融合掩码自编码器的自适应增强序列推荐

Fusion of Masked Autoencoder forAdaptive Augmentation Sequential Recommendation

为了解决序列推荐任务中基于对比学习方法生成的对比视图质量不佳的问题,提出融合掩码自编码器的自适应增强序列推荐模型。基于所有用户交互序列创建全局的项目-项目转换图,将序列模式与全局协作模式相结合,为项目表示提供更多的全局上下文;设计自适应图增强模块,基于自适应采样策略提取重要的自监督信号,学习更准确的项目表示,有效避免噪声信号的干扰;基于掩码的自编码器模块利用重掩码技术对高语义相关性的掩码项目进行二次掩码,使编码器学习更高级的项目表示,实现对掩码项目的合理重构;序列推荐器模块将位置信息、全局上下文与用户个性化交互序列相融合得到最终的项目表示,并根据该表示预测用户未来可能的交互项目,从而为用户提供更可靠推荐结果。在Books、Toys以及Retailrocket数据集上的实验结果表明,该模型的推荐准确性在命中率(HR)和归一化折损累计增益(NDCG)指标上均优于最先进的基线算法,例如在HR@5指标上,比最先进的基线提升4.59%,在NDCG@5指标上,比最先进的基线提升8.89%。

In order to address the issue of poor-quality contrast views generated by contrastive learning methods in sequential recommendation tasks,a model called GATSR,which is based on graph attention networks for sequential recommendation,is proposed.Firstly,a global item-item transition graph is created based on all user interaction sequences,combining sequential patterns with global collaborative patterns to provide global context for the item representation.Then,an adaptive graph augmentation module is designed to extract important self-supervised signals based on an adaptive sampling strategy,learning more accurate item representations and effectively avoiding the interference of noise signals.Subsequently,the masked autoencoder module employs re-masking technology to mask to highly semantically related masked items again,enabling the encoder to learn higher-level item representations and achieving the reasonable reconstruction of masked items.Finally,the sequential recommender module integrates position information,global context,and the personalized user interaction sequence to obtain the final item representation and predict the users future possible interaction items based on the representation,thereby providing more reliable recommendation results for users.Experimental results on the Books,Toys,and Retailrocket datasets show that the recommendation accuracy of the proposed model is superior to the most advanced baseline algorithms in terms of hit ratio(HR)and normalized discounted cumulative gain(NDCG)metrics.For example,it improves by 4.59%on the HR@5 metric and 8.89%on the NDCG@5 metric compared with the most advanced baseline.