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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]

In recent years, with the development of software development, a large number of developers develop software by invoking API. With the increasing number of APIs, how to accurately recommend the APIs to developers has become a urgently necessary task. In this paper, we discover that there is a relationship between the user and the API, and use such relationships and collaborative learning techniques to finish APIs recommendation. We propose a holistic framework that contains three models. In the models, we design a joint matrix factorization technique and try to discover the preference among APIs invocation process. In natural language processing, word embedding is widely used. In our models, we use doc2vec to turn the representation of users and APIs into vector representation and calculate the similarity separately to generate the relationships. Besides the two modes in users side and APIs side, we also propose an ensemble model fully leveraging the preference mined from both users side and APIs side. We conduct the experiments on a real-world dataset and the experimental results show that our models perform better than all compared methods. [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]