| 摘要: |
| 会话推荐旨在基于用户的一系列项目预测其交互的下一项目, 现有大多数会话推荐对于会话内项目间的时间间隔信息利用不够充分, 影响推荐准确性. 近年, 图神经网络凭借自身强大的复杂关系建模能力在会话推荐中受到推崇, 但仅基于图神经网络的会话推荐忽略了会话间的隐藏高阶关系, 信息不够丰富. 此外, 数据稀疏性一直是推荐系统中存在的现象, 研究中多使用对比学习对此实施改善, 然而大多对比学习框架形式单一, 泛化能力不强. 基于此, 提出一种结合自监督学习的会话推荐模型. 首先, 该模型利用用户会话内项目间的时间间隔信息对会话序列实施数据增强, 丰富会话内信息, 以提高推荐准确性; 其次, 构建超图卷积网络和Transformer编码器相结合的对偶视图, 从多视角捕捉会话间的隐藏高阶关系, 以丰富推荐多样性; 最后, 融合数据增强后的会话内信息、多视角下的会话间信息以及原始会话信息进行对比学习, 以增强模型泛化性. 通过与11个已有经典模型在4个数据集上的对比发现, 所提模型是可行高效的, 在HR与NDCG指标上分别平均提升5.96%、5.89%. |
| 关键词: 会话推荐 自监督学习 超图卷积网络 对偶视图 数据增强 |
| DOI：10.13328/j.cnki.jos.007404 |
| 分类号:TP181 |
| 基金项目:国家自然科学基金(62262025); 江西省自然科学基金重点项目(20224ACB202012); 赣鄱俊才支持计划-主要学科学术和技术带头人培养项目-领军人才(学术类) (20243BCE51024) |
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| Dual-view Self-supervised Session-based Recommendation Model Based on Temporal Interval Aware Data Augmentation |
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QIAN Zhong-Sheng, WAN Zi-Long, FAN Fu-Yu, FU Ting-Feng
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School of Computer and Artificial Intelligence, Jiangxi University of Finance and Economics, Nanchang 330013, China
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| Abstract: |
| Session-based recommendation aims to predict the next item a user will interact with based on a series of items. Most existing session-based recommender systems do not fully utilize the temporal interval information between items within a session, affecting the accuracy of recommendations. In recent years, graph neural networks have gained significant attention in session-based recommendation due to their strong ability to model complex relationships. However, session-based recommendations that rely solely on graph neural networks overlook the hidden high-order relationships between sessions, resulting in less rich information. In addition, data sparsity has always been a phenomenon in recommender systems, and contrastive learning is often employed to address this issue. However, most contrastive learning frameworks lack strong generalization capabilities due to their singular form. Based on this, a session-based recommendation model combined with self-supervised learning is proposed. First, the model utilizes the temporal interval information between items within user sessions to perform data augmentation, enriching the information within the sessions to improve recommendation accuracy. Second, a dual-view encoder is constructed, combining a hypergraph convolutional network encoder and a Transformer encoder to capture the hidden high-order relationships between sessions from multiple perspectives, thus enhancing the diversity of recommendations. Finally, the model integrates the augmented intra-session information, the multi-viewed inter-session information, and the original session information for contrastive learning to strengthen the model’s generalization ability. Comparisons with 11 existing classic models on 4 datasets show that the proposed model is feasible and efficient, with average improvements of 5.96% and 5.89% on HR and NDCG metrics, respectively. |
| Key words: session-based recommendation self-supervised learning hypergraph convolutional network dual-view data augmentation |
