most citedCalibration-Disentangled Learning and Relevance-Prioritized Reranking for Calibrated Sequential Recommendation

6 citations · 6 across the 2 of their papers we have counts for

collaborators

3 papers

cs.AI2025

LaViC: Adapting Large Vision-Language Models to Visually-Aware Conversational Recommendation

Hyunsik Jeon, Satoshi Koide, Yu Wang +2

Conversational recommender systems engage users in dialogues to refine their needs and provide more personalized suggestions. Although textual information suffices for many domains…

cs.IR2024★ 6 cited

Calibration-Disentangled Learning and Relevance-Prioritized Reranking for Calibrated Sequential Recommendation

Hyunsik Jeon, Se-eun Yoon, Julian McAuley

Calibrated recommendation, which aims to maintain personalized proportions of categories within recommendations, is crucial in practical scenarios since it enhances user satisfacti…

cs.CV2024

Imagery as Inquiry: Exploring A Multimodal Dataset for Conversational Recommendation

Se-eun Yoon, Hyunsik Jeon, Julian McAuley

We introduce a multimodal dataset where users express preferences through images. These images encompass a broad spectrum of visual expressions ranging from landscapes to artistic…