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研究生:郭雯寧
研究生(外文):Wen-NingKuo
論文名稱:藉由適地性社群網絡中打卡行為探勘之地標推薦方法
論文名稱(外文):Mining User Check-in Behaviors in Location-Based Social Networks for Point-of-Interest Recommendation
指導教授:曾新穆曾新穆引用關係
指導教授(外文):Vincent S. Tseng
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊工程學系碩博士班
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:英文
論文頁數:54
中文關鍵詞:推薦技術地標資料探勘適地性網路社交使用者偏好探勘
外文關鍵詞:Recommendation TechniquesPoint-Of-InterestData MiningLocation-Based Social NetworkUser Preference Mining
相關次數:
  • 被引用被引用:0
  • 點閱點閱:511
  • 評分評分:
  • 下載下載:38
  • 收藏至我的研究室書目清單書目收藏:2
近年來,使用適地性社交網路做地標推薦服務的研究備受矚目,現今許多推薦技術都只能建立在利用使用者的打卡行為上。我們在本論文中提出一個創新的方法,名稱為Urban POI Mine (UPOI-Mine),基於使用者的喜好與地點的特性對適地性社交網路的使用者做地標推薦。其推薦模型的核心為利用使用者正規化後的打卡行為資料,訓練出以多元線性迴歸為基礎的預測器。再利用此預測器評估每個使用者的偏好。我們可從適地性社交網路中取出每個地標的特徵值,特徵值分成三大類:1. 社交方面 2. 該地標熱門程度 3.使用者偏好度。社交方面的特徵值是經由到過該地標且為社交網路中相似的使用者的打卡行為得到;地標熱門程度的特徵值是直接評估該地標相關的熱門程度得到;喜好相關程度的特徵值由計算該地標被打卡的機率得到,而機率是由該地標的標籤與使用者喜好之間的相關性產生。我們設計了一系列完整的實驗,透過真實的打卡資料,證明我們的方法可以非常準確的進行地標推薦。本研究為首例利用結合社交、地標熱門程度與使用者偏好度三種特徵值,推薦都市中的地標給使用者。
In this thesis, we propose a novel approach named Urban POI Mine (UPOI-Mine) that integrates location-based social network (LBSN) for recommending users urban POIs based on the user preferences and location properties simultaneously. The core idea of UPOI-Mine is to build a regression-tree-based predictor in the normalized check-in space so as to support the prediction of interestingness of POI related to each user’s preference. Based on the LBSN data, we extract the features of places from i) Social Factor (SF), which is summarized from all socially similar users’ check-ins at a specific POI for each user; ii) Individual Preference (IP), which indicates the probability of checking in a POI related to the semantic tag between the user and POI; and iii) POI Popularity (PP), which is derived by measuring relative popularity of individual POI. To our best knowledge, this is the first work on urban POI recommendation that considers social factor, individual preference and POI popularity in LBSN data simultaneously. Through a series of experiments on a real dataset, we have validated our proposed UPOI-Mine and shown that UPOI-Mine has excellent performance under various conditions.
中文摘要 i
ABSTRACT ii
誌謝 iii
CONTENTS iv
LIST OF TABLES vii
LIST OF FIGURES viii
CHAPTER 1 INTRODUCTION 1
1.1 BACKGROUND 1
1.2 MOTIVATION 3
1.3 METHODOLOGY 6
1.4 CONTRIBUTIONS 9
1.5 THESIS ORGANIZATION 10
CHAPTER 2 RELATED WORK 11
2.1 GENERAL ITEM RECOMMENDATION SYSTEMS 11
2.2 RELEVANCE MEASUREMENT 13
2.3 MOBILE BEHAVIOR PREDICTIONS AND RECOMMENDATIONS 15
CHAPTER 3 PROPOSED APPROACH 20
3.1 OVERVIEW OF THE PROPOSED APPROACH 20
3.2 URBAN POI MINE 22
3.3 FEATURES FROM SOCIAL FACTOR 23
3.3.1 Similarity by Common Check-ins (CheckSim) 25
3.3.2 Similarity by Relative Distance (DisSim) 26
3.4 FEATURES FROM INDIVIDUAL PREFERENCE 27
3.4.1 Preference in Category 29
3.4.2 Preference in Highlight 31
3.5 FEATURES FROM POI POPULARITY 32
3.6 POI RECOMMENDATION 33
CHAPTER 4 EXPERIMENTAL EVALUATION 35
4.1 GOWALLA DATASET 35
4.2 EVALUATION METHODOLOGY 36
4.3 EXPERIMENTAL RESULTS AND DISCUSSIONS 38
4.3.1 Comparison of Relevance Prediction Models 39
4.3.2 Comparison of Various Features 40
4.3.3 Comparisons with Existing Recommenders 43
4.4 DISCUSSIONS ON EXPERIMENTAL RESULTS 45
CHAPTER 5 CONCLUSIONS & FUTURE WORK 47
5.1 CONCLUSIONS 47
5.2 FUTURE WORK 48
REFERENCES 49
VITA 54

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