response (attention) to consistent adverts (i.e., adverts that are contextually consistent with the media content) is contingent on how they are displayed (intra- and inter-pages). This study subjected the worldwide users of a location-based mobile social networking application to random manipulations of intra-page and inter-page advert displays. The findings reveal that controlling for the geographical locations of the users, high click-through counts are obtained when consistent adverts are displayed at the early stage of application interaction (top position of the front page), but the effect diminishes as users scroll down to the bottom of front page and navigate to subsequent pages.

Mining community and inferring friendship in mobile social networks

Along with the rapidly growth of mobile terminals and wireless technologies, mobile social networking services are very popular with peoples. Recently many mobile social platforms based on location-based service are developed to allow users to share their check-ins and events with friends. Check-ins data in location-based mobile social networks as well as call detail records (CDR) in mobile communication network may provide insight into community structure, relationships and members in the network. In this paper, we study the problem of community detection and friendship prediction in mobile social networks. We have presented a method to find community structure built on combination entropy, and evaluate modularity of a virtual campus mobile network (V-Net). The outcomes demonstrate that the proposed algorithm mine meaningful communities according to users׳ registration. We investigate the potential friendship among users by taking into account both users׳ links with friends and their check-ins at various positions in Gowalla. This work describes the probability distributions of friendships per number of friends, number of check-ins and number of visited places. The findings confirm that our approaches achieve well performance with aggregated features of user similarity and place entropy than other methods. Moreover, members reveal different social properties in the two networks, in the V-Net influence users tend to hold community together, while in Gowalla community members are likely to visit the common positions.

Study of POI-s recommendation based on a PMR framework

Location-based mobile social networks, such as micro-blogs, web-chats, etc. have provided mass historical user behavior information such as user profiles, check-in times, position tracking, and tweets, for research on mobile social networks. The availability of these data received from users offers a good opportunity to study the users' real-time POI-s recommendation based on context. In this paper, a new framework for location recommendation called PMR is enhanced by drawing from the idea of a PMJ model. One of the greatest strengths of the prototype is its ability to process users' feedback information in real time and update recommendations by integrating the latest methods of context awareness and the strength of a PMJ framework into a PMR framework. Additionally, it can re-score the recommended scheme and update the rank of the scheme so that it fits users' demands. Based on this, a set of algorithms are proposed to solve the key problems in the research of context-aware computing: context awareness, context storage, context recommendation, and context feedback. Experimental results show that the model performs with a higher recall rate (96.93%) and precision rate (87.27%) and a better customer satisfaction index (80.12%).

Recommending friends instantly in location-based mobile social networks

Differently from the general online social network (OSN), location-based mobile social network (LMSN), which seamlessly integrates mobile computing and social computing technologies, has unique characteristics of temporal, spatial and social correlation. Recommending friends instantly based on current location of users in the real world has become increasingly popular in LMSN. However, the existing friend recommendation methods based on topological structures of a social network or non-topological information such as similar user profiles cannot well address the instant making friends in the real world. In this article, we analyze users' check-in behavior in a real LMSN site named Gowalla. According to this analysis, we present an approach of recommending friends instantly for LMSN users by considering the real-time physical location proximity, offline behavior similarity and friendship network information in the virtual community simultaneously. This approach effectively bridges the gap between the offline behavior of users in the real world and online friendship network information in the virtual community. Finally, we use the real user check-in dataset of Gowalla to verify the effectiveness of our approach.

Development of Mobile Social Network Systems Using Real-Time Facial Authentication and Collaborative Recommendations

We propose a location-based mobile social networking system (SNS) that integrates biometric facial authentication using a modified local directional pattern (MLDP), collaborative content recommendations, and communication among users. The proposed system is an effective fusion of a traditional social networking system, a location-based system, and a provider of intelligent recommendations. The prototype and service scenario in this study show possibilities for technical advancement and the future extension of mobile social networking services. To verify the proposed system, we performed experiments to determine the recognition rate for a user's facial image on a real-world smartphone and the preference prediction accuracy of a collaborative filtering-based recommendation system. Through our implementation and preference prediction experiments, we confirmed that the proposed system is highly effective and applicable to convergence using a location-based service and a content recommender.

Mobile social media: Future challenges and opportunities

This article explores the future research opportunities and challenges of mobile social media. First, I problematize what constitutes the boundaries of mobile social media. Distinctions between location-based mobile social networks and non-location-based mobile social networks are established to suggest that the mobility of social media is in fact much broader than location alone. Second, several key theoretical questions are identified for future exploration, including micro, meso, and macro-level theories. Lastly, methodological challenges and opportunities are reflected upon and culminate in the call for multi-disciplinary programs of research to fully understand the role of mobile social media in the world today.