About Author

Zhao Yi, born in Wuhan, Hubei Province in 1984, graduated from Wuhan University with a doctor's degree. He is a teacher of the school of mathematics and computer of Guangdong Ocean University. He has been engaged in software engineering, artificial intelligence and service computing for a long time.

赵一，湖北武汉人，1984年生，武汉大学博士毕业，广东海洋大学数学与计算机学院教师，长期从事软件工程、人工智能、服务计算研究。

Book Description

With the rapid growth of the number of Internet software, a large number of Web services with similar functions are produced. The difficulty of service discovery has been caused by the addition of new users. Therefore, it is very important to propose a new service clustering and recommendation algorithm as well as domain knowledge discovery method. As with the traditional service recommendation method system, it is first necessary to enrich the user's application domain knowledge of related services, in order to increase user input query auxiliary words, and solve the problem of cold start. Therefore, there are great significance to service discovery and recommendation by domain knowledge utilization and on-demand extraction. Domain knowledge in this paper refers to the general knowledge of services summarized by domain experts.

Especially, the construction of knowledge networks and the discovery and utilization of hotspot knowledge will contribute to the development of software requirements and service recommendations. Research on appropriate knowledge network construction technology and hotspot knowledge discovery technology can not only make up for the service recommendation difficulties caused by the lack of user domain knowledge, but also support the existing knowledge network evolution needs. In addition, it is a very tedious work by processing disordered and a large number of unprocessed text datasets in PWeb. Orderly organization of service knowledge from the perspective of complex network model structure can improve the accuracy of service discovery and recommendation. Therefore, knowledge-oriented discovery and organization can provide important theoretical and practical significance for Internet service precision recommendation.

随着互联网软件数量快速增长，大量相似功能的Web服务随之产生，由于越来越多缺少专业知识的新用户加入，使得服务发现工作变得越发困难。因此，一种新型的服务聚类与推荐算法以及领域知识发现方法的提出，显得十分重要。与传统服务推荐方法系统一样，首先要丰富用户对相关服务的应用领域知识，以增加用户输入的查询辅助词汇，同时解决冷启动的问题。用户了解服务的领域知识（本文中领域知识是指领域专家归纳的服务共性知识。），可以更好的选择和使用服务。所以领域知识利用率和用户需求的按需提取，对服务发现与服务推荐具有重要的意义。

特别是知识网络的构建与热点知识的发现和利用，将有助于服务查询需求的获取与服务推荐。研究合适的知识网络构建技术和热点知识发掘技术，不仅可以弥补服务推荐时用户领域知识缺失所带来的服务推荐困难等问题，而且可以支撑已有的知识网络演化需求。另外，处理PWeb中无序和大量未经处理的文本描述数据集，也是一件相当繁琐的工作。从复杂网络模型结构角度对服务知识进行有序组织，可以提高服务发现与推荐的准确率。因此，开展面向知识的发掘与组织，能为互联网上服务精准推荐提供重要的理论与实际应用支持。