SMS: A Framework for Service Discovery by Incorporating Social Media Information
SMS: A Framework for Service Discovery by Incorporating Social Media Information
复制标题
SMS：结合社交媒体信息的服务发现框架
DOI：
10.1109/tsc.2016.2631521
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发表时间：
2019-05-01
影响因子：
8.1
通讯作者：
Wu, Zhaohui
中科院分区：
文献类型：
--
作者：
Liang, Tingting;Chen, Liang;Wu, Zhaohui
随着服务的爆炸式增长，包括Web服务、云服务、API和mashup，为消费者发现合适的服务正成为一个迫切的问题。传统的服务发现方法主要面临两个挑战：1）描述文档来源单一，语义信息不足，限制了服务发现的有效性; 2）随着用户功能和非功能需求的不断增加，需要考虑更多的因素。在本文中，我们提出了一种新的框架，称为SMS，有效地发现适当的服务，通过将社会媒体信息。具体来说，我们提出了不同的方法来衡量四个社会因素（语义相似性，流行度，活动，衰减因子）从Twitter收集。采用潜在语义索引（LSI）模型从包含服务的Twitter列表元数据中挖掘服务的语义信息。此外，我们假设目标查询服务匹配函数为多个社会因素的线性组合，并设计了一个权重学习算法来学习测量的社会因素的最佳组合。基于真实世界的数据集从Twitter上抓取的综合实验证明了所提出的框架SMS的有效性，通过一些比较的方法。
With the explosive growth of services, including Web services, cloud services, APIs and mashups, discovering the appropriate services for consumers is becoming an imperative issue. The traditional service discovery approaches mainly face two challenges: 1) the single source of description documents limits the effectiveness of discovery due to the insufficiency of semantic information; 2) more factors should be considered with the generally increasing functional and nonfunctional requirements of consumers. In this paper, we propose a novel framework, called SMS, for effectively discovering the appropriate services by incorporating social media information. Specifically, we present different methods to measure four social factors (semantic similarity, popularity, activity, decay factor) collected from Twitter. Latent Semantic Indexing (LSI) model is applied to mine semantic information of services from meta-data of Twitter Lists that contains them. In addition, we assume the target query-service matching function as a linear combination of multiple social factors and design a weight learning algorithm to learn an optimal combination of the measured social factors. Comprehensive experiments based on a real-world dataset crawled from Twitter demonstrate the effectiveness of the proposed framework SMS, through some compared approaches.