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系統識別號 U0026-1302201917414100
論文名稱(中文) 象徵圖模型於使用者時空間行為模式探勘技術之應用
論文名稱(英文) Learning Spatial-Temporal User Behaviors with Symbolic Graphs
校院名稱 成功大學
系所名稱(中) 資訊工程學系
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 107
學期 1
出版年 108
研究生(中文) 鄧善云
研究生(英文) SHAN-YUN TENG
學號 P78031159
學位類別 博士
語文別 英文
論文頁數 137頁
口試委員 口試委員-高宏宇
指導教授-莊坤達
口試委員-李政德
口試委員-曾新穆
口試委員-謝孫源
口試委員-黃俊龍
口試委員-葉彌妍
中文關鍵字 時間空間學習  題目推薦系統  互動式推薦  知識概念圖挖掘  圖嵌入  增強式學習  室內停留模式挖掘  象徵圖模型  機率性模式挖掘  崇拜鏈接預測  社群網路分析 
英文關鍵字 spatial-temporal learning  question recommender system  interactive recommendation  knowledge graph mining  graph embedding  reinforcement learning  indoor stop-by pattern mining  symbolic graph model  uncertain pattern mining  worship link prediction  social network analysis 
學科別分類
中文摘要 隨著數據挖掘技術的快速發展,數據分析過程有了很大的進程,從傳統單純統計方法的分析進展到現在機器學習演算法的開發。此外,歸功於數位裝置的蓬勃發展,使用者的數位紀錄愈發容易取得,透過分析並學習隱藏在數位紀錄中的使用者行為,能有效預估使用者未來的行為模式,例如:使用者空間移動行為,使用者社交行為和使用者評分性為等等。 由於在不同類型的資料中能發現極其有趣並不同的使用者行為,越來越多的研究人員將其主要重點放在用於學習和預測用戶行為的機器學習算法上。最近,由於具有地理定位能力的具有成本效益的移動設備的高滲透率,可以用時間和空間信息連續收集用戶行為記錄。因此,已經提出了許多時空學習方法來用空間或時間意義來計算用戶行為的隱藏信息。時空數據挖掘技術也在許多領域得到了廣泛的應用,如犯罪分析,自然災害預測,環境變化與醫學發展的關係研究。這些過往的研究清楚地證明了利用時間空間學習技術來分析並預測犯罪行為,疾病趨勢和環境變化的重大優勢和效益。不同於以往的研究論文,在我的論文中,我試圖學習新型態的使用者行為模式,在包括室內空間,社群網絡和電子學習系統等不同場景中研究尚未探索過的的時間空間使用者行為模式。更具體地說,我研究了以下三種使用者行為模式:在時間空間維度下室內空間的使用者停留行為、在時間維度下社群網路中的使用者崇拜行為、以及在時間維度下電子學習系統中的使用者評分行為。對於這些使用者行為,我設計了不同的象徵圖模型,如機率型象徵圖,異構象徵圖和依賴性象徵圖,分別學習時空性停留行為,時間性崇拜行為和時間性評分行為。象徵圖被認為是某種標記圖,其圖上邊的標籤是給定數據域的值。象徵圖的概念是通過考慮標籤為變量的圖形以及約束這些變量的可能值的一組條件或公式來概括這一概念。本論文主要研究的使用者學習行為的簡要說明如下。

機率象徵圖模型於使用者時空間停留行為模式探勘技術之應用:
在這項研究中,我探索了一種新的使用者行為模式,稱為使用者室內停留行為,旨在發現用戶在類似商場的室內環境中的停留行為。使用者室內停留行為的發現使得能夠在室內空間(例如:購物中心)的商店之間進行新的營銷協作,例如聯合優惠券促銷。此外,它還可以幫助解決在室內空間中人潮過度擁擠的情況。為了追求更好的實用性,我考慮了基於成本效益的無線傳感器環境,並對實際數據進行室內停留行為分析。我提出機率型的使用者停留模式框架結合了概率模型,從感測資料收集的數據中挖掘出的前k個最頻繁的使用者室內停留模式。此外,我建立了機率型象徵圖模型,並設計了索引性的項集頻率算法,以提高準確性和高效率性。在實驗中,我針對一個模擬數據和一個真實數據實作研究結果顯示,我提出的的PTkISP框架可以有效地發現高質量的使用者室內停留行為模式,並可以為營銷合作提供富有洞察力的觀察。

