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研究生:吳宗翰
研究生(外文):Zong-Han Wu
論文名稱:具動態時段之適地性協同過濾推薦系統
論文名稱(外文):Location-Based Collaborative Filtering Recommendation System with Dynamic Time Periods
指導教授:段裘慶段裘慶引用關係
口試委員:辛華昀許陳鑑周立德
口試日期:2012-06-08
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:電腦與通訊研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:84
中文關鍵詞:時間動態協同過濾LBS期望相對距離評分相似度
外文關鍵詞:Temporal DynamicCollaborative FilteringLBSExpected Related DistanceSimilarity of Evaluation
相關次數:
  • 被引用被引用:2
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  • 下載下載:48
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近年由於行動手持裝置的發展與普及,適地性行動商務應用需求也大幅增加。提供行動用戶最新、最正確且符合用戶需求的在地資訊也成為最主要的挑戰。傳統推薦系統只考慮過去用戶的歷史評價做為推薦依據,實際上用戶喜好與興趣景點 (Point of Interest, POI) 熱門度會隨時間不斷改變。為了讓推薦項目能更符合的目前時空條件,本論文提出結合期望相對距離、用戶近期喜好與POI新鮮度之具動態時段之適地性協同過濾推薦系統 (Location-based Collaborative Filtering Recommendation System with Dynamic Time Periods, LCFDTP),利用POI新鮮度過濾演算法,減少無效推薦機率及不必要之相似度計算時間,並透過近期喜好時間函數推薦給用戶符合用戶近期喜好之POI,改善傳統推薦機制隨時間日益偏差之推薦誤差。並進一步考量用戶移動性,加入期望相對距離讓位於用戶移動路徑上之POI能優先被系統推薦,減少行動用戶迴轉機率。
根據模擬實驗結果,本論文所提出之LCFDTP相較於TWCF、TPPCF及DFBT策略,不僅減少推薦誤差及更高的推薦覆蓋率,也減少平均推薦時間。在推薦誤差方面,在用戶速度為50 km/hr,查詢範圍為0.5 km時,LCFDTP之推薦誤差平均優於其他策略約61 %。在推薦覆蓋率方面,平均優於其他策略約9 %。最後,在平均推薦時間方面,平均優於其他策略約62 %。因此本論文所提出之LCFDTP更能符合行動用戶在行動環境下之查詢。


Demands for the application of the location-based mobile commerce have highly increased because of the development and popularity of handheld devices in recent years. To provide mobile users with the latest, most accurate localization information meeting user requirements is the major challenge. The traditional recommendation system only uses the historical assessments of users as the recommendation reference. In fact, user’s interest and the popularity of the point of interest (POI) will change frequently over time. It proposed the location-based collaborative filtering recommendations system of dynamic time period (LCFDTP), which combines expected related distance, user’s recent interest and the POI freshness. It adopt the algorithm of POI freshness to reduce invalid recommendation and unnecessary calculation of similarity. Through the function of the recent interest, it could recommend the POIs that the user likes recently and improve the recommend error. Further, it used expected factor to regulate the related distance in order to have priority to recommend the POIs in the user’s moving direction.
The simulation result showed that LCFDTP has not only less recommend error and more recommend coverage, but also less average recommend time than TWCF, TPPCF and DFBT. In recommend error, when user’s velocity is 50 km/hr and query range is 0.5 km, LCFDTP is better than the others about 61 %. In recommend coverage, LCFDTP is better than the others about 9 %. Finally, in average recommend time, LCFDTP is better than the others about 62 %. These showed that LCFDTP is more suitable to use in the mobile environment.


摘 要i
英文摘要iii
誌 謝v
目 錄vi
表目錄viii
圖目錄ix
第一章 緒論1
1.1 研究動機2
1.2 研究目的4
1.3 論文架構5
第二章 文獻探討6
2.1 行動定位相依查詢6
2.2 協同過濾推薦系統8
2.2.1 協同過濾機制9
2.2.2 協同過濾性能限制10
2.3 時間動態推薦系統11
2.3.1 時間衰退函數法12
2.3.1.1 時間權重協同過濾12
2.3.1.2 時間感知協同過濾13
2.3.1.3 時間分段協同過濾13
2.3.1.4 基於主題之協同過濾14
2.3.2 區段時間圖形法15
2.3.3 因子模型法16
2.3.4 策略比較表19
第三章 具動態時段之適地性協同過濾推薦系統20
3.1 LCFDTP推薦系統架構20
3.2 LCFDTP推薦系統之組成21
3.2.1 時間動態POI評分資料庫21
3.2.2 查詢範圍內POI新鮮度過濾26
3.2.3 POI相對距離期望係數27
3.2.4 POI評分相似度計算33
3.2.5 LCFDTP推薦價值計算35
3.3 LCFDTP演算法36
第四章 效能模擬與分析39
4.1 系統模擬環境39
4.1.1 模擬實驗參數設定39
4.1.2 模擬實驗環境限制46
4.2 效能評估因子47
4.2.1 推薦誤差47
4.2.2 推薦覆蓋率48
4.2.3 平均推薦時間48
4.3 模擬結果分析與比較49
4.3.1 POI新鮮度門檻值分析49
4.3.2 權重值影響分析52
4.3.3 推薦精確度分析53
4.3.4 推薦覆蓋率分析57
4.3.5 平均回應時間分析61
第五章 結論與未來研究方向65
5.1 結論65
5.2 未來研究方向66
參考文獻67
附錄78
A 中英文專有名詞對照表78
B 模擬系統簡介82
C 作者簡歷84


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