基于表示增强的RippleNet模型改进研究

doi:10.11896/jsjkx.240800142

摘要/Abstract

摘要： 随着互联网信息量的激增,推荐系统在解决信息过载问题中扮演着关键角色。针对现有推荐系统模型在实体和关系表示中的不足,提出了一种基于表示增强的RippleNet模型(Representation Enhanced RippleNet,RE-RippleNet)。一方面,传统模型在实体表示中忽略了关系的语义信息,通过将邻居实体和关系聚合到实体的嵌入表示中,提升实体嵌入的表达能力和用户表征的精确度。另一方面,在用户偏好传播的多跳波纹集聚合时,引入长短期记忆网络(Long Short-Term Memory,LSTM)来捕捉不同跳数中用户偏好表示的不同影响力和特征,以实现更深层次的用户偏好挖掘和更精准的推荐。在MovieLens-1M和Book-Crossing两个公共数据集上的点击率预测实验结果表明,相比基线模型RippleNet,RE-RippleNet在准确率(ACC)和AUC指标上均取得显著提升。其中,在MovieLens-1M数据集上的ACC和AUC分别提高了1.7%和1.2%,在Book-Crossing数据集上分别提高了3.6%和1.6%,结果验证了模型在提升推荐系统性能方面的有效性。

Abstract: As the volume of internet information grows exponentially,recommender systems play a crucial role in addressing information overload.In response to the deficiencies in entity and relation representations within existing recommendation systems,this paper proposes an enhanced model termed representation enhanced ripplenet(RE-RippleNet).On one hand,traditional models tend to overlook the semantic information inherent in relationships.By aggregating neighboring entities and relationships into the embedded representation of entities,the expressive power of entity embedding and the accuracy of user representation are improved.On the other hand,during the aggregation process of multi-hop ripple sets for propagating user preferences,a long short-term memory(LSTM) network is employed to capture the diverse influences and characteristics of user preference representations across different hops,facilitating a deeper exploration of user preferences and more precise recommendations.Click-through rate prediction experiments on two public datasets,MovieLens-1M and Book-Crossing,demonstrate that RE-RippleNet achieves significant improvements in accuracy(ACC) and AUC metrics,compared to the baseline RippleNet model.Specifically,ACC and AUC increases by 1.7% and 1.2% respectively on the MovieLens-1M dataset,and by 3.6% and 1.6% on the Book-Crossing dataset,validating the model’s effectiveness in enhancing recommender system performance.

Key words: Recommender systems, Knowledge graph, RippleNet, Representation enhancement, Long and short-term memory

中图分类号:

TP301

李鹏彦, 王宝会, 叶子豪. 基于表示增强的RippleNet模型改进研究[J]. 计算机科学, 2025, 52(6A): 240800142-9. https://doi.org/10.11896/jsjkx.240800142

LI Pengyan, WANG Baohui, YE Zihao. Study on Improvements of RippleNet Model Based on Representation Enhancement[J]. Computer Science, 2025, 52(6A): 240800142-9. https://doi.org/10.11896/jsjkx.240800142

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