Neighborhood and Hypergraph Collaboration for Session-Based Recommendation

CHEN Rong-yuan; WEN Jie-bin; HUANG Shao-nian; HE Ye-yu

doi:10.12263/DZXB.20250144

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Neighborhood and Hypergraph Collaboration for Session-Based Recommendation

PAPERS | 更新时间：2025-12-27

- Neighborhood and Hypergraph Collaboration for Session-Based Recommendation
- ACTA ELECTRONICA SINICA Vol. 53, Issue 8, Pages: 2805-2817(2025)
- 作者机构：
  
  1.湖南工商大学前沿交叉学院，湖南长沙 410205
  2.湖南工商大学统计学习与智能计算湖南省重点实验室，湖南长沙 410205
  3.湖南工商大学计算机学院，湖南长沙 410205
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(41101425);Changsha Natural Science Foundation(kq2402096;kq2208056)
- DOI：10.12263/DZXB.20250144
  CLC： TP391.3;
- Received：26 February 2025，
  
  Accepted：09 August 2025，
  
  Published：25 August 2025
- 稿件说明：
移动端阅览
陈荣元, 文杰彬, 黄少年, 等. 基于邻域与超图协作的会话推荐[J]. 电子学报, 2025, 53(08): 2805-2817.

CHEN Rong-yuan, WEN Jie-bin, HUANG Shao-nian, et al. Neighborhood and Hypergraph Collaboration for Session-Based Recommendation[J]. Acta Electronica Sinica, 2025, 53(08): 2805-2817.
陈荣元, 文杰彬, 黄少年, 等. 基于邻域与超图协作的会话推荐[J]. 电子学报, 2025, 53(08): 2805-2817. DOI：10.12263/DZXB.20250144

CHEN Rong-yuan, WEN Jie-bin, HUANG Shao-nian, et al. Neighborhood and Hypergraph Collaboration for Session-Based Recommendation[J]. Acta Electronica Sinica, 2025, 53(08): 2805-2817. DOI：10.12263/DZXB.20250144

摘要

现有会话推荐模型长于提取用户当前偏好，但不善于捕捉用户兴趣随时间和情境的动态演变，难以从短时交互序列数据中提取项目之间的隐性关系.本文提出了一种基于邻域与超图协作学习会话推荐模型（Neighborhood and Hypergraph Collaboration for session-based Recommendation model，NHG-Rec），首先综合利用自适应多跳超图卷积和邻域卷积，以同时捕捉项目间的显性和隐性关系；然后利用基于上下文感知的位置动态注意力机制，来挖掘会话内各项目的重要程度，从而捕捉用户实时兴趣；再采用多视图会话嵌入，通过局部-全局对比学习策略，以期捕捉项目间的多维特征、辨别语义差异.实验结果表明：对于Tmall、Diginetica、Nowplaying这3个基准数据集，相比SR-GNN、GCE-GNN、DHCN等主流基准模型，该模型的P@10、P@20、MRR@10、MRR@20性能指标分别平均提升了12.38%、5.47%、6.53%、6.39%.NHG-Rec模型能够捕捉用户兴趣的动态变化和项目间的多维关系.

Abstract

Current session recommendation models excel at extracting users’ immediate preferences but struggle to capture the dynamic evolution of user interests over time and context

making it challenging to extract latent relationships between items from short-term interaction sequences. This paper proposes a neighborhood and hypergraph collaboration for session-based recommendation model (NHG-Rec)

which first comprehensively utilizes adaptive multi-hop hypergraph convolution and neighborhood convolution to simultaneously capture explicit and implicit relationships between items; then employs a context-aware dynamic positional attention mechanism to explore the importance of items within a session

thereby capturing users’ real-time interests; further adopts multi-view session embeddings through a local-global contrastive learning strategy to capture multi-dimensional item features and distinguish semantic differences. Experimental results demonstrate that for Tmall

Diginetica

and Nowplaying benchmark datasets

compared to mainstream baseline models such as SR-GNN

GCE-GNN

and DHCN

this model improves P@10

P@20

MRR@10

and MRR@20 performance metrics by an average of 12.38%

5.47%

6.53%

and 6.39%

respectively. The NHG-Rec model effectively captures the dynamic changes of user interests and multi-dimensional relationships between items.

关键词

Keywords

references

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