期刊論文
| 學年 | 114 |
|---|---|
| 學期 | 1 |
| 出版（發表）日期 | 2025-09-19 |
| 作品名稱 | CARES: A Hybrid Caregivers Recommendation System Using Deep Learning and Knowledge Graphs |
| 作品名稱（其他語言） | |
| 著者 | Qiaoyun Zhang; Sze-Han Wang; Chung-Chih Lin; Chih-Yung Chang; Diptendu Sinha Roy |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Internet of Things 34, p. 101769 |
| 摘要 | Recommendation systems have prospered by leveraging user-item interactions and their features for personalized recommendations. Recent advancements in deep learning further enhance these recommendation systems with powerful backbones for learning from user-item data. However, solely depending on these interactions often leads to the cold-start problem, where items lacking historical data cannot be effectively recommended. Additionally, the issue of high similarity between user and item features frequently goes unresolved. This paper introduces a Hybrid Caregiver Recommendation mechanism, called CARES, designed to recommend suitable caregivers for postpartum women using deep learning and knowledge graphs. Initially, the proposed CARES utilizes Extreme Gradient Boosting (XGBoost) to identify important features, addressing the issue of feature similarity. Then it employs K-Means clustering to group postpartum women and caregivers based on similar features. Subsequently, it utilizes a Deep & Cross Network (DCN) to automatically learn feature interactions and constructs knowledge graphs to tackle the cold start problem. The proposed CARES also integrates exploration and exploitation strategies to balance the accuracy and diversity of recommendations. The proposed CARES compares with existing mechanisms on real datasets, and the simulation results demonstrate its effectiveness in terms of precision, recall, and F1-Score. |
| 關鍵字 | |
| 語言 | en |
| ISSN | |
| 期刊性質 | 國外 |
| 收錄於 | SCI |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 是 |
| 國別 | USA |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/128675 ) |