Recommender Systems (RS) aim to provide personalized suggestions of items for users against consumer over-choice. Although extensive research has been conducted to address different aspects and challenges of RS, there still exists a gap between academic research and industrial applications. Specifically, most of the existing models still work in an offline manner, in which the recommender is trained on a large static training set and evaluated on a very restrictive testing set in a one-time process. RS will stay unchanged until the next batch retrain is performed. We frame such RS as Batch Update Recommender Systems (BURS). In reality, they have to face the challenges where RS are expected to be instantly updated with new data streaming in, and generate updated recommendations for current user activities based on the newly arrived data. We frame such RS as Incremental Update Recommender Systems (IURS). In this article, we offer a systematic survey of incremental update for neural recommender systems. We begin the survey by introducing key concepts and formulating the task of IURS. We then illustrate the challenges in IURS compared with traditional BURS. Afterwards, we detail the introduction of existing literature and evaluation issues. We conclude the survey by outlining some prominent open research issues in this area.

翻译：推荐系统旨在帮助用户应对信息过载，为其提供个性化的物品推荐。尽管已有大量研究关注推荐系统的不同方面和挑战，学术界研究与工业应用之间仍存在差距。具体而言，现有大多数模型仍采用离线方式工作——推荐器在大型静态训练集上训练，并在一次性评估流程中基于严格受限的测试集进行评测，直到下一次批量重训练之前，推荐系统保持不变。我们将此类系统称为批量更新推荐系统。现实中，推荐系统必须应对新数据流不断涌入的挑战，并基于新到达的数据为当前用户行为生成实时更新的推荐结果。我们将此类系统称为增量更新推荐系统。本文对神经推荐系统的增量更新进行了系统性综述。首先介绍关键概念并对增量更新推荐系统任务进行形式化定义；其次阐述增量更新推荐系统相较于传统批量更新推荐系统所面临的挑战；随后详细介绍现有文献及评估方法；最后总结该领域若干突出的开放性研究问题。