TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering

no code implementations • 8 Nov 2022 • Seoyoung Hong, Minju Jo, Seungji Kook, Jaeeun Jung, Hyowon Wi, Noseong Park, Sung-Bae Cho

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).

Collaborative Filtering Recommendation Systems +2