博碩士論文 105423044 詳細資訊




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姓名 蔡名宣(Ming-Hsuan Tsai)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱 基於網路口碑與醫療利用理論之混合式推薦系統
(Hybrid Recommender System Based on eWOM and Healthcare Utilization Theory)
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檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2023-7-1以後開放)
摘要(中) 網路口碑是許多人搜尋醫療服務的重要依據,但網路資料量龐大,要找出符合需求的資訊相當耗時,推薦系統 (Recommender) 是解決上述問題的重要方法之一。在推薦系統設計中,冷啟動 (Cold-Start) (即一資料集中,若某項目從未被使用者評分時,要推薦此項目就變得困難)是個急待解決的問題,因為它會顯著降低推薦準確度。為此,本研究設計一套推薦機制,結合網路口碑與Anderson醫療利用理論 (Andersen Healthcare Utilization Model) 建立一套結合協同過濾式 (Collaborative filtering, CF) 及基於內容方法 (Content-based filtering, CBF) 的混合式牙醫推薦系統 (名為Dental Care Recommender, DCR),目的在解決冷啟動並協助使用者更快做出適當決策。
為了解DCR的實用性與精確度,本研究招募50位受測者使用DCR和Google搜尋引擎,並比較二者差異。實驗結果顯示DCR的推薦精確度顯著高於Google。而使用者對DCR的搜尋時間、系統品質、系統效能、系統觀感與使用意願的滿意度也較Google優異 (p<0.05)。本研究結果可提供醫療服務業者、管理者做為重要參考。
摘要(英) Many people now search for medical services based on electronic word-of-mouth (eWOM). However, with the vast amount of information existing on the internet, finding the information that meets one’s needs is extremely time-consuming, and recommender systems constitute one of the important solutions to this issue. In the design of recommender systems, cold start (in which an item in a dataset is difficult to recommend when said item has not been rated by users) is a problem in urgent need of a solution because it significantly reduces the accuracy of recommendations. In view of this, we used eWOM and the Andersen Healthcare Utilization Model to develop a hybrid recommendation mechanism combining collaborative filtering (CF) and content-based filtering (CBF). The objective of our system, called the Dental Care Recommender (DCR), is to solve the cold-start problem and help users in making good decisions quickly.
To understand the usefulness and accuracy of the DCR, we recruited 50 participants to use the DCR and the Google search engine and compare the two. The results indicated that the recommendations made by the DCR were significantly more accurate than those made by Google. The participants also expressed significantly greater satisfaction with the search time, system quality, system performance, and system perception in the DCR than Google and significantly greater willingness to use the DCR (p<0.05). The results of this study can provide crucial reference to medical service providers and managers.
關鍵字(中) ★ 推薦系統
★ 混合式推薦
★ 網路口碑
★ 冷啟動問題
★ 語意分析
★ 安德森醫療利用理論
關鍵字(英) ★ recommender system
★ hybrid recommendation
★ eWOM
★ cold-start problem
★ semantic analysis
★ Andersen Healthcare Utilization Model
論文目次 摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
第一章、 緒論 1
第1節、 研究背景與動機 1
第2節、 研究目的 2
第3節、 論文架構 2
第二章、 文獻探討 3
第1節、 推薦系統 3
第2節、 冷啟動問題 (Cold-Start Problem) 7
第3節、 以口碑為基礎的推薦系統 8
第三章、 研究方法 9
第1節、 系統架構 10
第2節、 資料搜集 11
第3節、 基於內容式的推薦 11
3.1 建立控制字彙 11
3.2 口碑文章搜集及分析 13
3.3 內容式個人化推薦 14
第4節、 協同過濾式的推薦 15
4.1 用戶資料搜集 15
4.2 用戶相似度計算 16
第5節、 混合式推薦 16
第6節、 評估模型 17
第四章、 結果與討論 18
第1節、 系統介紹 18
第2節、 系統評估 21
2.1 搜尋時間 21
2.2 推薦滿意度評估結果 22
2.3 使用者滿意度評估結果 22
2.4 小結 25
第五章、 結論與建議 26
第1節、 研究貢獻 26
第2節、 研究限制與未來方向 27
參考文獻 29
附錄一 34
附錄二 44
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指導教授 林熙禎 許文錦 審核日期 2018-7-10
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