加入用户对项目属性偏好的奇异值分解推荐算法

魏港明; 刘真; 李林峰; 张猛

doi:10.7652/xjtuxb201805015

您当前的位置：

首页 >

文章列表页 >

加入用户对项目属性偏好的奇异值分解推荐算法

更新时间：2025-07-09

- 加入用户对项目属性偏好的奇异值分解推荐算法
- Singular Value Decomposition Recommendation Algorithm Considering User's Preference for Item Attributes
- 西安交通大学学报 2018年52卷第5期页码：101-107
- 作者机构：
  
  北京交通大学计算机与信息技术学院,北京,100044
- 作者简介：
- 基金信息：
  
  : 2017-08-15。作者简介: 魏港明(1992—),男,硕士生;刘真(通信作者),女,副教授,硕士生导师。基金项目: 国家重点研发计划资助项目(2016YFB1200100);中央高校基本科研业务费专项资助项目(2017JBM024)
- DOI：10.7652/xjtuxb201805015
  中图分类号： TP391.9
- 网络首发：2018-05-10，
  
  纸质出版：2018
- 稿件说明：
移动端阅览
魏港明, 刘真, 李林峰, 等. 加入用户对项目属性偏好的奇异值分解推荐算法[J]. 西安交通大学学报, 2018,52(5):101-107.

Singular Value Decomposition Recommendation Algorithm Considering User's Preference for Item Attributes[J]. 2018, 52(5): 101-107.
魏港明, 刘真, 李林峰, 等. 加入用户对项目属性偏好的奇异值分解推荐算法[J]. 西安交通大学学报, 2018,52(5):101-107. DOI： 10.7652/xjtuxb201805015.

Singular Value Decomposition Recommendation Algorithm Considering User's Preference for Item Attributes[J]. 2018, 52(5): 101-107. DOI： 10.7652/xjtuxb201805015.

摘要

由于目前的矩阵分解推荐算法在解决项目冷启动问题时

没有充分利用项目的属性偏好信息与用户评分行为的交互信息

因此提出了加入用户对项目属性偏好的奇异值分解推荐(UC-SVD)算法。该算法综合考虑项目属性和用户对项目的评分

不仅在矩阵分解算法中加入了项目的属性信息

同时通过对评分数据集和属性数据集的综合分析

得出用户对项目属性的偏好矩阵

将项目属性特征因子和用户对项目属性的偏好特征因子一并加入到矩阵分解中。在数据集Movielens、HetRec2011上进行实验

结果表明

与经典矩阵分解协同过滤算法相比

所提算法不仅在一定程度上解决了项目的冷启动问题

而且在同等条件下的均方根误差平均降低了3.5%

平均绝对误差平均降低了3%

尤其是在更为稀疏的HetRec2011数据集上

项目属性对用户评分行为的影响更加明显

改进算法在推荐精度上表现出更大的优越性。

Abstract

The traditional recommendation algorithm based on matrix factorization is facing the problem that in cold start of items

the existing recommendation algorithm does not make full use of the interactive information about item attributes and user's rating behavior

therefore a singular value decomposition recommendation algorithm is proposed(UC-SVD)considering the item attributes and user's rating for items. It adds the item attribute information into the decomposition matrix

and through a comprehensive analysis on the rating data set and attribute data set to get the user preference matrix and item attribute characteristic factor

it adds the user preference characteristic factor to the matrix decomposition. Experimental results on the datasets of Movielens and HetRec2011 show that in comparison with the classic matrix factorization collaborative filtering algorithm

the proposed algorithm can not only solve the problem in cold start of items to some extent

but also under the same condition reduce the root mean square error and the mean absolute error by 3% and 4%

respectively. Especially on the more sparse HetRec2011 dataset

the impact of item attributes on user's rating behavior is more obvious

and this improved algorithm shows greater superiority in recommendation accuracy.

