基于社区网络的搜索引擎的研究

摘要

Traditional search engine can't satisfy user's need about real-time information from social network site. A model of real-time search engine is proposed, which used algorithm optimized spider to capture the data with guidance and structured the data by XML, and used the SNS open APl to get the update data. It filtered and classified the information by vector space model, sorted search results according to the correlation algorithms of time relevancy. The model can provide real-time search result and give tips and keep a higher correlation between query word and search result, and make a better search experience to user.%传统搜索引擎无法满足用户对社区网络中实时信息的需求.给出一种实时搜索引擎模型,利用经过算法优化的网络爬虫,实现制导式的数据抓取,同时利用社区网络提供的开放API获得更新数据.通过XML结构化数据,使用改进的向量空间模型对信息进行过滤和分类,并采用考虑时间因素的相关度算法对搜索结果进行排序.实验证明该模型能够实现搜索结果的实时性,并且能够保证搜索项与搜索结果之间比较高的相关度,为用户提供更好的搜索体验.