基于SVD++隐语义模型的信任网络推荐算法

陈佩武; 束方兴

doi:10.11959/issn.2096-0271.2021041

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基于SVD++隐语义模型的信任网络推荐算法

研究 | 更新时间：2024-06-03

- 基于SVD++隐语义模型的信任网络推荐算法
- A recommender algorithm based on SVD ++model under trust network
- 大数据 2021年7卷第4期页码：2021041
- 作者机构：
  
  1. 平安科技（深圳）有限公司，广东深圳 518031
  2. 北京大学互联网研究院（深圳），广东深圳 518055
- 作者简介：
  
  [ "陈佩武（1976-），男，平安科技（深圳）有限公司高级总监，深圳市金融智能机器人工程研究中心助理主任，主要研究方向为人工智能和大数据" ]
  [ "束方兴（1990-），男，北京大学互联网研究院（深圳）硕士生，主要研究方向为区块链和大数据" ]
- 基金信息：
- DOI：10.11959/issn.2096-0271.2021041
  中图分类号： TP315
- 网络首发：2021-07，
  
  纸质出版：2021-07-15
- 稿件说明：
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陈佩武, 束方兴. 基于SVD++隐语义模型的信任网络推荐算法[J]. 大数据, 2021,7(4):2021041.

Peiwu CHEN, Fangxing SHU. A recommender algorithm based on SVD ++model under trust network[J]. Big data research, 2021, 7(4): 2021041.
陈佩武, 束方兴. 基于SVD++隐语义模型的信任网络推荐算法[J]. 大数据, 2021,7(4):2021041. DOI： 10.11959/issn.2096-0271.2021041.

Peiwu CHEN, Fangxing SHU. A recommender algorithm based on SVD ++model under trust network[J]. Big data research, 2021, 7(4): 2021041. DOI： 10.11959/issn.2096-0271.2021041.

摘要

推荐算法通常基于用户的行为数据进行建模，然而显式行为数据的稀疏性可能会引起推荐算法的冷启动问题。为了降低数据稀疏和冷启动问题对推荐算法效果的影响，在已有显式信任关系的基础上，基于用户相似度引入隐式信任关系，通过SVD++隐语义模型设计了新的推荐算法。为了提升算法效果，进一步融合邻域模型，推导出算法评分预测式及损失函数。在Epinions开源数据集中将RMSE和MAE作为测试指标，在全体用户集和冷启动用户集上进行对比实验。实验结果显示，设计的推荐算法可以在一定程度上改善原推荐算法的冷启动问题，并取得更好的评分预测效果。

Abstract

Recommender algorithms are usually modeled based on user behavior data.However

the sparseness of explicit behavior data may cause the cold start problem of recommender algorithms.In order to solve the impact of data sparseness and cold-start problems on the effect of recommender algorithms

implicit trust relationship based on user similarity was introduced based on the existing revealed trust relationship

and a new recommender algorithm was designed through the SVD++ implicit semantic model.In order to improve the effect of the algorithm

the neighborhood model was integrated further

and the algorithm score prediction formula and loss function were derived.In the Epinions open source data set

RMSE and MAE were used as test indicators

and comparative experiments were conducted on the entire user set and the cold start user set.The experimental results show that the recommender algorithm can optimize the cold start problem of the original recommender algorithm to a certain extent

and achieve a better rating prediction accuracy.

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references

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