建立倒排索引(Inverted Index)，并使用空间向量模型(Space Vector Model)对检索结果进行排序
建立倒排索引(Inverted Index)，并使用空间向量模型(Space Vector Model)对检索结果进行排序
python main.py
输入检索的关键词和对应的csv文件，会在当前目录下创建倒排索引表。
这里使用一个csv文件来简单代表文档的集合，csv文件的一行代表一个文档
{词项1: {文档1: 在文档1中的频率...}...}
词项t的词项频率指的是t在d中出现的次数
含有词项t的文档数量
引入这一概念是为了对罕见的词赋予一个高的权重
词项t在文档d中的权重可以采用下式计算：
这样我们可以得到文档-词项的权值矩阵，查询时我们以相同的计算方法求得查询内容的tf-idf向量，再和每一文档求余弦相似度，即可找出“最相似”的文档
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