It is becoming more and more important to make analysis on activity regularity of population in urban commercial districts, which would be beneficial to commercial infrastructure distribution and construction. The paper presents an approach to mining hot time periods in urban commercial districts u sing POI information and mobile phone data. First, a data field-based self-adaptive DBScan algorithm (DFB-saDBScan) is proposed to make clusters for commercial districts. Second, boundary determination algorithm is presented based on convex hull. Then, a K-means algorithm with constraints (cK-means) is proposed to make extraction of hot time periods from flow of people. On the other hand, we also propose an approach to regional POI proportion analysis via urban POI data. Taking advantage of structure measurement indexes of landscape ecosystem, we analyze relationship between population mobility and facility distribution in commercial districts. Taking a downtown area of Wuhan city, China as case study, our approach is verified to be effectiveness and accuracy.

Wearable device is becoming more and more popular, the emergence of wearable equipment, is widely used in daily life, medical, industrial and scientific research and so on. But most of the wearable equipment still needs people's actual operation, which will bring a lot of inconvenience. For example, when people's hands are not available, they can't be operated. Now, many sensors are embedded in off-the-shelf smartwatch, which make it possible to detect different finger gestures by using these sensors. Past research mostly concentrates on identifying gross hand gestures, which usually need the help of other additional hardware that is expensive and discomfort. In this work we utilize muscle activity data getting from integrated motion sensors (accelerometer, linear accelerometer and gyroscope) nested in smartwatch to recognize tiny finger gestures. We extract features within a 1 second sliding window. We test classification effect of different classifiers and different sensors. In the result, we can recognize 5 unremarkable finger gestures well and identify specific finger gestures with a high accuracy. The result can be applied on current popular smartwatches interaction. Also our result will lay the foundation for related research in finger gestures recognition field.

Recommender systems aim to provide users with preferred items to address the information overload problem in the Web era. Social relations, item connections, and user-generated item reviews and ratings play important roles in recommender systems as they contain abundant potential information. Many methods have been proposed to predict users’ ratings by learning latent topic factors from their reviews and ratings of corresponding items. However, these methods ignore the relationships among items and cannot make full use of the complicated relations between reviews and ratings. Motivated by this observation, we integrate ratings, reviews, user connections and item relations to improve recommendations by combining matrix factorization with the Latent Dirichlet Allocation (LDA) model. Experimental results on two real-world datasets prove that item–item relations contain useful information for recommendations, and our model effectively improves recommendation quality.

Social Networks have experienced increased popularity and rapid growth in recent years. Recommendation is significant for users due to the extremely large amount of information in Social Networks. Most existing recommender systems rely on collaborative filtering techniques which focus on recommending the most relevant items to users based on past rating information of users or items. In Social Networks, the cold-start and data sparsity problems are very serious because new users and items are growing rapidly. Taking the Event Recommendation problem in Event-Based Social Networks as a scenario, many events are newly created and have few feedbacks. Existed collaborative filtering based methods will fail for Social Recommendation due to these problems. Therefore, a more sophisticated recommendation mechanism that can efficiently combine various contextual information to further improve recommendation quality is desired. In this paper, we propose a Context Aware Matrix Factorization model called CAMF which models implicit feedbacks and various contextual information simultaneously for Social Recommendation. Specifically, CAMF is a unified model that combines the Matrix Factorization model which models implicit feedbacks with the Linear Contextual Features model which models explicit contextual features. Extensive experiments on a large real-world dataset demonstrate that the CAMF model significantly outperforms state-of-the-art methods by 12.7% in terms of accuracy for the Event Recommendation problem.

Virtual communities are complex systems interweaving technical and human aspects. We propose here to identify usual functions provided by virtual communities and define a typology based on the common objects or goals shared by their users. The papers included in this special issue show some examples of research works lead in some of these categories of communities.

This paper presents a simple model for representing and evaluating the behavior of users participating in a decentralized data-sharing system. The aim is to explore alternatives to current Web 2.0 systems which suffer from trust attacks, bias, administrative overhead and brittleness. In our model, users get to share documents that they like and they follow other users who are more likely to share documents that they will like. Since there is no central authority, the users also use local and independent means of discovering and ranking documents. We show, using a simulation of the model, that this scheme provides an incentive for users to contribute to the system, and it makes the system more resistant to attacks.

In this paper we propose a Hidden Markov Model in order to predict the sentiment of soccer fans based on information regarding the result of matches. The model was constructed by data collected from a social network where fans of a soccer team periodically expressed feelings towards their team. We show that the choice of a HMM is justified due to the fact that the change in a fan’s sentiment is analogous to a Markovian process of change of state through time. A comparative evaluation will be performed between variations of the proposed models and also between the most accurate of them and classification algorithms. Second order HMM, considering the match results and fan’s gambling information, is the most accurate model even though the models are constructed from results from different kind of championships.

This article summarizes the research methods for processing short texts in the microblog, one of the most popular social media sites, analyzes the bottleneck problems and challenging issues, and discusses the possible direction and strategies for solving the above problems. Comprehensive literature review on short text processing has also been conducted by the way of case tracking and case studies. Text categorization and text clustering are the main research methods for processing short texts in microblogs, and topic detection, sentiment analysis, smart healthcare, business intelligence and location services are hot topics for the application of these methods. We also found some challenges for processing microblog short texts, including semantic processing, noise reduction, clarification of fuzzy themes and handling big data. The real-time online processing of big data is expected to be the main research direction in the future.

Today’s complex online applications often require the interaction of multiple (web) services that belong to potentially different business entities. Interoperability is a core element of such an environment, yet not a straightforward one due to the lack of common data semantics. The problem is often approached by means of standardization procedures in a top-down manner with limited adoption in practice. (De facto) standards for semantic interoperability most commonly emerge in a bottom-up approach, i.e., involving the interaction and information exchange among self-interested industrial agents. In this paper, we argue that the emergence of semantic interoperability can be seen as an economic process among rational agents and, although interoperability can be mutually beneficial for the involved parties, it may also be costly and might fail to emerge. As a sample scenario, we consider the emergence of semantic interoperability among rational web service agents in service-oriented architectures (SOAs), and we analyze their individual economic incentives with respect to utility, risk and cost. We model this process as a positive-sum game and study its equilibrium and evolutionary dynamics. According to our analysis, which is also experimentally verified, certain conditions on the communication cost, the cost of technological adaptation, the expected mutual benefit from interoperability, as well as the expected loss from isolation, drive the process.