20 citations · 37 across the 8 of their papers we have counts for
8 papers
KARMA: Knowledge graph-based Automated Reasoning Materialization and Alignment
Jinkyeong Choi, Chaebin Jeong, Donghyeon Park
Template-based contrastive synthesis is scalable, but its candidates often differ only in a few entity-slots while sequence-level optimization spreads supervision over mostly share…
ReFORM: Review-aggregated Profile Generation via LLM with Multi-Factor Attention for Restaurant Recommendation
Moonsoo Park, Seulbeen Je, Donghyeon Park
In recommender systems, large language models (LLMs) have gained popularity for generating descriptive summarization to improve recommendation robustness, along with Graph Convolut…
SimDiffRec: Semantic Similarity-Guided Diffusion for Contrastive Sequential Recommendation
Jinkyeong Choi, Yejin Noh, Donghyeon Park
In sequential recommendation systems, data augmentation and contrastive learning techniques have recently been introduced using diffusion models to achieve robust representation le…
Culinary Class Wars: Evaluating LLMs using ASH in Cuisine Transfer Task
Hoonick Lee, Mogan Gim, Donghyeon Park +2
The advent of Large Language Models (LLMs) have shown promise in various creative domains, including culinary arts. However, many LLMs still struggle to deliver the desired level o…
CookingSense: A Culinary Knowledgebase with Multidisciplinary Assertions
Donghee Choi, Mogan Gim, Donghyeon Park +4
This paper introduces CookingSense, a descriptive collection of knowledge assertions in the culinary domain extracted from various sources, including web data, scientific papers, a…
KitchenScale: Learning to predict ingredient quantities from recipe contexts
Donghee Choi, Mogan Gim, Samy Badreddine +3
Determining proper quantities for ingredients is an essential part of cooking practice from the perspective of enriching tastiness and promoting healthiness. We introduce KitchenSc…
RecipeMind: Guiding Ingredient Choices from Food Pairing to Recipe Completion using Cascaded Set Transformer
Mogan Gim, Donghee Choi, Kana Maruyama +4
We propose a computational approach for recipe ideation, a downstream task that helps users select and gather ingredients for creating dishes. To perform this task, we developed Re…
KitcheNette: Predicting and Recommending Food Ingredient Pairings using Siamese Neural Networks
Donghyeon Park, Keonwoo Kim, Yonggyu Park +2
As a vast number of ingredients exist in the culinary world, there are countless food ingredient pairings, but only a small number of pairings have been adopted by chefs and studie…