融合社交和标签信息的隐语义模型推荐算法

彭嘉恩; 邓秀勤; 刘太亨; 刘富春; 李文洲

doi:10.12052/gdutxb.170140

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融合社交和标签信息的隐语义模型推荐算法

综合研究 | 更新时间：2026-05-27

- 融合社交和标签信息的隐语义模型推荐算法
- A Recommendation Algorithm of Latent Factor Model Fused with the Social and Tag Information
- 广东工业大学学报 2018年35卷第4期页码：45-50
- 作者机构：
  
  1. 广东工业大学　应用数学学院,广东,广州,510520
  2. 广东工业大学　计算机学院,广东,广州,510006
- 作者简介：
  
  彭嘉恩(1993–)，女，硕士研究生，主要研究方向为数据挖掘、机器学习、推荐系统
  邓秀勤(1966–)，女，教授，主要研究方向为智能计算、数据挖掘等. E-mail：xiuqindeng@163.com
- 基金信息：
  
  国家自然科学基金资助项目(61673122,61273118);广东省公益研究与能力建设专项资金资助项目(2015A030402006);广东工业大学研究生创新创业及竞赛资助项目(2016YJSCX036，2017YJSCX039)
- DOI：10.12052/gdutxb.170140
  中图分类号： TP312
- 网络首发：2018-07-09，
  
  纸质出版：2018
- 稿件说明：
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彭嘉恩, 邓秀勤, 刘太亨, 刘富春, 李文洲. 融合社交和标签信息的隐语义模型推荐算法[J]. 广东工业大学学报, 2018, 35(4): 45-50.

Peng Jia-en, Deng Xiu-qin, Liu Tai-heng, et al. A Recommendation Algorithm of Latent Factor Model Fused with the Social and Tag Information[J]. 2018, 35(4): 45-50.
彭嘉恩, 邓秀勤, 刘太亨, 刘富春, 李文洲. 融合社交和标签信息的隐语义模型推荐算法[J]. 广东工业大学学报, 2018, 35(4): 45-50. DOI： 10.12052/gdutxb.170140.

Peng Jia-en, Deng Xiu-qin, Liu Tai-heng, et al. A Recommendation Algorithm of Latent Factor Model Fused with the Social and Tag Information[J]. 2018, 35(4): 45-50. DOI： 10.12052/gdutxb.170140.

摘要

为了提高隐语义模型在数据稀疏情况下推荐结果的质量

提出一种带有社交正则化项和标签正则化项的隐语义模型. 根据用户社交网络和物品标签的信息

设计出描述用户和物品概况的正则化项

并利用用户对物品的历史评分计算得到用户评分偏好

将这三项引入矩阵分解目标函数中

进一步约束目标函数

最后通过梯度下降法去优化模型参数

得到推荐结果. 为了验证算法的有效性

在Last.fm数据集上进行实验，实验结果表明

本文算法的推荐质量优于其他传统推荐算法.

Abstract

In order to improve the recommendation performance of latent factor model under the circumstance of data sparseness

a latent factor model with the social regularization and the tag regularization is proposed. According to the user’s social network and the item’s tag information

the regularization depicting the profiles of the user and the item is designed

and the user rating preferences calculated by using user’s history rating of items. These three items are introduced into the objective function of the matrix decomposition to further constrain the objective function. Finally

the gradient descent method is used to optimize the model parameters and get the recommendation result. To verify the efficacy of the proposed method

the model is tested by the Last.fm data set

and the experimental results show that the recommendation algorithm proposed in this study has a better recommendation performance compared with other traditional recommendation algorithms.

关键词

Keywords

references

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TAN X Q, CAI J, LUO L. The tag semantic retrieval of bibliography system based on the improved LSI [J]. Research on Library Science, 2014(11): 67-72.

CANTADOR I, BRUSILOVSKY P, KUFLIK T. Proceedings of the 2nd international workshop on information heterogeneity and fusion in recommender systems (HetRec 2011) : 27th October 2011, Chicago, IL, USA [J]. Soins Psychiatrie, 2011(232): 5-6.

LU L, WEI D Y. A collaborative filtering algorithm based on latent factor model [J]. Microelectronics Computer, 2015(2): 73-75.

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