Faced with hundreds of thousands news articles in the websites, it is difficult for users to find they are interested in. Therefore, various recommender systems were built. In recommendation, these read by a user typically form time sequence. However, traditional recommendation algorithms rarely consider sequence characteristic browsing behaviors. performance not good enough predicting next article which will read. To solve this problem, paper proposes time-ordered collaborative filtering...

Margin distribution has been proven to play a crucial role in improving generalization ability. In recent studies, many methods are designed using large margin machine (LDM), which combines with support vector (SVM), such that better performance can be achieved. However, these usually proposed based on single-view data and ignore the connection between different views. this article, we propose new multiview model, called MVLDM, constructs both mean variance. Besides, framework is achieve...

With the increasing popularity of Location-Based Social Networks (LBSNs), a significant volume check-in data users has been generated. Such massive brings difficulties for to efficiently retrieve their desired point-of-interest (POI). As result, POI recommendation systems have received extensive attention from academia and industry. Currently, most existing approaches only provide with fixed set recommended POIs based on historical records users, cannot achieve flexible feasible...

An experimental investigation of the mechanical behavior cemented granules is presented in order to verify and further clarify bond contact model used numerical simulations sands. The were idealized by a pair aluminum rods glued together means calcium aluminate cement. A series was prepared using specially designed sample preparation device. Then, relationships between (i.e., force-displacement failure conditions) examined both simple loading complex tests newly developed auxiliary devices....

Abstract Recently, many deep learning-based models have been successfully applied to click-through rate prediction. However, most previous focus only on feature-level interactions between a single user behavior and the target item or treat user’s historical as sequence uncover hidden interests behind it when mining interests. This can lead interest that evolves over time dynamically being ignored shown by not exploited. Based above problems, we propose evolving with feature co-action network...

With the rapid development of location-based social networks (LBSNs), point interest (POI) recommendation has become an important way to meet users' personalized demands. The aim POI is provide POIs for mobile users. However, traditional systems cannot satisfy reason that system recommend next a user based on user's context information. Also, provides no real-time guarantee performance. In this demo, we propose novel named R2SIGTP which more compared with existing ones. Our following...

In recent years, researches on the mining of user check-in behaviors for point-of-interest(POI) recommendations has attracted a lot attention. Personalized POI recommendation is significant task in location-based social networks(LBSNs) because it helps target users explore their surrounding environment and greatly benefits business real life. Although personalized system can significantly facilitate users' outdoor activities, faces many challenging problems, such as hardness to model human...

With the development of mobile internet and social platforms, lifestyle check-in has become popular in people's daily life. support highly accurate positioning technology, platform accumulated a large amount user data. Based on these data, can provide point-of-initerest recommendation services for users.However, existing algorithms often suffer from some limitations: (1) Recommendation systems using deep learning require extremely high hardware computing power not be deployed edge devices;...

The traditional predictive methods for tracking moving objects usually assume that have linear motion patterns. This severely limits their applicability, since in practice movement is free and uncertain. In this paper, a novel location prediction model based on grey theory presented. proposed adopts the modeling method to predict future of uncertain objects. Comparing with model, relaxes limitation pattern requirement accuracy sampling data. experiment results show can provide more exact than model.

With the increasing prevalence of web services on World Wide Web, a large number functionally equivalent are provided by different providers. Quality-of-Service (QoS), representing nonfunctional characteristics, plays an important role in dealing with how to recommend optimal users among these candidates. Many existing methods for predicting QoS values show that intensively relevant location due great influence network distance and internet connection between services. In this paper, we...

On-demand broadcast is a promising data dissemination approach in mobile computing environments thanks to its adaptability and scalability for large-scale dynamic workload. An important class of emerging applications needs monitor multiple time-varying items continuously be kept aware the up-to-date information. This paper investigates schedule problem disseminating timely periodic continuous queries, systematic highly efficient solution this type provided. In particular, we propose novel...