继2011年和2012年在International Conference on Web Services (ICWS)、International Conference on Service Computing (SCC)系列会议上发表长文2篇和3篇基础上，实验室今年再创佳绩，在ICWS2013 Research Track和Applications and Experience Track分别发表长文一篇，SCC2013 Industry and Application Track发表长文1篇。所发表论文简要信息如下：
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标题：Mashup Service Recommendation based on User Interest and Social Network
作者：Buqing Cao, Jianxun Liu, Zibin Zheng, Guangrong Wang
来源出版物: 2013 IEEE 20th International Conference on Web Services (ICWS2013)
摘要： With the rapid development of Web2.0 and its related technologies, Mashup services (i.e., Web applications created by combining two or more Web APIs) are becoming a hot research topic. The explosion of Mashup services, especially the functionally similar or equivalent services, however, make services discovery more difficult than ever. In this paper, we present an approach to recommend Mashup services to users based on user interest and social network of services. This approach firstly extracts users’ interests from their Mashup service usage history and builds a social network based on social relationships information among Mashup services, Web APIs and their tags. The approach then leverages the target user’s interest and the social network to perform Mashup service recommendation. Large-scale experiments based on a real-world Mashup service dataset show that our proposed approach can effectively recommend Mashup services to users with excellent performance. Moreover, a Mashup service recommendation prototype system is developed.
标题：An Efficient Trust Propagation Scheme for Predicting Trustworthiness of Service Providers in Service-Oriented Social Networks
作者：Yu Xu, Jianxun, Mingdong Tang, Frank Liu
来源出版物: 2013 IEEE 20th International Conference on Web Services (ICWS2013)
摘要：Perception of trustworthiness of service providers is a fundamental need in service selection. Trust propagation has been used to predict trustworthiness of service providers in service-oriented social networks. However, existing trust propagation methods may suffer from the scalability problem, i.e., their computation time is likely too high to be acceptable in practice, especially when they are applied to very large-scale service-oriented social networks. Moreover, they rarely consider the structural properties of social networks to optimize their performance. This paper proposes an efficient trust propagation scheme for predicting trust in service-oriented social networks. It exploits the specific structural properties of social networks and builds an advanced data structure from preprocessing to improve the efficiency of trust propagation. Our scheme can support multiple trust propagation strategies. Experiments show that our scheme is much more efficient than well-known trust propagation methods in trust prediction, while its trust prediction results are as accurate as theirs in service-oriented social networks.
标题：Trust-Aware Service Recommendation via Exploiting Social Networks
作者：Mingdong Tang, Yu Xu, Jianxun Liu, Zibin Zheng, Xiaoqing (Frank) Liu
来源出版物：2013 IEEE 20th International Conference on Services Computing (SCC2012)
摘要：With the rapid growth in the number of available services, recommending suitable services to users becomes increasingly important. A number of collaborative service recommendation methods based on user experiences have been proposed for this purpose. Most of them adopt the similarity-based Collaborative Filtering (CF) technique, which tends to identify similar users for a target user and recommends to the target user the services preferred by the similar users. However, a user similar to the target user is unnecessarily trustworthy to him/her. Therefore, the results recommended by similarity-based CF are probably unreliable. Moreover, existing service recommendation methods seldom incorporate social trust relationships among service users into service recommendation. In this paper, we propose a collaborative, trust-aware service recommendation method for service-oriented environments with social networks. The method is based on an integration of the user-service relation and the user-user social relation. Experimental results demonstrate that our service recommendation method significantly outperforms conventional similarity-based recommendation and trust-based service recommendation methods.
（编辑 彭桃）
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