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：用户产生交互的动机可划分为静态偏好与动态兴趣。为精准建模随时间演变的用户表征，近期序列推荐研究利用信息传播与演化技术，从批量到达的交互数据中进行挖掘。然而，现有方法忽视了用户在情境场景中易受其他用户近期行为影响这一事实，且对所有历史交互进行演化会稀释近期交互的重要性，导致动态兴趣的演化建模失真。针对该问题，本文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构建三种不同动态下的表征——静态嵌入、历史时态状态与情境时态状态，分别在历史场景与情境场景中建模演化过程。为双重提升时态状态演化与增量推荐的性能，我们设计伪多任务学习（PMTL）范式，将多个增量单目标推荐任务堆叠为多目标任务进行联合优化。在PMTL框架下，CPMR采用共享底层网络，在历史与情境场景间实施时态状态演化，并在用户-物品层面实现两者的融合。此外，CPMR包含一个用于增量预测的真实任务塔，以及两个专门基于新批次交互更新对应时态状态的伪任务塔。在四个基准推荐数据集上的实验结果表明，CPMR持续优于现有最优基线模型，并在其中三个数据集上取得显著性能提升。代码开源地址：https://github.com/DiMarzioBian/CPMR。