Landslide susceptibility maps (LSMs) are very crucial for planning policies in hazardous areas. However, the accuracy and reliability of LSMs depend on available data and the selection of suitable methods. This study is conducted to produce LSMs by combinations of machine learning methods and weighting techniques for Ha Giang province, Vietnam, where has limited data. In study area, we gather 11 landslide conditioning factors and establish a landslide inventory map. Computing the weights of classes (or factors) is very important to prepare data for machine learning methods to generate LSMs. We first use frequency ratio (FR) and analytic hierarchy process (AHP) techniques to generate the weights. Then, random forest (RF), support vector machine (SVM), logistic regression (LR), and AHP methods are combined with FR and AHP weights to yield accurate and reliable LSMs. Finally, the performance of these methods is evaluated by five statistical metrics, ROC and R-index. The empirical results have shown that RF is the best method in terms of R-index and the five metrics, i.e. TP rate (0.9661), FP rate (0.0), ACC (0.9835), MAE (0.0046), and RMSE (0.0350) for this study area. This study opens the perspective of weight-based machine learning methods for landslide susceptibility mapping.

Event-based social networks (EBSNs) have gained increasing popularity and rapid growth, EBSNs provide services for users to create events and make plan to attend. Developing and creating recommendation models are important and hot issues in EBSNs in recent years, such as event recommendation to users. Although several recommendation models have been proposed, event attendees recommendation models are not fully studied. In this paper, we study the event attendees recommendation problem through empirical experiments. Because of the nature of new events and severe data sparsity in EBSNs, traditional recommender systems work less efficiently for event attendees recommendation problem. To solve this problem, we propose a new location-based topic model that is based on scores of users computed from three major factors extracted from previously attended events, namely content, location and time. The proposed model includes two phases. The first phase uses the topic modeling Latent Dirichlet Allocation (LDA) and Jensen Shannon divergence to compute the similarity of events based on their contents. The spatial and temporal factors are also calculated. The scores of previous events with an upcoming event are computed from a combination of these three factors. Previous events with high scores are selected, then users who are extracted from the selected events are scored by temporal factors of these events in the second phase. Finally, we recommend users with top scores to the upcoming event. A series of experiments were conducted on real data collected from Meetup Event and the results have demonstrated the improvement of our model over baseline methods.

Event-based 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 event-based 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 well-known 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.