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中文摘要：
在电子商务中,协同推荐技术能够帮助用户发现感兴趣的东两。在协同推荐中,通常采用最近邻居的方法来产生推荐。随着商品数量的增多,协同推荐所需要的数据集也越来越稀疏,可用数据比例越来越少。为了解决这个问题,本文在传统的评分数据的基础上,引入用户的基本信息,对用户的基本信息进行离散化处理,将用户的基本信息转化成一个0、1的向量,在用户的信息的基础上计算最近邻居,根据最近邻居对用户缺失数据进行补充,在补充后的评分数据上进行聚类计算,并根据聚类结果对用户评分进行预测。实验表明引入用户的基本信息,并采用对基本信息离散化的处理方式进行缺失数据补充,在此基础上进行数据的聚类,能够提高预测评分的准确性。
英文摘要：
Collaborative recommendation technology can help people find something interesting in the e-commerce business field.In collaborative recommendation,there is a common way to generate recommendation called nearest neighbor method.With the increase of commodity quantity,the ratio of useful data is decreasing.In order to solve the sparse problem,we collect and discrete user information on the basis of ordinary score data,then we convert user information to a 0-1 vector.We compute the N-nearest neighbors from the user information matrix and smooth the it using the k-NN.We cluster the user rating matrix to predict the score.The experiment results show that the approach of rating and discretion the user information can improves the predicting score precision.
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