1 code implementation • 11 Dec 2023 • Jinseok Seol, Minseok Gang, Sang-goo Lee, Jaehui Park
Additionally, that the proxy embeddings are shared across all items allows the infrequent items to borrow training signals of frequent items in a unified model structure and end-to-end manner.
Ranked #1 on Recommendation Systems on Amazon Fashion (using extra training data)
1 code implementation • 22 Apr 2022 • Jinseok Seol, Youngrok Ko, Sang-goo Lee
In recommendation systems, utilizing the user interaction history as sequential information has resulted in great performance improvement.
no code implementations • 11 Mar 2022 • Jinseok Seol, Seongjae Kim, Sungchan Park, Holim Lim, Hyunsoo Na, EunYoung Park, Dohee Jung, Soyoung Park, Kangwoo Lee, Sang-goo Lee
The rapid growth of the online fashion market brought demands for innovative fashion services and commerce platforms.
no code implementations • 13 Oct 2021 • Seongjae Kim, Jinseok Seol, Holim Lim, Sang-goo Lee
The first challenge is that outfit recommendation often requires a complex and large model that utilizes visual information, incurring huge memory and time costs.
1 code implementation • 18 May 2021 • Hyunsoo Cho, Jinseok Seol, Sang-goo Lee
However, the primary objective of contrastive learning is to learn task-agnostic features without any labels, which is not entirely suited to discern anomalies.
no code implementations • 7 May 2021 • Hanbit Lee, Jinseok Seol, Sang-goo Lee
Image-to-image translation aims to learn a mapping between different groups of visually distinguishable images.
1 code implementation • 14 Aug 2017 • Hanbit Lee, Jinseok Seol, Sang-goo Lee
With the rapid growth of online fashion market, demand for effective fashion recommendation systems has never been greater.
no code implementations • WS 2017 • Sanghyuk Choi, Taeuk Kim, Jinseok Seol, Sang-goo Lee
Word embedding has become a fundamental component to many NLP tasks such as named entity recognition and machine translation.