A Recommendation Algorithm of Latent Factor Model Fused with the Social and Tag Information

Peng Jia-en; Deng Xiu-qin; Liu Tai-heng; Liu Fu-chun; Li Wen-zhou

doi:10.12052/gdutxb.170140

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A Recommendation Algorithm of Latent Factor Model Fused with the Social and Tag Information

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- A Recommendation Algorithm of Latent Factor Model Fused with the Social and Tag Information
- Vol. 35, Issue 4, Pages: 45-50(2018)
- 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：
- 基金信息：
- DOI：10.12052/gdutxb.170140
  CLC： TP312
- Online First：09 July 2018，
  
  Published：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

MA T, ZHOU J, TANG M, et al . Social network and tag sources based augmenting collaborative recommender system [J ] . Ieice Transactions on Information Systems, 2015, 98(4): 902-910.

SHAMBOUR Q, LU J. An effective recommender system by unifying user and item trust information for B2B applications [J]. Journal of Computer and System Science, 2015, 81(7): 1110-1126.

ZHENG N, LI Q. A recommender system based on tag and time information for social tagging systems [J]. Expert Systems with Applications, 2011, 38(4): 4575-4587.

MA H, ZHOU D, LIU C, et al. Recommender systems with social regularization[C]//Forth International Conference on Web Search and Web Data Mining. Hong Kong: ACM, 2011: 287-296.

LIU X, ABERER K. SoCo: A social network aided context-aware recommender system[C]//International Conference on World Wide Web. Rio de Janeiro: ACM, 2013: 781-802.

MA H, KING I, LYU M R. Learning to recommend with social trust ensemble[C]//International ACM SIGIR Conference on Research and Development in Information Retrieval. Boston: ACM, 2009: 203-210.

MA H, KING I, LYU M R. Learning to recommend with explicit and implicit social relations [J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): 1-19.

QIAN F, ZHAO S, TANG J, et al . SoRS: social recommendation using global rating reputation and local rating similarity [J ] . Physica A Statistical Mechanics Its Applications, 2016, 461(10): 61-72.

KIM K R, MOON N M. Recommender system design using movie genre similarity and preferred genres in Smart Phone [J]. Multimedia Tools and Applications, 2012, 61(1): 87-104.

CHOI S M, KO S K, HAN Y S. A movie recommendation algorithm based on genre correlations [J]. Expert Systems with Applications, 2012, 39(9): 8079-8085.

MA T, SUO X, ZHOU J, et al . Augmenting matrix factorization technique with the combination of tags and genres [J ] . Physica A: Statistical Mechanics Its Applications, 2016, 461( 9): 101-116.

KOREN Y, BELL R, VOLINSKY C. Matrix factorization techniques for recommender systems [J]. Computer, 2009, 42(8): 30-37.

SALAKHUTDINOV R, MNIH A. Probabilistic matrix factorization[C]// International Conference on Neural Information Processing Systems. Vancouver, British Columbia, Canada: Curran Associates Inc. 2007: 1257-1264.

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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