Recommendation Method Study Based on Users Page Interest Degree

摘要：

Mining users’ interest information and recommending corresponding products to them is a goal that many websites pursue. This paper focuses on the degree of interest in a page by the user. The proposed technique generates a user’s page interest degree matrix. It is assumed that this matrix will be a sparse matrix with null entries in a column. These null entries are updated with the average value of the column to create a not-sparse matrix against which a Singular Value Decomposition (SVD) is run. The Slope One Algorithm (SOA) is run against this generated matrix to improve predicting accuracy. Finally, encity.com website data was used to test the results by comparing the sparse generator matrix with the not-sparse generator matrix after SVD under Slope One. It was found that the latter is better than the former in predicting user’s page interest degree’s accuracy.

关键词： Singular Value Decomposition page interest degree collaborative filtering

作者: Liu Weijiang Jiang Hongjie Wang Ying

作者单位: Business School, Jilin University, China Management College, The Party School of the CPC Jilin Provincial Committee, China

会议类型: 国际会议

会议名称: The Ninth Wuhan International Conference on E-Business(第九届武汉电子商务国际会议)

会议地点: 武汉

会议语种:英文

页码: 283-289

在线出版日期: 2010-05-29（万方平台首次上网日期，不代表论文的发表时间）