指導教授：許永真臺灣大學：資訊工程學研究所辛丹尼Navarro, Daniel SilvaDaniel SilvaNavarro2014-11-262018-07-052014-11-262018-07-052014http://ntur.lib.ntu.edu.tw//handle/246246/261548Traveling is a very important activity on human life; moreover, is a very profitable business all over the world. However, when people start planning their trips overseas, searching for information about places to visit, can be a very time consuming and misleading task. This research, aims to create the first module of a bigger e-tourism recommendation platform for Taiwan travelers. This initial module, will focus in Taipei pre-travel issues and it will recommends point of interest using a collaborative filtering approach. To effectuate the recommendation, a modified version of slope one algorithm was utilized to predict the unavailable ratings on the dataset and mixed with the traditional CF prediction algorithm. This mixed algorithm showed a 10.19\% MAE improvement in comparison to the basic traditional collaborative filtering approach. To effectuate the experiments for this recommendation system, the dataset is composed by the most popular 86 points of interest in Taipei. These point of interest were reviewed by 27 foreigners living in Taiwan.Abstract v 1 Introduction 1 1.1 Motivation 1 1.1.1 Pre-Visiting Issues 2 1.1.2 In-Visiting Issues 3 1.1.3 Post-Visiting Issues 3 1.2 Objective 4 1.3 Thesis Organization 5 2 Related Work 7 2.1 Recommender Systems 7 2.2 Types of Recommender Systems 8 2.2.1 Demographic Recommendation 8 2.2.2 Content Based Filtering 8 2.2.3 Knowledge Based Filtering 9 2.2.4 Collaborative Filtering 10 2.2.5 Hybrid Recommender System 13 2.3 Tourism Recommender Systems 14 2.3.1 Travelers’ Issues 14 2.3.2 Popular Tourism Recommendation Services 15 3 Taipei Tourism Recommender System 17 3.1 Problem Definition 17 3.1.1 A Common Scenario 17 3.1.2 Objective 18 3.1.3 Definitions 18 3.2 The Recommender System 20 3.2.1 System Arquitecture 20 3.2.2 The Recommendation Algorithm 21 4 TP-REC Implementation 27 4.1 The Software 27 4.1.1 The Control Layer 28 4.1.2 The Model Layer 28 4.1.3 The View Layer 28 4.2 The User Interface 29 4.2.1 Login view 29 4.2.2 Main view 30 5 Experimentation And Evaluation 37 5.1 Data Collection And Data Presentation 37 5.1.1 Point Of Interest 38 5.1.2 Users’ Ratings and Background 38 5.2 Experiments And Performance Results 40 5.2.1 Experiments And Selected Benchmarking Algorithms 41 5.2.2 Experiments Results 42 5.3 Users’ Recommendation Feedback 43 5.3.1 The Interviewed Users 43 5.3.2 The Interview Methodology 44 5.3.3 Users’ Interview Results 45 5.3.4 Interviewee Conclusion Analysis 46 6 Conclusion and Future Work 47 6.1 Summary of Contributions 48 6.2 Limitations 48 6.3 Future Work 49 Bibliography 514561323 bytesapplication/pdf論文公開時間：2019/08/01論文使用權限：同意有償授權(權利金給回饋本人)推薦系統旅遊導覽旅遊thesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/261548/1/ntu-103-R97922151-1.pdf