TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering
Seoyoung Hong, Minju Jo, Seungji Kook, Jaeeun Jung, Hyowon Wi, Noseong, Park, Sung-Bae Cho

TL;DR
TimeKit introduces a novel approach to enhance collaborative filtering recommender systems by forecasting user/item embeddings over time using neural controlled differential equations, leading to improved recommendation accuracy.
Contribution
It proposes a time-series forecasting upgrade kit that integrates neural controlled differential equations into collaborative filtering to better handle temporal dynamics.
Findings
Significantly improves existing collaborative filtering algorithms.
Effective in real-world benchmark datasets.
Enhances recommendation accuracy through temporal embedding forecasting.
Abstract
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…
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Taxonomy
TopicsRecommender Systems and Techniques · Traffic Prediction and Management Techniques · Energy Load and Power Forecasting
MethodsBalanced Selection
