臺灣博碩士論文加值系統

網路的興盛、行動裝置的普及與定位技術的成熟，使人們除了能夠藉由社群網站內交流生活經驗與討論最近的消息趣聞外，更能夠在餐廳或是旅遊景點打卡分享近況，因此可以說現在人們的現實生活與網路人生已密不可分。透過社群網站的服務，使用者可收藏想嘗試餐廳的評論、標記出感興趣的商家及記錄已去過的旅遊景點，這些使用者標記的餐廳、景點…等，我們將其統稱為興趣點 (point of interest, POI)。根據使用者所在地點找出其可能感興趣的鄰近店家，可進而發掘潛在的商業利益，因此如何有效地推薦興趣點給使用者，已引起眾多產學界的研究興趣。有鑑於現在的社群網站內含有商家的地理訊息與類別資訊、使用者的交友清單與評論內容等豐富的異質資訊，我們採用了異質資訊網路的形式來加以表現，並以社群網路分析中的關係預測技術來處理興趣點推薦的問題。更明確地來說，我們著重於探討使用者是否會到訪未曾去過 (或久未到訪) 的興趣點，並採用「詮釋路徑」的方法由眾多異質資訊中截取出具有語意的特徵，接著以監督式學習的技術建立到訪關係的預測模型。我們以點評網站Yelp提供的真實資料集進行一系列的實驗評估，實驗結果顯示我們的方法確實能萃取出異質網路中有用的資訊並建置出有不錯效果的預測模型。

A point of interest (POI) is a specific location that people may find useful or interesting. Examples include restaurants, stores, attractions, and hotels. With recent proliferation of location-based social networks (LBSNs), numerous users are gathered to share information on various POIs and to interact with each other. POI recommendation is then a crucial issue because it not only helps users to explore potential places but also gives LBSN providers a chance to post POI advertisements. As we utilize a heterogeneous information network to represent a LBSN in this work, POI recommendation is remodeled as a link prediction problem, which is significant in the field of social network analysis. Moreover, we propose to utilize the meta-path-based approach to extract implicit (but potentially useful) relationships between a user and a POI. Then, the extracted topological features are used to construct a prediction model with appropriate data classification techniques. In our experimental studies, the Yelp dataset is utilized as our testbed for performance evaluation purposes. Results of the experiments show that our prediction model is of good prediction quality in practical applications.

Chapter 1 Introduction 1
1.1 Motivation and Overview 1
1.2 Contributions of this Work 3
Chapter 2 Preliminaries 4
2.1 Location-based Social Networks 4
2.2 POI Recommendation 6
2.2.1 Basics of a POI Recommendation System 7
2.2.2 Latent Factors for POI Recommendation 8
2.3 Link Prediction in a Heterogeneous Information Network 10
2.3.1 Basics of a Heterogeneous Information Network 10
2.3.2 Problem of Link Prediction 11
Chapter 3 Utilizing Meta-Path Techniques for POI Recommendation 14
3.1 Representing the structure of a LBSN through a Heterogeneous Information Network 14
3.2 Extracting Latent Factors as Topological Features 17
3.3 Proposed Prediction Models 20
Chapter 4 Empirical Studies 25
4.1 Data Analysis on the Yelp Dataset 25
4.2 Experimental Results 29
4.3 A Case Study 33
Chapter 5 Conclusions and Future Works 35
Bibliography 36

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