中文摘要：

针对虚拟研究环境中的重要资源论文,提出了基于内容过滤的推荐算法,即根据研究者兴趣实现个性化服务,推荐所需论文。该算法采用矢量空间模型作为用户兴趣和资源描述模型,使用余弦相似度计算资源推荐度;基于效率考虑,利用朴素贝叶斯分类算法减小搜索空间。实验表明,推荐效果和效率得到了明显改善。

英文摘要：

This paper proposed a content-based recommending algorithm to recommend papers in VRE for researchers. The algorithm utilized VSM （ vector space model） to represent users＇ interests and resources, calculated recommended degree by cosine similarity. To improve efficiency, used naive Bayes classify method to reduce the searching space. Experimental results demonstrate that this approach can produce better accuracy and performance.