融合结构邻居和语义邻居的解耦图对比学习推荐模型
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国家重点研发计划(2018YFC0808500)


Disentangled Graph Contrastive Learning Recommendation Model Integrating Structural Neighbor and Semantic Neighbor
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    摘要:

    基于GCN的协同过滤模型在推荐领域取得了较好的效果, 但现有的图协同过滤学习方法通常不区分用户和项目的交互关系, 不易挖掘用户行为的潜在意图. 因此, 提出了一种融合结构邻居和语义邻居的解耦图对比学习推荐模型. 首先, 将用户和项目嵌入投影到独立空间进行意图解耦; 其次, 在图传播阶段, 依据用户和项目的意图特征挖掘其潜在语义邻居, 根据意图相似性对结构邻居和语义邻居进行解耦表征学习, 生成用户和项目的完整高阶表示. 在对比学习阶段, 对节点进行随机扰动并生成对比视图, 构建结构和语义的对比学习任务; 最后, 根据多任务策略, 对监督任务和对比学习任务进行联合优化. 在真实数据集Yelp2018和Amazon-Book上的实验表明, 提出的模型相比最优基准模型NCL在两个数据集上的Recall@20指标提高了7.54%、5.65%, NDCG@20指标提高了8.57%、6.28%.

    Abstract:

    The GCN-based collaborative filtering model achieves good performance in the recommendation field, but existing graph collaborative filtering learning methods usually do not distinguish the interaction relationship between users and items, which makes it difficult to mine the underlying intentions of user behavior. To address these issues, a decoupling graph contrastive learning recommendation model is proposed. Firstly, users and items are embedded into independent spaces to decouple their intentions. Secondly, during the graph propagation phase, potential semantic neighbors are discovered based on the intention features of users and items. The representation learning of structural and semantic neighbors is decoupled based on intent similarity, generating complete high-level representations for users and items. In the contrastive learning phase, nodes are randomly perturbed to create contrastive views, and contrastive learning tasks are constructed for both structural and semantic aspects. Finally, a multi-task strategy jointly optimizes the supervised task and the contrastive learning task. Experimental results on Yelp2018 and Amazon-Book datasets show that the proposed model outperforms the optimal baseline model NCL. It demonstrates improvements of 7.54% and 5.65% in Recall@20, and 8.57% and 6.28% in NDCG@20 on the two datasets, respectively.

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杨红伟,曹家晟,刘学军,邢卓雅.融合结构邻居和语义邻居的解耦图对比学习推荐模型.计算机系统应用,,():1-12

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  • 收稿日期:2024-02-01
  • 最后修改日期:2024-03-05
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  • 在线发布日期: 2024-06-05
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