39 citations · 124 across the 18 of their papers we have counts for
18 papers
A Scalable and Transferable Time Series Prediction Framework for Demand Forecasting
Young-Jin Park, Donghyun Kim, Frédéric Odermatt +2
Time series forecasting is one of the most essential and ubiquitous tasks in many business problems, including demand forecasting and logistics optimization. Traditional time serie…
Direct Preference-based Policy Optimization without Reward Modeling
Gaon An, Junhyeok Lee, Xingdong Zuo +3
Preference-based reinforcement learning (PbRL) is an approach that enables RL agents to learn from preference, which is particularly useful when formulating a reward function is ch…
Pivotal Role of Language Modeling in Recommender Systems: Enriching Task-specific and Task-agnostic Representation Learning
Kyuyong Shin, Hanock Kwak, Wonjae Kim +5
Recent studies have proposed unified user modeling frameworks that leverage user behavior data from various applications. Many of them benefit from utilizing users' behavior sequen…
Ask Me What You Need: Product Retrieval using Knowledge from GPT-3
Su Young Kim, Hyeonjin Park, Kyuyong Shin +1
As online merchandise become more common, many studies focus on embedding-based methods where queries and products are represented in the semantic space. These methods alleviate th…
Deformable Graph Transformer
Jinyoung Park, Seongjun Yun, Hyeonjin Park +5
Transformer-based models have recently shown success in representation learning on graph-structured data beyond natural language processing and computer vision. However, the succes…
VQ-AR: Vector Quantized Autoregressive Probabilistic Time Series Forecasting
Kashif Rasul, Young-Jin Park, Max Nihlén Ramström +1
Time series models aim for accurate predictions of the future given the past, where the forecasts are used for important downstream tasks like business decision making. In practice…
Hazard Gradient Penalty for Survival Analysis
Seungjae Jung, Kyung-Min Kim
Survival analysis appears in various fields such as medicine, economics, engineering, and business. Recent studies showed that the Ordinary Differential Equation (ODE) modeling fra…
Metropolis-Hastings Data Augmentation for Graph Neural Networks
Hyeonjin Park, Seunghun Lee, Sihyeon Kim +5
Graph Neural Networks (GNNs) often suffer from weak-generalization due to sparsely labeled data despite their promising results on various graph-based tasks. Data augmentation is a…
Scaling Law for Recommendation Models: Towards General-purpose User Representations
Kyuyong Shin, Hanock Kwak, Su Young Kim +4
Recent advancement of large-scale pretrained models such as BERT, GPT-3, CLIP, and Gopher, has shown astonishing achievements across various task domains. Unlike vision recognition…
Intent-based Product Collections for E-commerce using Pretrained Language Models
Hiun Kim, Jisu Jeong, Kyung-Min Kim +7
Building a shopping product collection has been primarily a human job. With the manual efforts of craftsmanship, experts collect related but diverse products with common shopping i…
Global-Local Item Embedding for Temporal Set Prediction
Seungjae Jung, Young-Jin Park, Jisu Jeong +4
Temporal set prediction is becoming increasingly important as many companies employ recommender systems in their online businesses, e.g., personalized purchase prediction of shoppi…
One4all User Representation for Recommender Systems in E-commerce
Kyuyong Shin, Hanock Kwak, Kyung-Min Kim +4
General-purpose representation learning through large-scale pre-training has shown promising results in the various machine learning fields. For an e-commerce domain, the objective…
Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-Learning
Dasol Hwang, Jinyoung Park, Sunyoung Kwon +3
In recent years, graph neural networks (GNNs) have been widely adopted in the representation learning of graph-structured data and provided state-of-the-art performance in various…
A Worrying Analysis of Probabilistic Time-series Models for Sales Forecasting
Seungjae Jung, Kyung-Min Kim, Hanock Kwak +1
Probabilistic time-series models become popular in the forecasting field as they help to make optimal decisions under uncertainty. Despite the growing interest, a lack of thorough…
Multi-Manifold Learning for Large-scale Targeted Advertising System
Kyuyong Shin, Young-Jin Park, Kyung-Min Kim +1
Messenger advertisements (ads) give direct and personal user experience yielding high conversion rates and sales. However, people are skeptical about ads and sometimes perceive the…
Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs
Dasol Hwang, Jinyoung Park, Sunyoung Kwon +3
Graph neural networks have shown superior performance in a wide range of applications providing a powerful representation of graph-structured data. Recent works show that the repre…
Hop Sampling: A Simple Regularized Graph Learning for Non-Stationary Environments
Young-Jin Park, Kyuyong Shin, Kyung-Min Kim
Graph representation learning is gaining popularity in a wide range of applications, such as social networks analysis, computational biology, and recommender systems. However, diff…
Tripartite Heterogeneous Graph Propagation for Large-scale Social Recommendation
Kyung-Min Kim, Donghyun Kwak, Hanock Kwak +7
Graph Neural Networks (GNNs) have been emerging as a promising method for relational representation including recommender systems. However, various challenging issues of social gra…