The motivations of users to make interactions can be divided into static preference and dynamic interest. To accurately model user representations over time, recent studies in sequential recommendation utilize information propagation and evolution to mine from batches of arriving interactions. However, they ignore the fact that people are easily influenced by the recent actions of other users in the contextual scenario, and applying evolution across all historical interactions dilutes the importance of recent ones, thus failing to model the evolution of dynamic interest accurately. To address this issue, we propose a Context-Aware Pseudo-Multi-Task Recommender System (CPMR) to model the evolution in both historical and contextual scenarios by creating three representations for each user and item under different dynamics: static embedding, historical temporal states, and contextual temporal states. To dually improve the performance of temporal states evolution and incremental recommendation, we design a Pseudo-Multi-Task Learning (PMTL) paradigm by stacking the incremental single-target recommendations into one multi-target task for joint optimization. Within the PMTL paradigm, CPMR employs a shared-bottom network to conduct the evolution of temporal states across historical and contextual scenarios, as well as the fusion of them at the user-item level. In addition, CPMR incorporates one real tower for incremental predictions, and two pseudo towers dedicated to updating the respective temporal states based on new batches of interactions. Experimental results on four benchmark recommendation datasets show that CPMR consistently outperforms state-of-the-art baselines and achieves significant gains on three of them. The code is available at: https://github.com/DiMarzioBian/CPMR.

翻译：用户进行交互的动机可分为静态偏好和动态兴趣。为准确建模用户随时间变化的表征，近期序列推荐研究利用信息传播与演化从批次到达的交互中挖掘模式。然而，这些方法忽略了用户易受上下文场景中其他用户近期行为影响的事实，且对所有历史交互进行演化会稀释近期交互的重要性，导致无法准确建模动态兴趣的演化过程。为此，我们提出上下文感知伪多任务推荐系统（CPMR），通过为每个用户和物品在三种动态模式下创建静态嵌入、历史时序状态和上下文时序状态，来建模历史场景与上下文场景中的演化。为双重提升时序状态演化与增量推荐性能，我们设计了一种伪多任务学习（PMTL）范式，将多个增量单目标推荐任务堆叠为多目标联合优化任务。在该范式下，CPMR采用共享底层网络实现历史与上下文场景中时序状态的演化，并在用户-物品层级进行状态融合。此外，CPMR包含一个用于增量预测的真实任务塔，以及两个专用于基于新批次交互更新对应时序状态的伪任务塔。在四个基准推荐数据集上的实验表明，CPMR始终优于现有最优基线模型，并在其中三个数据集上取得显著提升。代码开源地址：https://github.com/DiMarzioBian/CPMR。