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In the development of applications for mobile multimedia computing, it has been a core problem to improve the efficiency of applications development. The use of Web APIs has played an important role in applications development and mashups creation, but it takes a lot of time to find a suitable Web API. In this paper, we propose to employ APIs recommendation to solve this problem. We use the matrix factorization (MF) model to predict the relationship between user and Web APIs. The relationship between user and Web APIs is represented by latent features. But different from MF model, we find that the relationship between Web APIs have an impact on prediction performance. This paper uses the embedding technique and mines the latent relationships between APIs and we develop a novel prediction model that leverages the latent relationships between APIs, which is named as LRM (LRM is short for latent relationships mining). The latent relations are input into the LRM model to produce the relation prediction between APIs and developers. The experimental results show that our method achieves high performance and behaves the best compared to existing baselines. [ABSTRACT FROM AUTHOR]

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]

With the rapid development of mobile devices, more and more individuals and enterprises move their applications into Apps and enjoy high motility services. They address the performance-variation-aware workflows scheduling problem by leveraging a time-series-based prediction model and a Critical-Path-Duration-Estimation-based (CPDE for short) VM Selection strategy. A real-time multi-feature anomaly detection algorithm based on Non-extensive entropy and Renyi cross entropy is proposed, and a Hidden Markov based state prediction model and a mimic transformation strategy for The Network Topology Mimic Association Graph based on predicted states is proposed. [Extracted from the article]

As a new computing technology proposed with the development of 5G, IoT technologies and increasing requirement of mobile applications and services, edge computing enables mobile application developers and content providers to serve context-aware mobile services (e.g., mobile app recommendation). Mobile app recommendation is known as an effective solution to overcome the information overload in mobile app markets. Most existing models only consider user-app interaction and feature modeling, and neglect the structural information which actually is a crucial part in the scenario of app recommendation. To fully exploit both structural and feature information for app recommendation, this paper proposes a novel heterogeneous graph neural network framework (HGNRec) including one inner module and one outer module. Specifically, the inner module is able to use a node-level attention to learn the importance between a node and its meta-path based neighbors. The outer module with a path-level attention can learn the importance of different meta-paths. With the learned importance from two modules, the comprehensive embeddings for user and app nodes can be generated by integrating features from meta-path based neighbors. Extensive experiments on the real-world Google Play mobile app dataset demonstrate the effectiveness of HGNRec. • HGNRec is the first attempt to model mobile app recommendation under the heterogeneous graph neural network framework. • Hierarchical attention layers can obtain the significances of different neighbor nodes and meta-paths. • HGNRec performs best when both accuracy and efficiency are considered. • Structural information with more heterogeneous relationships leads a superior performance. [ABSTRACT FROM AUTHOR]