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研究生:謝易達
論文名稱:旅遊Apps推薦景點的自適應調整功能
論文名稱(外文):A Self-Adaptive Approach to Recommendation Functions forTourism Apps
指導教授:賴聯福賴聯福引用關係
學位類別:碩士
校院名稱:國立彰化師範大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
畢業學年度:103
語文別:中文
論文頁數:45
中文關鍵詞:自適應基因演算法資料探勘推薦系統
外文關鍵詞:adaptationgenetic algorithmsdata miningrecommendation systems
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一般旅遊網站及Apps僅提供旅遊資訊的查詢、討論或經驗分享,但是在規劃旅遊行程時缺乏針對使用者個人情境的景點推薦機制,旅遊時也不會依據個人的不同情境進行互動。為了改善以上的問題,本實驗室已應用資料探勘與擴增實境技術開發一個具影像互動性的社群旅遊Apps,稱為「如影隨行」。利用Apriori和FP-growth演算法,藉由資料探勘方式找到符合使用者個人化的景點推薦排序,包括從目前位置推薦附近景點、推薦自己尚未去過的熱門景點、和推薦好友們的私房景點等。其資料探勘方法的各項門檻值和權重值是由人工主觀給予固定值,然而主觀所認定數值可能不是最佳值,可能會導致推薦景點的排序結果和使用者的需求有落差。本論文使用基因演算法提供自適應調整機制,記錄使用者點選推薦景點的動作作為隱性回饋,以不斷學習的方式逐步微調各項門檻值和權重值,透過自適應調整機制來持續改善推薦景點的排序結果。
Traditional travel websites provide the inquiring, the sharing and the discussion about travel information. However, these travel websites cannot recommend us some attractions or interact with us based on our personal situations. To alleviate the mentioned problems, we had developed an interactive tourism App through augmented reality and data mining. The data mining approach including Apriori and FP-growth is adopted to recommend attractions based on personal situations. In this thesis, we gather and analyze the feedbacks of user’s clicks of recommend attractions to perform self-adaptation. The notion of genetic algorithms is adopted to continuously adjust the settings for data mining. A series of actions including selection, reproduction, crossover, and mutation are applied to produce the next generation of the settings. The results of data mining can thus be improved gradually by continuous adaptation.
中文摘要 I
Abstract II
目錄 IV
圖目錄 V
表目錄 VII
第一章 緒論 1
1.1 研究背景與動機 1
1.2 論文架構 3
第二章 相關研究與文獻 4
2.1 使用者回饋 4
2.2 關聯式法則探勘 5
2.3 基因演算法 6
2.4 自適應機制 7
第三章 「如影隨行」旅遊App應用程式 8
3.1 利用擴增實境建立影像互動性旅遊App的系統架構 8
3.2 使用資料探勘推薦景點 10
第四章 自適應調整機制 16
4.1 基因演算法 16
4.1.1 基因演算法流程 17
4.2 自適應調整參數之流程 21
4.3 基因演算法於自適應調整機制 22
第五章 實驗結果 26
5.1全台灣景點之熱門景點推薦 26
5.2全台灣景點之私房景點推薦 34
第六章 結論 41
參考文獻 42

圖目錄
圖 1. 影像互動性旅遊App「如影隨行」系統架構 9
圖 2. 選取推薦條件介面 10
圖 3. Apriori演算法 11
圖 4. 使用Apriori演算法進行景點資料探勘 12
圖 5. FP-tree的結構 13
圖 6. 熱門景點推薦介面 14
圖 7. 拓荒景點推薦介面 14
圖 8. 私房景點推薦介面 15
圖 9. 二進位編碼 17
圖 10. 實數編碼 17
圖 11. 符號編碼 17
圖 12. 輪盤選擇法示意圖 18
圖 13. 基因演算法流程圖 20
圖 14. 自適應調整推薦景點之門檻值與權重值的流程圖 21
圖 15. 個體x與個體i的各自排序結果 23
圖 16. 本研究之基因以算法流程示意圖 23
圖 17. Whole Arithmetic Crossover範例圖,α=0.3 24
圖 18. 高斯分布圖 25
圖 19. 高斯突變範例圖 25
圖 20. 熱門景點推薦的支持度門檻值變化 33
圖 21. 熱門景點推薦的信賴度門檻值變化 33
圖 22. 熱門景點推薦的熱門程度增加權重值(incp)變化 33
圖 23. 私房景點推薦的支持度門檻值變化 40
圖 24. 私房景點推薦的信賴度門檻值變化 40
圖 25. 私房景點推薦的喜愛程度增加權重值(incf)變化 40

表目錄
表 1 個體與其適應值 18
表 2 使用者點選之景點 26
表 3 熱門景點推薦演算法參數的預設初始族群 27
表 4 基因演算法第一代第11個體之個人化推薦景點 28
表 5 第二代子代族群 30
表 6 基因演算法第十五代第一順位個體之個人化推薦景點 31
表 7 基因演算法第二十五代第一順位個體之個人化推薦景點 32
表 8 使用者點選之景點 34
表 9 私房景點推薦演算法參數的預設初始族群 34
表 10 基因演算法第一代第5個體之個人化推薦景點 36
表 11 第二代子代族群 37
表 12 基因演算法第十一代第一順位個體之個人化推薦景點 38
表 13 基因演算法第二十三代第一順位個體之個人化推薦景點 39

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