【摘要】 随着高校图书馆馆藏书目的增加,读者在没有具体借阅目标的情况下,从图书馆借阅图书所花费的时间越来越多.针对这种情况,提出了基于内容的高校图书馆推荐算法,详细论述了中文分词、词语权重的计算、向量空间模型的构建以及图书相似度的计算,并对中文分词程序和词语权重算法在短文本中的应用进行了改进,对构建向量空间模型时遇到的稀疏矩阵问题给出了解决方法.研究结果表明,利用基于内容的推荐算法为读者推荐图书,比较符合读者兴趣,容易被读者接受.更多还原

【Abstract】 As the number of the university library books is increasing,readers will spend more and more time to borrow books from library if they don’t have specific lending targets.As for this situation,the content-based recommendation algorithm of university library was proposed,and Chinese words segmentation,weight calculation of words,construction of vector space model and similarity calculation of books were expounded.What’s more,the Chinese word segmentation procedure and the application of words weighting algorithm in short texts were improved,and a solution of sparse matrix that caused by vector space model constructing was raised.The results prove that using the content-based recommendation algorithm to recommend books for readers will meet the needs of the interests of readers and can be accepted by readers easily.更多还原