TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering. (arXiv:2211.04266v1 [cs.IR])

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 …

arxiv collaborative collaborative filtering filtering forecasting series upgrade