transfer learning for collaborative filtering using a psychometrics model

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ID: 163180
2016
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Abstract
In a real e-commerce website, usually only a small number of users will give ratings to the items they purchased, and this can lead to the very sparse user-item rating data. The data sparsity issue will greatly limit the recommendation performance of most recommendation algorithms. However, a user may register accounts in many e-commerce websites. If such users’ historical purchasing data on these websites can be integrated, the recommendation performance could be improved. But it is difficult to align the users and items between these websites, and thus how to effectively borrow the users’ rating data of one website (source domain) to help improve the recommendation performance of another website (target domain) is very challenging. To this end, this paper extended the traditional one-dimensional psychometrics model to multidimension. The extended model can effectively capture users’ multiple interests. Based on this multidimensional psychometrics model, we further propose a novel transfer learning algorithm. It can effectively transfer users’ rating preferences from the source domain to the target domain. Experimental results show that the proposed method can significantly improve the recommendation performance.
Reference Key
zhang2016mathematicaltransfer Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Haijun Zhang;Bo Zhang;Zhoujun Li;Guicheng Shen;Liping Tian
Journal journal of power sources
Year 2016
DOI
10.1155/2016/8063962
URL
http://dx.doi.org/10.1155/2016/8063962
Keywords
civil engineering (general)mathematics engineering (general)

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