The increasing number of available Web Application Programming Interfaces (APIs) in various service sharing communities have enabled software developers to develop their interested multimedia mashups quickly and conveniently. In this situation, a multimedia mashup with complex functionalities could be achieved by composing a set of pre-selected Web APIs by software developers. However, due to the APIs diversity in terms of development organization, programming language, invocation interface, etc, it is often difficult to determine the compatibility between the APIs selected by multimedia mashup developers beforehand especially when the developers have little background knowledge of APIs, which significantly decreases the successful rate of subsequent multimedia mashup development. In response to this challenge, we propose a subgraph matching-based compatible API’s composition recommendation method, called SubMC_WACR. The advantage of SubMC_WACR is that it can directly search for the API’s subgraphs that not only meet the functional requirements of the multimedia mashup but also are compatible with each other, thus boosting the effectiveness of multimedia mashup development. Through extensive experiments on a real dataset crawled from the Web API sharing platform ProgrammableWeb.com, we have demonstrated that our proposed recommendation method achieves significant improvements in terms of recommendation precision and compatibility compared with other competitive API recommendation methods.

Service recommendation provides an effective solution to extract valuable information from the huge and ever-increasing volume of big data generated by the large cardinality of user devices. However, the distributed and rich multi-source big data resources raise challenges to the centralized cloud-based data storage and value mining approaches in terms of economic cost and effective service recommendation methods. In view of these challenges, we propose a deep neural collaborative filtering based service recommendation method with multi-source data (i.e., NCF-MS) in this paper, which adopts the cloud-edge collaboration computing paradigm to build recommendation model. More specifically, the Stacked Denoising Auto Encoder (SDAE) module is adopted to extract user/service features from auxiliary user profiles and service attributes. The Multiple Layer Perceptron (MLP) module is adopted to integrate the auxiliary user/service features to train the recommendation model. Finally, we evaluate the effectiveness of the NCF-MS method on three public datasets. The experimental results show that our proposed method achieves better performance than existing methods.