Shuiguang Deng

Social network-based service recommendation with trust enhancement

Abstract Given the increasing applications of service computing and cloud computing, a large number of Web services are deployed on the Internet, triggering the research of Web service recommendation. Despite of service QoS, the use of user feedback is becoming the current trend in service recommendation. Likewise in traditional recommender systems, sparsity, cold-start and trustworthiness are major issues challenging service recommendation in adopting similarity-based approaches. Meanwhile, with the prevalence of social networks, nowadays people become active in interacting with various computers and users, resulting in a huge volume of data available, such as service information, user-service ratings, interaction logs, and user relationships. Therefore, how to incorporate the trust relationship in social networks with user feedback for service recommendation motivates this work. In this paper, we propose a social network-based service recommendation method with trust enhancement known as RelevantTrustWalker. First, a matrix factorization method is utilized to assess the degree of trust between users in social network. Next, an extended random walk algorithm is proposed to obtain recommendation results. To evaluate the accuracy of the algorithm, experiments on a real-world dataset are conducted and experimental results indicate that the quality of the recommendation and the speed of the method are improved compared with existing algorithms.

Collaborative Web Service QoS Prediction with Location-Based Regularization

Predicting the Quality of Service (QoS) values is important since they are widely applied to Service-Oriented Computing (SOC) research domain. Previous research works on this problem do not consider the influence of user location information carefully, which we argue would contribute to improving prediction accuracy due to the nature of Web services invocation process. In this paper, we propose a novel collaborative QoS prediction framework with location-based regularization (LBR). We first elaborate the popular Matrix Factorization (MF) model for missing values prediction. Then, by taking advantage of the local connectivity between Web services users, we incorporate geographical information to identify the neighborhood. Different neighborhood situations are considered to systematically design two location-based regularization terms, i.e. LBR1 and LBR2. Finally we combine these regularization terms in classic MF framework to build two unified models. The experimental analysis on a large-scale real-world QoS dataset shows that our methods improve 23.7% in prediction accuracy compared with other state-of-the-art algorithms in general cases.

An Efficient Recommendation Method for Improving Business Process Modeling

In modern commerce, both frequent changes of custom demands and the specialization of the business process require the capacity of modeling business processes for enterprises effectively and efficiently. Traditional methods for improving business process modeling, such as workflow mining and process retrieval, still requires much manual work. To address this, based on the structure of a business process, a method called workflow recommendation technique is proposed in this paper to provide process designers with support for automatically constructing the new business process that is under consideration. In this paper, with the help of the minimum depth-first search (DFS) codes of business process graphs, we propose an efficient method for calculating the distance between process fragments and select candidate node sets for recommendation purpose. In addition, a recommendation system for improving the modeling efficiency and accuracy was implemented and its implementation details are discussed. At last, based on both synthetic and real-world datasets, we have conducted experiments to compare the proposed method with other methods and the experiment results proved its effectiveness for practical applications.

An Extended Matrix Factorization Approach for QoS Prediction in Service Selection

With the growing adoption of Web services on the World Wide Web, the issue of QoS-based service selection is becoming important. A common hypothesis of previous research is that the QoS information to the current user is supposed all known and accurate. However, the real case is that there are many missing QoS values in history records. To avoid the expensive and costly Web services invocations, this paper proposes an extended Matrix Factorization (EMF) framework with relational regularization to make missing QoS values prediction. We first elaborate the Matrix Factorization (MF) model from a general perspective. To collect the wisdom of crowds precisely, we employ different similarity measurements on user side and service side to identify neighborhood. And then we systematically design two novel relational regularization terms inside a neighborhood. Finally we combine both terms into a unified MF framework to predict the missing QoS values. To validate our methods, experiments on real Web services data are conducted. The empirical analysis shows that our approaches outperform other state-of-the-art methods in QoS prediction accuracy.

