Recommender systems are a long-standing research problem in data mining and
machine learning. They are incremental in nature, as new user-item interaction
logs arrive. In real-world applications, we need to periodically train a
collaborative filtering algorithm to extract user/item embedding vectors and
therefore, a time-series of embedding vectors can be naturally defined. We
present a time-series forecasting-based upgrade kit (TimeKit), which works in
the following way: it i) first decides a base collaborative filtering
algorithm, ii) extracts user/item embedding vectors with the base algorithm
from user-item interaction logs incrementally, e.g., every month, iii) trains
our time-series forecasting model with the extracted time-series of embedding
vectors, and then iv) forecasts the future embedding vectors and recommend with
their dot-product scores owing to a recent breakthrough in processing
complicated time-series data, i.e., neural controlled differential equations
(NCDEs). Our experiments with four real-world benchmark datasets show that the
proposed time-series forecasting-based upgrade kit can significantly enhance
existing popular collaborative filtering algorithms.

该研究提出了一个基于时间序列预测的升级工具包 (TimeKit)，能够显著增强现有流行的协同过滤算法，通过每月提取用户 / 项目嵌入向量，并使用时间序列嵌入向量训练预测模型，然后根据嵌入向量的点积得分推荐。