Joint recommendation algorithm based on tensor completion and user preference

Zhi XIONG; Kai XU; Lingru CAI; Weihong CAI

doi:10.11959/j.issn.1000-436x.2019231

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Joint recommendation algorithm based on tensor completion and user preference

Correspondences | 更新时间：2024-06-05

- Joint recommendation algorithm based on tensor completion and user preference
- Journal on Communications Vol. 40, Issue 12, Pages: 155-166(2019)
- 作者机构：
  
  1. 汕头大学计算机科学与技术系，广东汕头 515063
  2. 汕头大学智能制造技术教育部重点实验室，广东汕头 515063
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61202366);Science and Technology Planning Project of Guangdong Province(2018A030313438);Science and Technology Planning Project of Guangdong Province(2018A030313889);Science and Technology Planning Project of Guangdong Province(2019B010116001)
- DOI：10.11959/j.issn.1000-436x.2019231
  CLC： TP391.3
- Online First：2019-12，
  
  Published：25 December 2019
- 稿件说明：
移动端阅览
Zhi XIONG, Kai XU, Lingru CAI, et al. Joint recommendation algorithm based on tensor completion and user preference[J]. Journal on Communications, 2019, 40(12): 155-166.
DOI：

Zhi XIONG, Kai XU, Lingru CAI, et al. Joint recommendation algorithm based on tensor completion and user preference[J]. Journal on Communications, 2019, 40(12): 155-166. DOI： 10.11959/j.issn.1000-436x.2019231.

摘要

针对现有推荐算法缺乏对用户偏好的考虑，推荐效果不理想的问题，提出了一种联合张量填补和用户偏好的推荐算法。首先，基于评分矩阵和项目所属类别矩阵构建用户–项目–类别的三维张量；然后，利用Frank-Wolfe算法进行迭代计算，填补缺失数据，同时基于张量数据构建用户类别偏好矩阵和评分偏好矩阵；最后，基于填补后的张量以及2个偏好矩阵设计联合推荐算法，并采用差分进化算法进行参数调优。实验结果表明，与一些常用算法和新近提出的算法相比，所提算法的推荐效果优于对比算法，其精度平均提升了1.96%~3.44%，召回率平均提升了1.35%~2.40%。

Abstract

Aiming at the problem that existing recommendation algorithms have little regard for user preference

and the recommendation result is not satisfactory

a joint recommendation algorithm based on tensor completion and user preference was proposed.First

a user-item-category 3-dimensional tensor was built based on user-item scoring matrix and item-category matrix.Then

the Frank-Wolfe algorithm was used for iterative calculation to fill in the missing data of the tensor.At the same time

a user category preference matrix and a scoring preference matrix were built based on the 3-dimensional tensor.Finally

a joint recommendation algorithm was designed based on the completed tensor and the two preference matrices

and the differential evolution algorithm was used for parameter tuning.The experimental results show that compared with some typical and newly proposed recommendation algorithms

the proposed algorithm is superior to the compare algorithms

the precision is improved by 1.96% ~ 3.44% on average

and the recall rate is improved by 1.35%~2.40% on average.

关键词

Keywords

references

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