Computing compatibility in dynamic service composition

Dynamically composing services requires mechanisms to ensure component services compatible with each other both at all of the syntax, semantic and behavioral level. This paper focuses on the issue of behavioral compatibility in a service composition. It adopts the π-calculus to model service behaviors and interactions in a formal way. Based on the formalization, it proposes a method to automatically check the behavioral compatibility in a qualitative way. Furthermore, it presents an algorithm to compute the compatibility degree in a quantitative way. The algorithm is implemented in a prototype and its performance analysis is also carried out to show that it can help composing services on the fly and ensure the services compatible with each other to provide functions with newly-added values.

Service Selection for Composition with QoS Correlations

QoS as an important criterion has attracted more and more attention in the service selection process. Various QoS-aware service selection methods have been proposed in recent years. However, few of them take into account of the QoS correlations between services, causing several performance issues. QoS correlations can be defined as that some QoS attributes of a service are not only dependent on the service itself but are also correlated to other services. Since such correlations will affect QoS values, it is important to study how to select appropriate candidate services while taking into account of QoS correlations when generating composite services with optimal QoS values. To this end, we propose a novel method of service selection, called the correlation-aware service pruning (CASP) method. It manages QoS correlations by accounting for all services that may be integrated into optimal composite services and prunes services that are not the optimal candidate services. Our experiments show that this method can manage complicated correlations between services and significantly improve the QoS values of the generated composite services.

DartGrid II: a semantic grid platform for ITS

Intelligent transportation systems offer an alternative approach to solving many problems by implementing advances in information, Internet, communication, and cybernetics technologies. Grid computing can support traffic data semantization, resource sharing, ITS subsystem cooperation, and global-scale distributed computing that connects all kinds of resources. We are currently using grid technology to build DartGrid II, a semantic ITS platform to support resource sharing, service flow management, and cross-domain cooperation.

Trust-Based Personalized Service Recommendation: A Network Perspective

Recent years have witnessed a growing trend of Web services on the Internet. There is a great need of effective service recommendation mechanisms. Existing methods mainly focus on the properties of individual Web services (e.g., functional and non-functional properties) but largely ignore users’ views on services, thus failing to provide personalized service recommendations. In this paper, we study the trust relationships between users and Web services using network modeling and analysis techniques. Based on the findings and the service network model we build, we then propose a collaborative filtering algorithm called Trust-Based Service Recommendation (TSR) to provide personalized service recommendations. This systematic approach for service network modeling and analysis can also be used for other service recommendation studies.

Top-k Automatic Service Composition: A Parallel Method for Large-Scale Service Sets.

Quality-of-Service (QoS)-aware web service composition is of great importance to assemble individual services into a composite one meeting functional and nonfunctional requirements. Given a large number of candidate services, automatic composition is essential so as to derive a composite service efficiently. Most existing methods return one solution that is optimal in some given criteria. This is somewhat rigid in terms of flexibility. In case some component service in the optimal composition becomes unavailable, the composition algorithm has to run again to find another optimal solution. Also, in a lot of circumstances users prefer multiple alternatives over a single one. Therefore, providing top- k service compositions according to their QoS is becoming more desirable. On another aspect, from the perspective of computation efficiency, due to the explosion of the searching space, single-threaded methods are usually not capable of handling a large number of candidate services. This paper tackles these two issues together, i.e., large-scale, QoS-based services composition yielding top- k solutions. The composition algorithm is based on the combination of backtrack search and depth-first search, which can be executed in a parallel way. Experiments are carried out based on the datasets provided by the WS-Challenge competition 2009 and China Web Service 2011. The results show that our approach can not only find the same optimal solution as the winning systems from these competitions, but also provide alternative solutions together with the optimal QoS.

Inverted Indexing for Composition-Oriented Service Discovery

Service discovery becomes a key to hastening the evolution of web services as the number of services is expected to increase dramatically. In this paper, we propose to index all the ontology-annotated outputs in registered services. For each ontology-annotated output, there is a service list which records all the services in the registry that deliver the output. Based on the indexing, we propose a composition-oriented service discovery algorithm, which greatly accelerates the filtering of irrelevant atomic services by making use of the inverted indexing, and increases the likelihood of finding a possible candidate by exploring service composition. Experimental results show that the proposed algorithm provides a better performance on response time than the sequential matchmaking, and a better recall rate than the algorithms without the exploration of composition.