融合注意力与结构降噪的对比学习知识感知推荐

doi:10.3778/j.issn.1002-8331.2405-0231

摘要/Abstract

摘要： 现有基于知识图谱推荐方法利用知识图谱丰富项目表示，但是固定聚合邻居实体的策略无法动态调整邻居实体的重要性，而且用户历史行为数据往往是嘈杂的，影响了推荐系统的性能。针对这类问题，提出一种融合注意力机制与图结构降噪的对比学习推荐算法，在多视图对比学习框架下采用了一个度敏感边缘修剪方法对用户-项目交互图进行结构降噪，去掉可能含有无意交互噪声的边，并缓解图神经网络中度数高的节点学习特征时容易存在的过度平滑问题。在知识图谱聚合中引入可学习的图注意力机制来有效识别知识图谱中信息丰富的知识连接，动态调整不同实体的权重。在Last.FM和MovieLens-1M两个真实的公共数据集上与其他先进算法进行对比实验，结果表明该模型在AUC、F1和Recall@[K]评价指标上均优于其他先进模型。

Abstract: Existing knowledge graph-based recommendation methods use knowledge graphs to enrich item representations. However, the strategy of fixed aggregation of neighbor entities cannot dynamically adjust the importance of neighbor entities, and user historical behavior data is often noisy, affecting the performance of the recommendation system. To address such problems, a contrastive learning recommendation algorithm is proposed that integrates attention mechanism and graph structure denoising. Under the multi-view contrastive learning framework, a degree-sensitive edge pruning method is used to perform structural denoising on the user-item interaction graph, removing edges that may contain unintentional interaction noise, and alleviating the over-smoothing problem that is prone to occur when nodes with high degree in graph neural network learn features. A learnable graph attention mechanism is introduced in knowledge graph aggregation to effectively identify information-rich knowledge connections in the knowledge graph and dynamically adjust the weights of different entities. Through comparative experiments with other advanced algorithms on two real public data sets： Last.FM and MovieLens-1M, it is shown that the model is better than other advanced models in AUC, F1 and Recall@[K] indicators.

Key words: knowledge graph（KG）, recommendation system, contrastive learning, attention mechanism, graph structure denoising

任衍栋, 张东, 李冠宇. 融合注意力与结构降噪的对比学习知识感知推荐[J]. 计算机工程与应用, 2025, 61(17): 232-240.

REN Yandong, ZHANG Dong, LI Guanyu. Contrastive Learning with Integrated Attention and Structure Denoising in Knowledge-Aware Recommendation Algorithms[J]. Computer Engineering and Applications, 2025, 61(17): 232-240.

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