Modern neural collaborative filtering techniques are critical to the success
of e-commerce, social media, and content-sharing platforms. However, despite
technical advances -- for every new application domain, we need to train an NCF
model from scratch. In contrast, pre-trained vision and language models are
routinely applied to diverse applications directly (zero-shot) or with limited
fine-tuning. Inspired by the impact of pre-trained models, we explore the
possibility of pre-trained recommender models that support building recommender
systems in new domains, with minimal or no retraining, without the use of any
auxiliary user or item information. Zero-shot recommendation without auxiliary
information is challenging because we cannot form associations between users
and items across datasets when there are no overlapping users or items. Our
fundamental insight is that the statistical characteristics of the user-item
interaction matrix are universally available across different domains and
datasets. Thus, we use the statistical characteristics of the user-item
interaction matrix to identify dataset-independent representations for users
and items. We show how to learn universal (i.e., supporting zero-shot
adaptation without user or item auxiliary information) representations for
nodes and edges from the bipartite user-item interaction graph. We learn
representations by exploiting the statistical properties of the interaction
data, including user and item marginals, and the size and density distributions
of their clusters.

通过利用用户 - 物品交互矩阵的统计特性，我们学习了用于支持无需用户或物品辅助信息的零 - shot 自适应（zero-shot recommendation）的通用表示，从而探索了预训练推荐模型在新领域构建推荐系统的可能性。

预训练神经推荐系统：一个可迁移的零样本框架

Pre-trained Neural Recommenders: A Transferable Zero-Shot Framework for  Recommendation Systems

Sequential recommendation demonstrates the capability to recommend items by
modeling the sequential behavior of users. Traditional methods typically treat
users as sequences of items, overlooking the collaborative relationships among
them. Graph-based methods incorporate collaborative information by utilizing
the user-item interaction graph. However, these methods sometimes face
challenges in terms of time complexity and computational efficiency. To address
these limitations, this paper presents AutoSeqRec, an incremental
recommendation model specifically designed for sequential recommendation tasks.
AutoSeqRec is based on autoencoders and consists of an encoder and three
decoders within the autoencoder architecture. These components consider both
the user-item interaction matrix and the rows and columns of the item
transition matrix. The reconstruction of the user-item interaction matrix
captures user long-term preferences through collaborative filtering. In
addition, the rows and columns of the item transition matrix represent the item
out-degree and in-degree hopping behavior, which allows for modeling the user's
short-term interests. When making incremental recommendations, only the input
matrices need to be updated, without the need to update parameters, which makes
AutoSeqRec very efficient. Comprehensive evaluations demonstrate that
AutoSeqRec outperforms existing methods in terms of accuracy, while showcasing
its robustness and efficiency.

AutoSeqRec 是一种用于顺序推荐任务的增量推荐模型。它基于自编码器，由编码器和三个解码器组成。通过重建用户 - 物品交互矩阵和物品转换矩阵的行列，捕捉用户的长期偏好和短期兴趣，最终实现了准确性、鲁棒性和高效性的提升。