With the outbreak of today's streaming data, the sequential recommendation is a promising solution to achieve time-aware personalized modeling. It aims to infer the next interacted item of a given user based on the historical item sequence. Some recent works tend to improve the sequential recommendation via random masking on the historical item so as to generate self-supervised signals. But such approaches will indeed result in sparser item sequence and unreliable signals. Besides, the existing sequential recommendation models are only user-centric, i.e., based on the historical items by chronological order to predict the probability of candidate items, which ignores whether the items from a provider can be successfully recommended. Such user-centric recommendation will make it impossible for the provider to expose their new items, failing to consider the accordant interactions between user and item dimensions. In this paper, we propose a novel Dual Contrastive Network (DCN) to achieve mutual harmony between user and item provider, generating ground-truth self-supervised signals for sequential recommendation by auxiliary user-sequence from an item-centric dimension. Specifically, we propose dual representation contrastive learning to refine the representation learning by minimizing the Euclidean distance between the representations of a given user/item and historical items/users of them. Before the second contrastive learning module, we perform the next user prediction to capture the trends of items preferred by certain types of users and provide personalized exploration opportunities for item providers. Finally, we further propose dual interest contrastive learning to self-supervise the dynamic interest from the next item/user prediction and static interest of matching probability. Experiments on four benchmark datasets verify the effectiveness of our proposed method.

翻译：随着流式数据的爆发，序列推荐是实现时间感知个性化建模的一种有前景的解决方案，旨在基于历史物品序列推断给定用户的下一个交互物品。近期一些研究倾向于通过对历史物品进行随机掩码来生成自监督信号以改进序列推荐，但这类方法会导致物品序列更加稀疏，并产生不可靠的信号。此外，现有序列推荐模型仅以用户为中心，即基于按时间顺序排列的历史物品来预测候选物品的概率，忽略了来自提供商的物品能否被成功推荐。这种以用户为中心的推荐会使提供商无法曝光其新物品，未能考虑用户与物品维度之间的协调交互。本文提出一种新颖的双重对比网络（Dual Contrastive Network, DCN），以实现用户与物品提供商之间的互惠和谐，通过从物品中心维度引入辅助用户序列，为序列推荐生成真实的自监督信号。具体而言，我们提出双重表示对比学习，通过最小化给定用户/物品与其历史物品/用户表示之间的欧氏距离来优化表示学习。在第二个对比学习模块之前，我们执行下一用户预测以捕获特定类型用户偏好物品的趋势，并为物品提供商提供个性化探索机会。最后，我们进一步提出双重兴趣对比学习，以自监督方式学习来自下一物品/用户预测的动态兴趣与匹配概率的静态兴趣。在四个基准数据集上的实验验证了所提方法的有效性。