10 citations · 36 across the 8 of their papers we have counts for
8 papers
HyperCLOVA X Technical Report
Kang Min Yoo, Jaegeun Han, Sookyo In +393
We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. H…
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…
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…
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…
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…
Graphs, Entities, and Step Mixture
Kyuyong Shin, Wonyoung Shin, Jung-Woo Ha +1
Existing approaches for graph neural networks commonly suffer from the oversmoothing issue, regardless of how neighborhoods are aggregated. Most methods also focus on transductive…
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…