基于动静态和关系特征全局捕获的社交推荐模型

doi:10.13229/j.cnki.jdxbgxb.20230535

Abstract

Abstract:

The social recommendation algorithm based on graph neural networks has achieved good performance in improving the performance of recommendation systems. However， existing methods overlook the dynamic evolution of users interest preferences and items attractiveness， as well as the potential connections between items. This can lead to the model learning features that are not accurate and rich enough， limiting prediction accuracy. For this problem， a social recommendation model based on the global capture of dynamic， static and relational features is proposed. The model first captures the long-term static features and short-term dynamic features of users and items through interaction modeling network and temporal modeling network， respectively. Then， the gated fusion network adaptively fuses the long-term static features and short-term dynamic features to obtain the dynamic and static features. Finally， the relationship aggregation network is used to capture the relational features. The experimental results on the Ciao and Epinions datasets show that the prediction error of the proposed model is significantly lower than that of the existing advanced methods， and it has good application value.

Key words: computer application, social recommendation, recommendation system, graph neural networks, dynamic and static features, relational features

CLC Number:

TP391

Xiao-dong CAI,Qing-song ZHOU,Yan-yan ZHANG,Yun XUE. Social recommendation based on global capture of dynamic， static and relational features[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(2): 700-708.

Figures/Tables 9

Fig.1

Fig.2

Fig.3

Table 1

Pseudocode of DSRSRec model"

输入：用户-项目评分矩阵 $R$ 、用户-用户信任矩阵 $F$

输出：目标用户对预测项目的评分

开始

1.基于 $R 、 F$ 分别生成 $G R 、 G S$

2.基于 $R$ 和 $N$ 经式（13）计算项目间的相似度生成 $G I$

3.基于高斯分布随机初始化各用户、项目、评分的嵌入向量

4.基于 $G R$ 和交互建模网络经式（1）~（5）生成用户和项目的长期静态特征

5.基于时序建模网络和 $K$ 经式（6）~（8）生成用户短期动态特征（项目同理）

6.基于门控融合网络经式（9）~（10）生成用户动静态特征（项目同理）

7.基于 $G S$ 和关系聚合网络经式（11）~（12）生成用户的关系特征（项目同理，但基于 $G I$ ）

8.基于用户和项目的动静态特征及关系特征经式（16）生成用户和项目全局特征

9.基于用户和项目的全局特征经式（17）计算出预测分数

10.基于式（18）计算损失值并利用梯度下降算法优化DSRSRec模型

结束

Table 1

Table 2

Statistical information of the datasets"

数据集	$\| U \|$	$\| V \|$	评分数	社交关系数
Ciao	2 062	14 439	35 990	52 721
Epinions	15 448	255 461	920 119	355 717

Table 2

Table 3

Fig.4

Fig.5

Fig.6

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