異構象徵圖模型於使用者時間崇拜行為模式探勘技術之應用:
我在這項研究中,探索一種新的社群網路鏈接預測,稱為“崇拜鏈接”預測,用以學習一般使用者在社交網絡上對名人的崇拜鏈接。對“崇拜”鏈接的預測可以實現有價值的社會服務,例如病毒式營銷,名人人氣估計和名人推薦系統。然而,作為商業安全和個人隱私的關注,只有一般統計的社群網路屬性容易取得,而使用者的詳細鏈接信息的取得是相對困難的。為了解決這些問題,我設計了一種新穎的學習框架,包括具有新發現的社群統計特性所建構的異構函示圖,並融合互動式學習的高斯估計算法。我們對真實數據的實驗結果顯示所提出的學習框架可以克服缺少鏈接標籤的問題並有效地發現一般人和名人間的崇拜鏈接,實作的社群網路包括Instagram、Twitter和DBLP。

依賴性象徵圖模型於使用者時間答題行為模式探勘技術之應用:
在這項研究中,我主要著重在從電子學習系統的交互式問答過程中探索使用者的答題行為。我提出一種名為CagMab的新型互動式學習系統,其被設計為以循環策略與使用者的互動中推薦未知問題給使用者,這樣的未知推薦系統有助於像是自我評估式的會話機器人的應用。該流程使使用者能夠發現自我的弱點並進一步幫助他們成長。雖然透過傳統的多臂吃角子老虎機框架提供了解決方案,但它通常會導致的叫不準確的問題推薦結果,因為它僅僅依賴於使用者回答問題的上下文特徵。為了解決這個問題,我開發了一種新穎的互動式學習框架,利用了概念象徵圖的依賴性來學習使用者的答題行為。我們對實際數據的實驗研究表明,所提出的框架可以互動式的有效預測使用者答題評分,以用於電子學習系統中的問題推薦。

總結,在我的博士論文中主要研究利用象徵圖在室內空間,社群網路和電子學習系統中來學習時間空間維度下的使用者行為。並在實驗結果中證實了利用象徵圖結合機器學習演算法可以大大的提升學習使用者十空間行為的準確性。
英文摘要 Recently, interesting user behaviors have been found in different fields. Therefore, many research focuses on understanding user behaviors, needs, and motivations through observation techniques, data analysis, and machine learning methodologies. By learning different user behaviors, the prediction of user behaviors can lead to applications for indispensable services such as trajectory patterns mining, social links prediction, and personalized recommender systems. Due to the high penetration rate of cost-effective mobile devices, it becomes possible to continuously collect user behavior records with temporal and spatial information. As such, many spatial-temporal learning methodologies have been proposed to figure out the hidden information of user behaviors with the spatial or temporal sense. In my dissertation, we attempt to learn spatial-temporal user behaviors which are left unexplored thus far in different scenarios such as indoor spaces, social networks, and E-learning systems. More specifically, we investigated the following three kinds of user behaviors, spatial-temporal stop-by behaviors in indoor spaces, temporal worship behaviors in celebrity-dived networks, and temporal rating behaviors in E-learning systems.

