In a practical recommender system, new interactions are continuously observed. Some interactions are expected, because they largely follow users' long-term preferences. Some other interactions are indications of recent trends in user preference changes or marketing positions of new items. Accordingly, the recommender needs to be periodically retrained or updated to capture the new trends, and yet not to forget the long-term preferences. In this paper, we propose a novel and generic retraining framework called Disentangled Incremental Learning (DIL) for graph-based recommenders. We assume that long-term preferences are well captured in the existing model, in the form of model parameters learned from past interactions. New preferences can be learned from the user-item bipartite graph constructed using the newly observed interactions. In DIL, we design an Information Extraction Module to extract historical preferences from the existing model. Then we blend the historical and new preferences in the form of node embeddings in the new graph, through a Disentanglement Module. The essence of the disentanglement module is to decorrelate the historical and new preferences so that both can be well captured, via carefully designed losses. Through experiments on three benchmark datasets, we show the effectiveness of DIL in capturing dynamics of useritem interactions. We also demonstrate the robustness of DIL by attaching it to two base models - LightGCN and NGCF.

翻译：在实际推荐系统中，新交互数据不断涌现。部分交互符合预期，因其基本遵循用户长期偏好；另一些交互则反映了用户偏好变化趋势或新商品的营销定位。因此，推荐系统需要定期重训练或更新以捕捉新趋势，同时避免遗忘长期偏好。本文提出一种新颖且通用的重训练框架——解耦增量学习（Disentangled Incremental Learning, DIL），适用于基于图的推荐系统。我们假设现有模型已通过历史交互参数充分捕捉长期偏好，而新偏好可从新观测交互构建的用户-物品二分图中学得。在DIL中，我们设计信息提取模块从现有模型中提取历史偏好，再通过解耦模块以节点嵌入形式将历史偏好与新偏好融合至新图中。该解耦模块的核心在于通过精心设计的损失函数去相关历史偏好与新偏好，使二者均能被有效捕捉。基于三个基准数据集的实验表明，DIL能有效捕获用户-物品交互的动态变化。同时，通过将其附加至LightGCN和NGCF两种基模型，我们验证了DIL的鲁棒性。