融合项目属性偏好的矩阵分解推荐模型

doi:10.19665/j.issn1001-2400.2022.03.017

Abstract

Abstract:

In order to solve a series of problems such as sparse data,especially cold start,the traditional collaborative filtering algorithm can not accurately calculate the similarity between users and articles,so that it cannot accurately recommend corresponding articles for users.Combined with the advantages of the nearest neighbor based collaborative filtering algorithm and model-based collaborative filtering algorithm,this paper proposes a recommendation model based on matrix decomposition.The model uses model-based collaborative filtering,which is based on matrix decomposition and integrates other auxiliary information,in order to optimize the effect of matrix decomposition and make more accurate score prediction.Based on the traditional matrix decomposition algorithm,in the existing recommendation model,first,the similarity is calculated based on the user attribute and project attribute information,and the scoring matrix is constructed to predict the user's initial score.Then,the user interest matrix is constructed by integrating the user’s preferences for project attributes.At the same time,the user attribute information and project attribute information are integrated into the new matrix decomposition model to predict the score of cold start users.Compared with the traditional personalized recommendation model,the new model has a better recommendation accuracy.Through simulation experiments,it is also confirmed that the recommended model mentioned in this paper can alleviate the cold start problem to a certain extent and improve the accuracy.At the same time,good results have been achieved in the scalability of the model.

Key words: user cold start, matrix decomposition, user attributes, project attributes, user preference

CLC Number:

TP391

HAN Lifeng,CHEN Li,SHI Xiaolong. Matrix decomposition recommendation model incorporating item attribute preference[J].Journal of Xidian University, 2022, 49(3): 147-159.

Figures/Tables 12

References 28

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用户	项目1	…	项目j	…	项目n
用户1	R_1,1	…	R_1,_j	…	R_1,_n
?	?		?		?
用户i	R_i_,1	…	R_i_,_j	…	R_i_,_n
?	?		?		?
用户m	R_m_,1	…	R_m_,_j	…	R_m_,_n

用户ID	年龄	性别	职业
1	2	1	2
2	6	0	7
3	2	1	4
?	?	?	?
943	2	1	5

电影名	类型	年代	地区	评分
电影A	浪漫	80年代	美国	3
电影B	儿童、动画	90年代	美国	5
电影C	动画	80年代	英国	4
电影D	动作、悬疑	90年代	日本	4

数据集名称	用户数	电影数	评分数
MovieLens ML-100K	943	1 682	100 004
MovieLens ML-1M	6 040	3 883	1 000 000

Matrix decomposition recommendation model incorporating item attribute preference

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Figures/Tables 12

References 28

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