Learning Spatial-Temporal Stop-by Behaviors with Uncertain Symbolic Graphs in Indoor Spaces:
In this work, we explore a new mining paradigm, called Indoor Stop-by Patterns (ISP), to discover user stop-by behavior in mall-like indoor environments. The discovery of ISPs enables new marketing collaborations, such as a joint coupon promotion, among stores in indoor spaces (e.g., shopping malls). Moreover, it can also help in eliminating the overcrowding situation. To pursue better practicability, we consider the cost-effective wireless sensor-based environment and conduct the analysis of indoor stop-by behaviors on real data. The proposed Probabilistic Top-k Indoor Stop-by Patterns Discovery (PTkISP) framework incorporates the probabilistic model to identify top-k ISPs over uncertain data collected from sensing logs. Moreover, we develop an uncertain symbolic model and devise an Index 1-itemset (IIS) algorithm to enhance the accuracy and efficiency. Our experimental studies on one synthetic data and one real data show that the proposed PTkISP framework can efficiently discover high-quality ISPs and can provide insightful observations for marketing collaborations.

Learning Temporal Worship Behaviors with Heterogeneous Symbolic Graph in Celebrity-Dived Networks:
We in this work explore a new link prediction paradigm, called ‘worship’ prediction, to discover worship links between users and celebrities on celebrity-dived networks. The prediction of ‘worship’ links enables valuable social services, such as viral marketing, popularity estimation, and celebrity recommendation. However, as the concern of business security and personal privacy, only public-accessible statistical social properties, instead of the detailed information of users, can be utilized to predict the ‘worship’ labels.
To address these issues, a novel learning framework is devised, including a heterogeneous factor graph with new discovered statistical properties and a Gaussian estimation based learning algorithm with active learning. Our experimental studies on real data, including Instagram, Twitter, and DBLP, show that the proposed learning framework can overcome the problem of missing labels and efficiently discover worship links.

Learning Temporal Rating Behaviors with Dependent Symbolic Graphs in E-Learning Systems:
In this work, we address an important issue on the exploration of user rating behaviors from an interactive question-answering process in E-learning systems. A novel interactive learning system, called CagMab, is devised to interactively recommend questions with a round-by-round strategy, which contributes to applications such as a conversational bot for self-evaluation. The flow enables users to discover their weakness and further helps them to progress. Even though formulating the problem with the multi-armed bandit framework provides a solution, it often leads to suboptimal results for interactive unknowns recommendation as it simply relies on the contextual features of answered questions. To address this issue, we develop a novel interactive learning framework by borrowing strengths from the dependency of concept-aware graphs for learning user ratings. Our experimental studies on real data show that the proposed framework can effectively predict user ratings in an interactive fashion for the recommendation in E-learning systems.

