User Preference Mining through Collaborative Filtering and Content Based Filtering in Recommender System
User Preference Mining through Collaborative Filtering and Content Based Filtering in Recommender System
复制标题
推荐系统中通过协同过滤和基于内容的过滤挖掘用户偏好
DOI：
10.1007/3-540-45705-4_26
复制
发表时间：
2002
期刊：
影响因子：
--
通讯作者：
Jung
中科院分区：
文献类型：
--
作者：
Su;Jung
以前关于实现协作和基于内容的过滤系统的研究都没有得出决定性的解决方案，从这一点来看，推荐的准确性下降是值得注意的。本文将首先讨论如何最大限度地减少这两个系统的缺点的方法。然后，通过比较所得到的用户简档和群组简档的相似性，可以提高用户和群组偏好的准确性。要减少负面影响，必须做到以下几点。在基于内容的过滤具有多维特征的情况下，应该使用关联词挖掘来提取相关特征。挖掘出的特征所表示的数据不是表示成一串数据，而是表示成一个相关的词向量。为了弥补其缺陷，基于内容的过滤系统应该使用贝叶斯分类，这是一种通过维护相关词知识库来对产品进行分类的系统。此外，为了降低用户乘积矩阵的稀疏性，必须降低维度。为了降低栏目的维度，有必要结合相关词汇知识库使用贝叶斯分类。最后，为了减少行的维度，必须将用户分类到集群中。
Previous studies on implementing both collaborative and content based filtering systems fail to come to a conclusive solution, and in this light, the decreased accuracy of recommendations is notable. This paper shall first address methods on how to minimize the shortcomings of the two respective systems. Then, by comparing the similarity of the resulting user profiles and group profiles, it is possible to increase the accuracy of the user and group preference. To lessen the negative aspects the following must be done. With the case of the multi dimensional aspects of content based filtering, associated word mining should be used to extract relevant features. The data expressed by the mined features are not expressed as a string of data, but as a related word vector. To make up for its faults, content based filtering systems should use Bayesian classification, a system that classifies products by maintaining a knowledge base of related words. Also, to decrease the sparsity of the user- product matrix, the dimensions must be reduced. In order to reduce the dimensions of the columns, it is necessary to use Bayesian classification in tandem with the related-word knowledge base. Finally to reduce the dimensions of the rows the users must be classified into clusters.