关键词: 示的相关度计算模型, 并通过文档对象模型树形结构, 获取以可扩展标记语言关键词表示的制造信息语料并形成语料库, 实现符合用户兴趣的相似Web页面判断和收集, 通过丰富站点摘要技术, 实现制造信息的主动推荐服务, 从

Abstract: Existing enterprises information systems seldom take differenet requirement tendency of different personnel into consideration, the idea of Manufacturing Information Active Recommendation (MIAR) was put forward to transfer proper information to proper personnel correctly and timely. Through analyzing methods of manufacturing information acquirement in Web environment and its requirement for active recommendation service, the architecture of MIAR service was constructed. By active learning and passive learning methods, users'interest toward specific information were acquired, and content requirement structure of manufacturing information was also defined. To analyze Web pages, similarity computing model based on words distance and relevancy computing model based on key words was proposed. Manufacturing information corpus buildup by eXtensible Markup Language (XML) key words from tree structures of Document Object Module (DOM) was obtained so as to judge and collect similar Web pages according to users'interest. The MIAR was implemented by Rich Site Summary (RSS) to improve enterprises information systems'service quality and strengthen enterprises'response to dynamic information.

Key words: Web services, manufacturing information, active recommendation, information system, user interest acquirement, rich site summary