no code implementations • 27 Dec 2023 • Hyowon Wi, Yehjin Shin, Noseong Park
However, it has been overlooked for a long time to design an imputation method based on continuous-time recurrent neural networks (RNNs), i. e., neural controlled differential equations (NCDEs).
no code implementations • 27 Dec 2023 • Jeongwhan Choi, Hyowon Wi, Chaejeong Lee, Sung-Bae Cho, Dongha Lee, Noseong Park
Contrastive learning (CL) has emerged as a promising technique for improving recommender systems, addressing the challenge of data sparsity by leveraging self-supervised signals from raw data.
2 code implementations • 16 Dec 2023 • Yehjin Shin, Jeongwhan Choi, Hyowon Wi, Noseong Park
In the SR domain, we, for the first time, show that the same problem occurs.
Ranked #1 on Sequential Recommendation on MovieLens 1M
no code implementations • 12 Dec 2023 • Jayoung Kim, Yehjin Shin, Jeongwhan Choi, Hyowon Wi, Noseong Park
Structured data, which constitutes a significant portion of existing data types, has been a long-standing research topic in the field of machine learning.
no code implementations • 7 Dec 2023 • Jeongwhan Choi, Hyowon Wi, Jayoung Kim, Yehjin Shin, Kookjin Lee, Nathaniel Trask, Noseong Park
Transformers, renowned for their self-attention mechanism, have achieved state-of-the-art performance across various tasks in natural language processing, computer vision, time-series modeling, etc.
no code implementations • 8 Nov 2023 • Seonkyu Lim, Jaehyeon Park, Seojin Kim, Hyowon Wi, Haksoo Lim, Jinsung Jeon, Jeongwhan Choi, Noseong Park
Long-term time series forecasting (LTSF) is a challenging task that has been investigated in various domains such as finance investment, health care, traffic, and weather forecasting.
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).