基于用户兴趣感知的多关系推荐模型

doi:10.3778/j.issn.1002-8331.2206-0148

摘要/Abstract

摘要： 由于图卷积网络（GCN）能够利用高阶邻居的协作信号来更好地学习用户和项目的嵌入，它已经广泛应用于推荐系统。但在当前基于GCN的多关系推荐模型中用户节点的嵌入学习会受到与之兴趣不相似的高阶相邻用户的干扰，导致拥有不同兴趣的用户经过多层图卷积后会得到相似的嵌入，从而产生了过度平滑问题。因此针对上述问题提出了一个基于用户兴趣感知的多关系推荐模型（IMRRM）。该模型会在用户项目异构交互图中利用轻量化的图卷积网络得到每个用户的图形结构信息。子图生成模块利用用户的图结构信息和初始特征有效地识别出兴趣相似的用户，并将相似用户及其交互项目组成一个子图。通过在子图中进行深层嵌入学习来防止兴趣不相关的高阶邻居传播更多的负面信息从而得到更精确的用户嵌入。因此IMRRM模型减少了噪声信息对用户节点嵌入学习的影响，有效地缓解了过度平滑问题来更加准确地进行多关系推荐。通过在Beibei和Taobao这两个公共数据集上实验来验证IMRRM的有效性和鲁棒性。实验结果表明，IMRRM模型在HR10上分别提高了1.98%和1.49%，在NDCG10上分别提高了1.58%和1.81%，具有较好的性能。

关键词: 图卷积网络, 多关系推荐, 子图, 兴趣感知

Abstract: Because graph convolution network（GCN） can use the cooperation signals of high-order neighbors to learn the embedding of users and items better, it has been widely used in recommendation systems. However, in the current multi-relationship recommendation model based on GCN, the embedding learning of user nodes will be interfered by high-order adjacent users with dissimilar interests, so that users with different interests will get similar embedding after multi-layer graph convolution, resulting in the over-smoothing problem. Therefore, this paper proposes a multi-relationship recommendation model based on user interest-aware（IMRRM） because the above problem. First, the model uses a light graph convolutional network in the user-item heterogeneous interaction graph to obtain the graph structure information of each user. And then the subgraph generation module uses the user’s graph structure information and initial features to effectively identify users with similar interests, and group similar users and their interaction items into a subgraph. Finally, more accurate user embeddings are obtained by performing deep embedding learning in subgraphs to prevent unrelated high-order neighbors from propagating more negative information. Therefore, the IMRRM model reduces the influence of noise information on user embedding learning, effectively alleviates the over-smoothing problem, and makes multi-relationship recommendation more accurately. The effectiveness and robustness of IMRRM are verified by experiments on two public datasets, Beibei and Taobao. The experimental results show that the IMRRM model is improved by 1.98% and 1.49% on HR10, and 1.58% and 1.81% on NDCG10, respectively, with better performance.

Key words: graph convolutional network, multi-relational recommendation, subgraph, interest-aware

胡新荣, 邓杰文, 罗瑞奇, 刘军平, 朱强, 彭涛. 基于用户兴趣感知的多关系推荐模型[J]. 计算机工程与应用, 2023, 59(11): 231-240.

HU Xinrong, DENG Jiewen, LUO Ruiqi, LIU Junping, ZHU Qiang, PENG Tao. Multi-Relationship Recommendation Model Based on User Interest-Aware[J]. Computer Engineering and Applications, 2023, 59(11): 231-240.

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