no code implementations • 1 May 2024 • Chaejeong Lee, Jeongwhan Choi, Hyowon Wi, Sung-Bae Cho, Noseong Park
In this paper, we propose a novel Stochastic sampling for i) COntrastive views and ii) hard NEgative samples (SCONE) to overcome these issues.
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.
1 code implementation • 25 Nov 2022 • Jeongwhan Choi, Seoyoung Hong, Noseong Park, Sung-Bae Cho
In particular, diffusion equations have been widely used for designing the core processing layer of GNNs, and therefore they are inevitably vulnerable to the notorious oversmoothing problem.
1 code implementation • 17 Nov 2022 • Jeongwhan Choi, Seoyoung Hong, Noseong Park, Sung-Bae Cho
Various methods have been proposed for collaborative filtering, ranging from matrix factorization to graph convolutional methods.
Ranked #1 on Collaborative Filtering on Gowalla
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).
no code implementations • 25 Sep 2019 • Jin-Young Kim, Sung-Bae Cho
In this paper, we propose a method to decompose the latent space into basis, and reconstruct it by linear combination of the latent bases.
no code implementations • 10 Sep 2019 • Wonsup Shin, Tae-Young Kim, Sung-Bae Cho
A lifelog is a kind of big data to analyze behavior patterns in the daily life of individuals collected from various smart de-vices.
no code implementations • 7 Sep 2019 • Wonsup Shin, Seok-Jun Bu, Sung-Bae Cho
The two primary goals of the portfolio management problem are maximizing profit and restrainting risk.
no code implementations • ICLR 2019 • Jin-Young Kim, Sung-Bae Cho
Unlike the conventional GAN models with hidden distribution of latent space, we define the distributions explicitly in advance that are trained to generate the data based on the corresponding features by inputting the latent variables that follow the distribution.