Earticle

초록

영어

This study aims to solve the 'semantic interference' problem that occurs in B2B recommender systems when processing over 17,000 product categories within a single embedding space. To address this, we propose a two-stage Hierarchical Multimodal Recommendation Model (Hierarchical Model). The proposed model first predicts one of 238 mid-level categories in the first stage, and then performs detailed item recommendation on a refined candidate pool within that category in the second stage. Comparative experiments against a single model (Baseline Model) using the same CLIP-based architecture showed that the Hierarchical Model achieved a consistent and significant performance improvement of 4.5-5.0%p on average across all key metrics, including Precision@K, MAP@K, and NDCG@K. This paper empirically demonstrates that this performance enhancement is not merely due to search space reduction, but is based on clear theoretical justifications: (1) 'semantic disambiguation' through the specialization of embedding spaces, (2) the efficiency of a 'learned cascade' structure, and (3) the mitigation of data sparsity via the 'information bottleneck' principle.

목차

Abstract
1. 서론
2. 관련 연구
2.1 B2B 환경에서의 추천 시스템
2.2 멀티모달 기반 추천 모델
2.3 계층적 구조를 활용한 카테고리 기반 추천
3. 연구 방법
3.1 연구 개요
3.2 데이터 전처리
3.3 계층형 멀티모달 추천 모델 설계
4. 실험 및 결과
4.1 실험 환경 설정
4.2 모델 성능 평가
5. 결론 및 향후 연구
5.1 결론
5.2 향후 연구
References

키워드

저자

• 오소진 [ Sojin Oh | Professor, Hannam University Business School ] First Author
• 김재경 [ Jae Kyung Kim | Professor, Hannam University Business School ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  JITAM [Journal of Information Technology Applications and Management]

• 간기

  격월간

• pISSN

  1598-6284

• eISSN

  2508-1209

• 수록기간

  1999~2026

• 십진분류

  KDC 005 DDC 005