顺序推荐通常利用深度神经网络来挖掘交互序列中的丰富信息。然而,现有方法往往面临交互数据不足的问题。为了缓解稀疏性问题,将自监督学习引入顺序推荐中。尽管有效,我们认为,当前基于自监督学习的(即,基于SSL的)序贯推荐模型具有以下局限性:(1)仅使用单一的自监督学习方法,对比自监督学习或生成自监督学习。(2)在图结构域或节点特征域中采用简单的数据增强策略。我们认为,他们没有充分利用这两种自监督方法的功能,也没有充分探索结合图增强方案的优势。因此,他们往往无法学习更好的项目表示。鉴于此,我们提出了一种新的多任务顺序推荐框架,称为自适应自监督学习顺序推荐(ASLRec)。具体来说,我们的框架自适应地结合了对比和生成自监督学习方法,同时在图拓扑和节点特征级别应用不同的扰动。这种方法构造了不同的增强图视图,并采用了多个损失函数(包括对比损失,生成损失,遮罩损失,和预测损失)用于联合训练。通过包含各种方法的功能,我们的模型在不同的增强图视图中学习项目表示,以实现更好的性能并有效地减轻交互噪声和稀疏性。此外,我们将一小部分随机均匀噪声添加到项目表示中,使项目表示更加统一,并减轻交互记录中固有的受欢迎程度偏差。我们对三个公开的基准数据集进行了广泛的实验,以评估我们的模型。结果表明,与其他14种竞争方法相比,我们的方法实现了最先进的性能:命中率(HR)提高了14.39%以上,归一化贴现累计收益(NDCG)增长超过18.67%。
Sequential recommendation typically utilizes deep neural networks to mine rich information in interaction sequences. However, existing methods often face the issue of insufficient interaction data. To alleviate the sparsity issue, self-supervised learning is introduced into sequential recommendation. Despite its effectiveness, we argue that current self-supervised learning-based (i.e., SSL-based) sequential recommendation models have the following limitations: (1) using only a single self-supervised learning method, either contrastive self-supervised learning or generative self-supervised learning. (2) employing a simple data augmentation strategy in either the graph structure domain or the node feature domain. We believe that they have not fully utilized the capabilities of both self-supervised methods and have not sufficiently explored the advantages of combining graph augmentation schemes. As a result, they often fail to learn better item representations. In light of this, we propose a novel multi-task sequential recommendation framework named Adaptive Self-supervised Learning for sequential Recommendation (ASLRec). Specifically, our framework combines contrastive and generative self-supervised learning methods adaptively, simultaneously applying different perturbations at both the graph topology and node feature levels. This approach constructs diverse augmented graph views and employs multiple loss functions (including contrastive loss, generative loss, mask loss, and prediction loss) for joint training. By encompassing the capabilities of various methods, our model learns item representations across different augmented graph views to achieve better performance and effectively mitigate interaction noise and sparsity. In addition, we add a small proportion of random uniform noise to item representations, making the item representations more uniform and mitigating the inherent popularity bias in interaction records. We conduct extensive experiments on three publicly available benchmark datasets to evaluate our model. The results demonstrate that our approach achieves state-of-the-art performance compared to 14 other competitive methods: the hit rate (HR) improved by over 14.39%, and the normalized discounted cumulative gain (NDCG) increased by over 18.67%.