Existing service recommendation methods, that employ memory-based collaborative filtering (CF) techniques, compute the similarity between users or items using nonfunctional attribute values obtained at service invocation. However, using these nonfunctional attribute values from invoked services alone in similarity computation for personalized service recommendation is not sufficient. This is because two users may invoke the same service, but their personalized preferences on nonfunctional attributes that describe the service may be different. Thus, to accurately personalize service recommendation, it is necessary for CF-based recommendation systems to incorporate users personalized preferences on nonfunctional attributes when recommending services to an active user. This paper proposes a CF-based service recommendation method that considers users' personalized preference on nonfunctional attributes. We first compute the satisfaction of an active user's preference on nonfunctional attribute(s) and then use these satisfaction values to obtain their similarity measures. We then employ the top-k algorithm to identify neighbors of the active user and subsequently, use the weighted average with mean offset method to predict his/her nonfunctional attribute. We evaluate our method using real-world services and also conduct experiments to show that the proposed method improves recommendation accuracy significantly.

For service users to get the best service that meet their requirements, they prefer to personalize their nonfunctional attributes, such as reliability and price. However, the personalization makes it challenging for service providers to completely meet users’ preferences, because they have to deal with conflicting nonfunctional attributes when selecting services for users. With this in mind, users may sometimes want to explicitly specify their trade-offs among nonfunctional attributes to make their preferences known to service providers. In this article, we present a novel service selection method based on fuzzy logic that considers users’ personalized preferences and their trade-offs on nonfunctional attributes during service selection. The method allows users to represent their elastic nonfunctional requirements and associated importance using linguistic terms to specify their personalized trade-off strategies. We present examples showing how the service selection framework is used and a prototype with real-world airline services to evaluate the proposed framework's application.

The number of online services is rapidly increasing due to the increased adoption of services-oriented technology. In this context, recommendation systems can provide high-quality Web services that meet Mashup developers’ expectations. The use of different kinds of auxiliary information in recommendation systems is commonplace. They enrich recommendation systems so that they can make relevant recommendations. Yet, knowledge graph-based service recommendation usually only considers the textual semantic information of the service and ignores the importance of discrete attribute information of the service for service recommendation. This may lead to the inability to comprehensively capture the multidimensional characteristics of services, thus affecting the accuracy and reliability of recommendations. To this end, this paper proposes a Web service recommendation method for Mashup creation that exploits pre-training representation and knowledge graphs as auxiliary information. Firstly, it uses the neural factorization machines and Doc2Vec to obtain the text semantic representation and the discrete attribute representation of Web services respectively. Secondly, it combines the text semantic representation and the discrete attribute representation to generate the pre-training representation as the input of knowledge graph convolutional networks. Thirdly, it constructs the Web services knowledge graph using Mashups, Web services, and related information and learns the preferences of Mashup developers and higher-order structural relations between Web services using knowledge graph convolutional networks to complete Web service recommendations. Finally, the proposed method is compared to the baselines, i.e., feature interaction-based (LR, FM, FFM, and NFM), KG-based (RippleNet and KGCN), and Doc2Vec for entity representation-based (DKGCN) Web service recommendation methods, using a real-world dataset from ProgrammableWeb. The experimental results show that the proposed method significantly improves the quality of recommendation in terms of the accuracy, recall, and Micro-F1.

Web service selection based on quality of service (QoS) has been a research focus in an environment where many similar web services exist. Current methods of service selection usually focus on a single service request at a time and the selection of a service with the best QoS at the user's own discretion. The selection does not consider multiple requests for the same functional web services. Usually, there are multiple service requests for the same functional web service in practice. In such situations, conflicts occur when too many requesters select the same best web service. This paper aims at solving these conflicts and developing a global optimal service selection method for multiple related service requesters, thereby optimizing service resources and improving performance of the system. It uses Euclidean distance with weights to measure degree of matching of services based on QoS. A 0-1 integral programming model for maximizing the sum of matching degree is created and consequently, a global optimal service selection algorithm is developed. The model, together with a universal and feasible optimal service selection algorithm, is implemented for global optimal service selection for multiple requesters (GOSSMR). Furthermore, to enhance its efficiency, Skyline GOSSMR is proposed. Time complexity of the algorithms is analyzed. We evaluate performance of the algorithms and the system through simulations. The simulation results demonstrate that they are more effective than existing ones.