An event recommendation model using ELM in eventbased social network In recent years, eventbased social network (EBSN) platforms have increasingly entered peoplexe2x80x99s daily life and become more and more popular. In EBSNs, event recommendation is a typical problem which recommends interested events to users. Different from traditional social networks, both online and offline factors play an important role in EBSNs. However, the existing methods do not make full use of the online and offline information, which may lead to a low accuracy, and they are also not efficient enough. In this paper, we propose a novel event recommendation model to solve the above shortcomings. At first, a feature extraction phase is constructed to make full use of the EBSN information, including spatial feature, temporal feature, semantic feature, social feature and historical feature. And then, we transform the recommendation problem to a classification problem and ELM is extended as the classifier in the model. Extensive experiments are conducted on real EBSN datasets. The experimental results demonstrate that our approach is efficient and has a better performance than the existing methods.

Activeness and Loyalty Analysis in EventBased Social Networks Eventbased social networks (EBSNs) are widely used to create online social groups and organize offline events for users. Activeness and loyalty are crucial characteristics of these online social groups in terms of determining the growth or inactiveness of the social groups in a specific time frame. However, there is less research on these concepts to clarify the existence of groups in eventbased social networks. In this paper, we study the problem of group activeness and user loyalty to provide a novel insight into online social networks. First, we analyze the structure of EBSNs and generate features from the crawled datasets. Second, we define the concepts of group activeness and user loyalty based on a series of time windows, and propose a method to measure the group activeness. In this proposed method, we first compute a ratio of a number of events between two consecutive time windows. We then develop an association matrix to assign the activeness label for each group after several consecutive time windows. Similarly, we measure the user loyalty in terms of attended events gathered in time windows and treat loyalty as a contributive feature of the group activeness. Finally, three wellknown machine learning techniques are used to verify the activeness label and to generate features for each group. As a consequence, we also find a small group of features that are highly correlated and result in higher accuracy as compared to the whole features.