以网络为基础的网络社会包含海量新闻信息，基于内容语义的新闻推荐成为迫切需求。针对上述目的以及社会网络中新闻推荐方法的独特性，改进了基于社会网络的新闻推荐模型。利用历史新闻中人们的新闻相似性、浏览时间、浏览次数、外推行为以及评价等指标，发掘和构建了社会网络中人与人之间的朋友关系，并把它与个人历史浏览记录相结合，计算当前新闻的综合推荐度，从而进行推荐。实验表明，该方法改进了社会网络中的新闻推荐，能更好地向用户推荐新闻。%Web-based network society contains huge amounts of news and information,the news recommendation based on the semantic content has become urgent needs. For the above purposes,and the uniqueness of news recommendation methods in social networks,im-prove the news recommendation model based on social network. Discover and construct the relations between people in social network,u-sing the user's news similarity,scanning time,scanning number etc in history record,and put it with your browsing history records,calcu-lating the comprehensive recommendation of current news to make recommendations. Experiments show the method improves the news recommendation in social network,better recommending the news for users.
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