关键词

Keywords

references

赵长伟, 彭勤科, 张志勇. 混合因子矩阵分解推荐算法 [J]. 西安交通大学学报, 2016, 50(12): 87-91.

ZHAO Changwei, PENG Qinke, ZHANG Zhiyong. A matrix factorization algorithm with hybrid implicit and explicit attributes for recommender systems [J]. Journal of Xi'an Jiaotong University, 2016, 50(12): 87-91.

PAN T, LIU Q, CHANG L I U. Ratings distribution recommendation model-based collaborative filtering recommendation algorithm [C]∥ The 2nd International Conference on Software, Multimedia and Communication Engineering. [s.n.]: SMCE, 2007: 378-380.

燕彩蓉, 张青龙, 赵雪, 等. 基于广义高斯分布的贝叶斯概率矩阵分解方法 [J]. 计算机研究与发展, 2016, 52(12): 2793-2800.

YAN Cairong, ZHANG Qinglong, ZHAO Xue, et al. A method of Bayesian probabilistic matrix factorization based on generalized Gaussian distribution [J]. Journal of Computer Research and Development, 2016, 52(12): 2793-2800.

吴金龙. Netflix Prize中的协同过滤算法 [D]. 北京: 北京大学, 2010: 25-34.

BAO Y, FANG H, ZHANG J. TopicMF: simultaneously exploiting ratings and reviews for recommendation [C]∥Proceedings of the 28th AAAI Conference on Artificial Intelligence. New York, USA: ACM, 2014: 2-8.

KOREN Y. Factor in the neighbors: Scalable and accurate collaborative filtering [J]. ACM Transactions on Knowledge Discovery from Data, 2010, 4(1): 1-24.

GUO G, ZHANG J, YORKE-SMITH N. TrustSVD: Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings [C]∥Proceedings of the 29th AAAI Conference on Artificial Intelligence. New York, USA: ACM, 2015: 123-129.

JAMALI M, ESTER M. A matrix factorization technique with trust propagation for recommendation in social networks [C]∥ACM Conference on Recommender Systems. New York, USA: ACM, 2010: 135-142.

MANZATO M G. gSVD++: supporting implicit feedback on recommender systems with metadata awareness [C]∥ACM Symposium on Applied Computing. New York, USA: ACM, 2013: 908-913.

MANZATO M G. Discovering latent factors from movies genres for enhanced recommendation [C]∥Proceedings of the 6th ACM Conference on Recommender Systems. New York, USA: ACM, 2012: 249-252.

QIN J, CAO L, PENG H. Collaborative filtering recommendation algorithm based on weighted item category [C]∥Control and Decision Conference. Piscataway, NJ, USA: IEEE, 2016: 2782-2786.

YU Y, WANG C, WANG H, et al. Attributes coupling based matrix factorization for item recommendation [J]. Applied Intelligence, 2016, 46(3): 1-13.

项亮. 推荐系统实践 [M]. 北京: 人民邮电出版社, 2012: 186-195.

HARPER F M, KONSTAN J A. The MovieLens datasets: history and context [J]. ACM Transactions on Interactive Intelligent Systems, 2016, 5(4): 19.

CARAGEA C, SILVESCU A, MITRA P, et al. Can't see the forest for the trees?: a citation recommendation system [C]∥Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries. New York, USA: ACM, 2013: 111-114.

WINLAW M, HYNES M B, CATERINI A, et al. Algorithmic acceleration of parallel ALS for collaborative filtering: speeding up distributed big data recommendation in spark [C]∥21st IEEE International Conference on Parallel and Distributed Systems. Piscataway, NJ, USA: IEEE, 2015: 682-691.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

混合因子矩阵分解推荐算法

一种采用矩阵分解的正交4抽头多小波无损压缩算法

椭球状态定界的数值稳定算法

一种利用多群组智慧的协同推荐算法

采用小波包树能量矩阵奇异值分解的局部放电模式识别