인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한산업공학회 Industrial Engineering & Management Systems Industrial Engineering & Management Systems Vol.22 No.3
- 발행연도
- 2023.9
- 수록면
- 340 - 348 (9page)
- DOI
- 10.7232/iems.2023.22.3.340
이용수
초록· 키워드
In recent years, recommender systems based on machine learning have become common tools on various web services. Among recommendation models, embedding methods such as Item2Vec that utilize embedded representations of items are widely used in actual applications due to their effectiveness and ease of use. By utilizing embedded representations acquired through learning the interaction between users and items, it is easy to discover similar items from the viewpoint of the user’s purchasing tendencies. In contrast, with this method, only biased items are recommended, making it difficult to ensure a wide variety of recommended items. However, there is a trade-off between the diversity of recommended items and accuracy and providing diversity in recommended items while maintaining accuracy is a challenging problem. Therefore, in this study, we propose a method to expand the new evaluation metric "recommendation region" (sum of distances of recommended items from the user vector in the embedding space) without significantly reducing accuracy. Specifically, we recommend not only items that are close to the user vector in the embedding space but also items with a certain distance based on detailed observation of the positional relationships. With the proposed method, we aim to increase user satisfaction by expanding the diversity of items that the user comes into contact with in the service. Finally, we demonstrate the usefulness of our proposed method through evaluation experiments using open-source datasets.
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목차
- ABSTRACT
- 1. INTRODUCTION
- 2. RELATED WORK
- 3. PROPOSED METHOD
- 4. EVALUATION EXPERIMENT
- 5. DISCUSSIONS
- 6. CONCLUSION AND FUTURE WORKS
- REFERENCES
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