实验室在去年首次于International Conference on Web Services (ICWS2011)的Research Track与Application and Experience Track上分别发表长文1篇的基础上，今年再创佳绩，在ICWS2012 Research Track发表长文两篇，International Conference on Service Computing (SCC)发表长文1篇。所发表论文简要信息如下：
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标题: Location-Aware Collaborative Filtering for QoS-Based Service Recommendation
作者: Mingdong Tang; Yechun Jiang; Jianxun Liu; Xiaoqing Liu
来源出版物: 2012 IEEE 19th International Conference on Web Services (ICWS2012)
页: 202 – 209 DOI:10.1109/ICWS.2012.61 出版年: June.2012
摘要: Collaborative filtering is one of widely used Web service recommendation techniques. In QoS-based Web service recommendation, predicting missing QoS values of services is often required. There have been several methods of Web service recommendation based on collaborative filtering, but seldom have they considered locations of both users and services in predicting QoS values of Web services. Actually, locations of users or services do have remarkable impacts on values of QoS factors, such as response time, throughput, and reliability. In this paper, we propose a method of location-aware collaborative filtering to recommend Web services to users by incorporating locations of both users and services. Different from existing user-based collaborative filtering for finding similar users for a target user, instead of searching entire set of users, we concentrate on users physically near to the target user. Similarly, we also modify existing service similarity measurement of collaborative filtering by employing service location information. After finding similar users and services, we use the similarity measurement to predict missing QoS values based on a hybrid collaborative filtering technique. Web service candidates with the top QoS values are recommended to users. To validate our method, we conduct series of large-scale experiments based on a real-world Web service QoS dataset. Experimental results show that the location-aware method improves performance of recommendation significantly.
标题: An Efficient Search Strategy for Service Provider Selection in Complex Social Networks
作者: Yu Xu; Jianxun Liu; Mingdong Tang; Buqing Cao; Xiaoqing Liu
来源出版物: 2012 IEEE Ninth International Conference on Services Computing (SCC2012)
页: 130 - 137 DOI:10.1109/SCC.2012.31 出版年: June. 2012
摘要: The trustworthiness of service providers plays an important role when a consumer selects a service. This paper studies the problem of how to efficiently search and select trustworthy service providers for users in social networks consisting of service providers and consumers. A trust value between two participants can be derived by existing methods from the optimal trust path between them in a social network. When more than one trust factors are taken into consideration, the exact optimal trust path selection algorithm is NP-complete. Although several heuristic algorithms have been proposed to find approximate solutions, their time complexities are still too high to be acceptable in practice, especially when they are used in very large scale social networks. Focusing on reducing trust path searching time, this paper proposes an efficient preprocessing-based search strategy. It exploits structural properties of the social networks and builds an advanced data structure from preprocessing, which can be used to simplify and accelerate the trust path searching. Experimental results show our strategy is very efficient and nearly achieves a constant time complexity. The computed trustworthiness based on our method has excellent performance close to that of the best existing heuristic algorithm.
标题: AWSR: Active Web Service Recommendation Based on Usage History
作者: Guosheng Kang; Jianxun Liu; Mingdong Tang; Xiaoqing Liu; Buqing Cao; Yu Xu
来源出版物: 2012 IEEE 19th International Conference on Web Services (ICWS2012)
页: 186 - 193 DOI: 10.1109/ICWS.2012.86 出版年: June. 2012
摘要: Web services are very prevalent nowadays. Recommending Web services that users are interested in becomes an interesting and challenging research problem. In this paper, we present AWSR (Active Web Service Recommendation), an effective Web service recommendation system based on users' usage history to actively recommend Web services to users. AWSR extracts user's functional interests and QoS preferences from his/her usage history. Similarity between user's functional interests and a candidate Web service is calculated first. A hybrid new metric of similarity is developed to combine functional similarity measurement and nonfunctional similarity measurement based on comprehensive QoS of Web services. The AWSR ranks publicly available Web services based on values of the hybrid metric of similarity, so that a Top-K Web service recommendation list is created for a user. AWSR has been implemented and deployed on the Web. By conducting large-scale experiments based on a real-world Web services dataset, it is shown that our system effectively recommends Web services based on users functional interests and non-functional requirements with excellent performance.
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(徐宇、彭桃供稿)
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