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研究生:陳鏡宇
研究生(外文):Ching-YuChen
論文名稱:基於適地性社群網路探勘及滿足多重限制之高效率個人化旅遊行程規劃
論文名稱(外文):Efficient Personalized Trip Planning with Multiple Constraints by Mining Location-Based Social Networks
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:71
中文關鍵詞:資料探勘旅遊行程規劃多重限制適地性社群網路
外文關鍵詞:Data MiningTrip PlanningMultiple ConstraintsLocation-Based Social Networks
相關次數:
  • 被引用被引用:0
  • 點閱點閱:330
  • 評分評分:
  • 下載下載:34
  • 收藏至我的研究室書目清單書目收藏:1
近年來,由於無線網路與智慧型手機的興起,帶動適地性服務與應用的迅速發展。在眾多的適地性服務當中,有關旅遊推薦的研究議題受到了高度的關注。其中一個熱門的議題為規劃符合使用者多重限制之下的旅遊行程推薦。雖然已經有不少的研究探討符合使用者限制之旅遊推薦議題,但多數的研究只是將這些限制當成篩選景點的前置作業,並非將各種限制融入整個旅遊規劃的流程當中。然而,若能夠將多種限制適當地融入旅遊規劃中,將能夠得到更加符合使用者需求的個人化旅遊行程。除此之外,從較大的區域或是較多的旅遊景點中規劃出旅遊行程,其運算的時間複雜度是非常的高,有賴現今雲端技術與平行處理的工具越來越成熟,如何改善運算效能將是非常實用且重要的課題。本研究提出一個創新的架構,名為Personalized Trip Recommendation with Multi-Constraints (PTRMC)。此架構能夠從適地性社群網路中的簽到行為,有效率的探勘並推薦滿足多項使用者限制之個人化旅遊行程。在PTRMC中,我們首先根據使用者的偏好以及時間的特性,提出了一個自動化與個人化的景點評分探勘模組。接著,在同時考慮多項使用者限制的情況下,我們提出一個高效率演算法,名為Parallel Trip-Mine+,高效率地規劃旅遊行程。據我們所知,本研究是第一個針對旅遊行程推薦同時考慮多重使用者限制、使用者社群關係、時間變化特性及平行化處理技術之研究。透過Gowalla網站所蒐集到的真實簽到資料,我們進行了一系列完整的實驗,實驗結果顯示PTRMC不論是在準確度以及運算效能上都有相當優異的表現。
In recent years, studies on travel recommendation get highly attentions since the rapid developments of wireless technologies and smart phones bring various Location-Based Services (LBSs) and applications. Among them, one of the hot topics is the constraint-based trip recommendation. Although a number of studies on this topic have been proposed in the literatures, most of them only regard the user-specific constraints as some filtering conditions for planning the trip. Actually, it is inevitable to immerse the constraints into travel recommendation systems for providing a personalized trip to users. In the other hand, the time complexity of trip planning from a certain set of attractions is sensitive to the scalability of travel regions. With the maturity of cloud computing and parallel computing tools nowadays, how to reduce the computational cost by parallel cloud computing techniques is also a critical issue. In this thesis, we propose a novel framework, named Personalized Trip Recommendation with Multi-Constraints (PTRMC), which can efficiently recommend personalized trips meeting multiple constraints of users by mining user’s check-in behaviors. In PTRMC, an automatic module is first proposed to mine the scores of attractions by considering both user-based preferences and temporal-based properties. Then, a trip planning algorithm, named Parallel Trip-Mine+, is proposed to efficiently plan the trip which satisfies multiple user-specific constraints. To our best knowledge, this is the first work on travel recommendation that considers the issues of multiple constraints, social relationship, temporal property and parallel computing simultaneously. Through comprehensive experimental evaluations on a real check-in dataset obtained from Gowalla, the proposed PTRMC is shown to deliver excellent performance.
中文摘要 I
Abstract III
誌謝 V
Content VI
List of Tables VIII
List of Figures IX
1. Introduction 1
1.1 Background 1
1.2 Motivation 3
1.3 Problem Statement 3
1.4 Contribution 4
1.5 Thesis Organization 5
2. Related Work 6
2.1 Location-Based Social Network 6
2.2 Location Recommendation 9
2.3 Trip Planning 10
2.4 Trip-Mine Algorithm 12
3. Proposed Methods 13
3.1 Overview of Our Proposed Framework 13
3.2 Preliminary 15
3.3 Attraction Finding 18
3.4 Attraction Score Mining 20
3.4.1 User-based Attraction Score Mining 20
3.4.2 Temporal-based Attraction Score Mining 23
3.5 Online Query Mechanism 25
3.5.1 Attraction Score Fusion 26
3.5.2 Planning Approach 27
3.5.3 Trip Diversity Re-Ranking 40
4. Experiments and Evaluation 42
4.1 Gowalla Datasets 42
4.2 Simulation Model and Experimental Settings 44
4.3 Comparison of Attraction Score Mining 45
4.4 Efficiency of Trip Planning 49
4.4.1 Impact of Number of Attractions NA 50
4.4.2 Impact of Travel Time Constraint CTT 50
4.4.3 Impact of Budget Constraint CBD 51
4.5 Comparison of Various Trip Planning Methods 55
4.5.1 Impact of Number of Attractions NA 56
4.5.2 Impact of Travel Time Constraint CTT 57
4.5.3 Impact of Budget Constraint CBD 58
4.6 Case Study 59
4.6.1 Trip Planned by d-LOA and v-LOA 59
4.6.2 Trip Planned by GOA 60
4.6.3 Trip Planned by Parallel Trip-Mine+ 61
4.7 Discussions on Experimental Results 63
5. Conclusions and Future Work 64
5.1 Conclusions 64
5.2 Future Work 66
References 67

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