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Recently, more and more mobile apps are employed in the marketing field with technical advances. Mobile marketing apps have become a prevalent way for enterprise marketing. Therefore, it has been an important and urgent problem to provide personalized and accurate recommendation in mobile marketing, with a large number of items and limited capability of mobile devices. Recommendation have been investigated widely, however, most existing approaches fail to consider the stability or change of users' behaviors over time. In this paper, we first propose to mine the periodic trends of users' consuming behavior from historical records by KNN(K-nearest neighbor) and SVR (support vector regression) based time series prediction, and predict the next time when a user re-purchases the item, so that we can recommend the items which users have purchased before at proper time. Second, we aim to find the regularity of users' purchasing behavior during different life stages and recommend the new items that are needed and proper for their current life stage. In order to solve this, we mine the mapping model from items to user's life stage first. Based on the model, users' current life stage can be estimated from their recent behaviors. Finally, users will be recommended with new items which are proper to their estimated life stage. Experimental results show that it has improved the effectiveness of recommendation obviously by mining users' consuming behaviors with temporal evolution. [ABSTRACT FROM AUTHOR]

Predicting urban traffic volume is of great significance to traffic management and urban construction. An accurate prediction model can help drivers optimize driving routes, allocate resources reasonably and reduce urban traffic congestion. Most of the existing studies do not consider the complex nonlinear spatio-temporal relationship. In the spatial dimension, they do not consider the impact of regional semantics and regional interactions. In the temporal dimension, they ignore the impact of long-term historical information and key time points. Aiming at the complexity of traffic data, in this paper, we design a ResNet-TCN model to predict the urban traffic volume. Firstly, we construct and extract features from the vehicle GPS tracking and external information, such as velocity, time, location and weather. Then, we obtain regional semantic information by the ResNet model and combine the weights of the regional division with the average vehicle velocity into a two-channel matrix. We extract the key features of the matrix sequence and predict the velocity by the TCN model. Finally, we estimate the traffic volume through a traffic volume inference model in the traffic field. We conduct a large number of experiments on the actual dataset of Chengdu and compare our model with the existing models. The experimental results show that our method has better performance on prediction accuracy. [ABSTRACT FROM AUTHOR]

Along with the popularity of intelligent services and mobile services, service recommendation has become a key task, especially the task based on quality-of-service (QoS) in edge computing environment. Most existing service recommendation methods have some serious defects, and cannot be directly adopted in edge computing environment. For example, most of existing methods cannot learn deep features of users or services, but in edge computing environment, there are a variety of devices with different configurations and different functions, and it is necessary to learn deep features behind those complex devices. In order to fully utilize hidden features, this paper proposes a new matrix factorization (MF) model with deep features learning, which integrates a convolutional neural network (CNN). The proposed mode is named Joint CNN-MF (JCM). JCM is capable of using the learned deep latent features of neighbors to infer the features of a user or a service. Meanwhile, to improve the accuracy of neighbors selection, the proposed model contains a novel similarity computation method. CNN learns the neighbors features, forms a feature matrix and infers the features of the target user or target service. We conducted experiments on a real-world service dataset under a batch of cases of data densities, to reflect the complex invocation cases in edge computing environment. The experimental results verify that compared to counterpart methods, our method can consistently achieve higher QoS prediction results. [ABSTRACT FROM AUTHOR]