让推荐系统学会识别物品时间序列，提升预测准确率
Heterogeneous Sequel-Aware Graph Neural Networks for Sequential Learning
- 构建异构时序感知图神经网络，融合用户偏好与物品序列关系
- 在六个数据集上表现优于或媲美Transformer、GNN等主流模型
- 适合做序列推荐的工程师和研究者参考
基于图的推荐系统利用高阶用户与物品嵌入进行下一步物品预测。动态引入邻居的协同信号有助于学习过程中捕捉相似用户的偏好。尽管物品间关联及其对推荐的影响已被广泛研究，但物品时间序列（即后续序列）在推荐中的作用仍鲜有探索。本文结合高阶用户嵌入与时序物品序列（即序列感知）嵌入，证明时序感知图神经网络在推荐性能上优于或至少可媲美不考虑序列信息的图推荐系统。我们在三个合成数据集和三个真实世界数据集上，对异构时序感知图神经网络（HSAL-GNN）与多种序列学习算法（如Transformer、GNN、自编码器）进行了广泛对比实验。结果表明，引入物品序列信息能显著提升推荐效果。
原文摘要 · Abstract (English)
Graph-based recommendation systems use higher-order user and item embeddings for next-item predictions. Dynamically adding collaborative signals from neighbors helps to use similar users' preferences during learning. While item-item correlations and their impact on recommendations have been studied, the efficacy of temporal item sequences for recommendations is much less explored. In this paper, we examine temporal item sequence (sequel-aware) embeddings along with higher-order user embeddings and show that sequel-aware Graph Neural Networks have better (or comparable) recommendation performance than graph-based recommendation systems that do not consider sequel information. Extensive empirical results comparing Heterogeneous Sequel-aware Graph Neural Networks (HSAL-GNNs) to other algorithms for sequential learning (such as transformers, graph neural networks, auto-encoders) are presented on three synthetic and three real-world datasets. Our results indicate that the incorporation of sequence information from items greatly enhances recommendations.
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