In summary, we in my dissertation focus on learning spatial-temporal user behaviors with symbolic graphs in indoor spaces, social networks, and E-learning systems. The experimental results of learning the proposed user behaviors show that incorporating symbolic graphs with machine learning algorithms can significantly improve the accuracy performances.
論文目次 Contents
中文摘要............................................ i
Abstract............................................. iii
Acknowledgment ........................................ vi
Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
List of Tables.......................................... xii
List of Figures .........................................xiv
1 Introduction......................................... 1
1.1 Background ...................................... 1
1.2 OverviewoftheDissertation............................. 2
1.2.1 Learning Spatial-Temporal Stop-by Behaviors with Uncertain Symbolic Graphsin Indoor Spaces........................... 2
1.2.2 Learning Temporal Worship Behaviors with Heterogeneous Symbolic Graph in Celebrity-Dived Networks......................... 3
1.2.3 Learning Temporal Rating Behaviors with Dependent Symbolic Graphs in E-LearningSystems ............................ 4
1.3 Organization ..................................... 6
2 Learning Spatial-Temporal Stop-by Behaviors with Uncertain Symbolic Graphs in Indoor Spaces........................................ 7
2.1 Introduction of Learning Spatial-Temporal Stop-by Behaviors . . . . . . . . . . 7
2.2 Preliminaries of Learning Spatial-Temporal Stop-by Behaviors . . . . . . . . . . 12
2.2.1 Indoor Tracking Settings........................... 13
2.2.2 Symbolic Model Design............................ 16
2.2.3 Problem Formulation............................. 19
2.3 Framework of Learning Spatial-Temporal Stop-by Behaviors . . . . . . . . . . . 20
2.3.1 Object Traversal Event Filtering ...................... 21
2.3.2 ROIs Mapping ................................ 23
2.3.3 Traditional Uncertain Frequent Pattern Mining . . . . . . . . . . . . . . 26
2.3.4 Frequent Device Pattern Mining....................... 27
2.3.5 Object Stop-by Events Clustering...................... 32
2.3.6 Top-k Indoor Stop-by Patterns Discovery. . . . . . . . . . . . . . . . . . 36
2.4 Experimental Results of Learning Spatial-Temporal Stop-by Behaviors . . . . . 38
2.4.1 Data Description ............................... 38
2.4.2 Experimental Setup.............................. 40
2.4.3 Experiments on Synthetic Data ....................... 42
2.4.4 Experiments on Real Data.......................... 49
2.4.5 Observations of Object Stop-by Behavior on Real Data . . . . . . . . . . 52
2.5 Related Work of Learning Spatial-Temporal Stop-by Behaviors . . . . . . . . . . 56
2.6 Summary of Learning Spatial-Temporal Stop-by Behaviors . . . . . . . . . . . . 60
3 Learning Temporal Worship Behaviors with Heterogeneous Symbolic Graph in Celebrity-Dived Networks....................................... 61
3.1 Introduction of Learning Temporal Worship Behaviors . . . . . . . . . . . . . . 61
3.2 Preliminaries of Learning Temporal Worship Behaviors . . . . . . . . . . . . . . 65
3.2.1 Problem Formulation............................. 65
3.2.2 Analysis of User-Follow-Celebrity Relation . . . . . . . . . . . . . . . . . 66
3.3 Framework of Learning Temporal Worship Behaviors . . . . . . . . . . . . . . . 71
3.3.1 Relational Property based Factor Graph .................. 71
3.3.2 Gaussian Estimation based Learning .................... 75
3.3.3 Active Learning................................ 78
3.4 Related Work of Learning Temporal Worship Behaviors . . . . . . . . . . . . . . 80
3.5 Experimental Results of Learning Temporal Worship Behaviors . . . . . . . . . 82
3.5.1 Experimental Setup.............................. 82
3.5.2 Evaluation of RPG .............................. 85
3.5.3 Evaluation of Active Learning........................ 88
3.5.4 Execution Time Analysis........................... 93
3.6 Summary of Learning Temporal Worship Behaviors . . . . . . . . . . . . . . . . 95
4 Learning Temporal Rating Behaviors with Dependent Symbolic Graphs in E-Learning Systems ........................................... 97
4.1 Introduction of Learning Temporal Rating Behaviors . . . . . . . . . . . . . . . 97
4.2 Problem Definition ..................................100
4.3 DataAnalysis of Learning Temporal Rating Behaviors . . . . . . . . . . . . . . 101
4.3.1 Dataset ....................................102
4.3.2 Analysis on Links of Concept Graphs....................102
4.4 Framework of Learning Temporal Rating Behaviors . . . . . . . . . . . . . . . . 104
4.4.1 Multi-Armed Bandit Formulation ......................104
4.4.2 Learning Latent Factors ...........................106
4.4.3 Regret Analysis................................111
4.5 Experimental Results of Learning Temporal Rating Behaviors . . . . . . . . . . 113
4.5.1 Dataset and Experimental Setup ......................115
4.5.2 Interactive Recommendation Performance . . . . . . . . . . . . . . . . . 116
4.5.3 Capability of Handling Cold-Start Users ..................117
4.5.4 Parameter Sensitivity.............................117
4.5.5 Diversity of Recommended Questions....................118
4.5.6 Capability of Exploring Unknown Unknowns . . . . . . . . . . . . . . . . 122
4.6 Related Work of Learning Temporal Rating Behaviors. . . . . . . . . . . . . . . 123
4.7 Summary of Learning Temporal Rating Behaviors . . . . . . . . . . . . . . . . . 124
5 Conclusions .........................................125
Reference ............................................127
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