臺灣博碩士論文加值系統

隨著近年來深度學習模型在各行各業的發展與成功，各式利用深度學習的推薦應用也精彩紛呈。然而，應用複雜模型的推薦系統雖然準確率大幅提升，卻缺乏可解釋性，降低使用者的信任及滿意度，進而降低推薦系統的價值。因此，模型如何在高準確度與可解釋性之間取得平衡，甚至是在取得高精確度的情形下保有解釋力，成為推薦系統領域越來越熱門的議題。

在此篇論文中，我們面臨的問題為：希望能準確預測並推薦投資人下個月購買的基金，並附有推薦的解釋。為此，本論文利用知識圖譜的架構特性，利用深度學習技術將顧客與基金及其他解釋因子嵌入為特徵向量，使用知識圖譜的翻譯式模型進行訓練，將傳統網路無法學習的結構性知識加入，取得個人化的基金推薦及解釋。此外，我們考慮到基金推薦的特性，改變訓練方式，進一步將單步的解釋拓展為複雜解釋，並採用各解釋的稀有性建立另一個解釋指標，幫助使用者在解釋力與稀有度中取得理想的客製化解釋。最後，我們認為基於知識圖譜的架構具有非常彈性的使用方式，並藉此延伸出一系列特殊推薦的應用。

透過基金實際購買資料集的實證，我們證實此架構能有效的提供高準確度的推薦，且由知識圖譜中關係的抽換證實本架構能夠有效學習結構性知識，同時驗證加入顧客與基金的特徵資料有助於模型的嵌入學習。最後，我們透過數個解釋範例呈現一般解釋、複雜解釋及特殊推薦的結果，展現本架構在解釋上的效力。

Since deep learning based models have gained success in various fields during recent years, many recommendation systems also start to take advantage of the deep learning techniques. However, while the deep learning based recommendation systems have achieved high recommendation performance, the lack of interpretability may reduce users' trust and satisfaction, while limiting the model to wide adoption in the real world. As a result, to strike a balance between high accuracy and interpretability, or even obtain both of them at the same time, has become a popular issue among the researches of recommendation systems.

In this thesis, we would like to predict and recommend the funds that would be purchased by the customers in the next month, while providing explanations simultaneously. To achieve the goal, we leverage the structure of knowledge graph, and take advantage of deep learning techniques to embed customers and funds features to a unified latent space. We fully utilize the structure knowledge which cannot be learned by the traditional deep learning models, and get the personalized recommendations and explanations. Moreover, we extend the explanations to more complex ones by changing the training procedure of the model, and proposed a measure to rate for the customized explanations while considering strength and uniqueness of the explanations at the same time. Finally, we regard that the knowledge graph based structure could be extended to other applications, and proposed some possible special recommendations accordingly.

By evaluating on the dataset of mutual fund transaction records, we verify the effectiveness of our model to provide precise recommendations, and also evaluate the assumptions that our model could utilize the structure knowledge well. Last but not least, we conduct some case study of explanations to demonstrate the effectiveness of our model to provide usual explanations, complex explanations, and other special recommendations.

摘要 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Background and Problem Setting . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Research Goal and Contributions . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1 Explainable Artificial Intelligence (XAI) Approaches . . . . . . . . . . . . . . 7
2.1.1 Global Intepretability Approaches . . . . . . . . . . . . . . . . . . . . 7
2.1.2 Local Interpretability Approaches . . . . . . . . . . . . . . . . . . . . 8
2.1.3 Transparent Model Design Approaches . . . . . . . . . . . . . . . . . 9
2.2 Recommender System Approaches . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2.1 Collaborative Filtering Approaches . . . . . . . . . . . . . . . . . . . 10
2.2.2 Deep Learning Based Approaches . . . . . . . . . . . . . . . . . . . . 11
2.2.3 Knowledge Graph Embeddings Approaches . . . . . . . . . . . . . . . 13
2.3 Explainable Recommendation Systems Approaches . . . . . . . . . . . . . . . 14
2.3.1 Explainble Recommender Systems . . . . . . . . . . . . . . . . . . . . 14
2.3.2 Display Types of Explainable Recommender System . . . . . . . . . . 16
2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3 Proposed Knowledge Graph-based Recommendation . . . . . . . . . . . . . . . . 19
3.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.2 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.3 Knowledge Graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.3.1 Introduction of Knowledge Graph . . . . . . . . . . . . . . . . . . . . 22
3.3.2 Overview of Translation Based Models . . . . . . . . . . . . . . . . . 23
3.3.3 Entities and Relations in our Framework . . . . . . . . . . . . . . . . . 26
3.4 Deep Neural Network Embeddings . . . . . . . . . . . . . . . . . . . . . . . . 26
3.5 Learning of Recommendation with Deep Neural Knowledge Graph Embeddings 29
3.5.1 Recommendation of Deep Neural Network Knowledge Graph Embeddings
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.5.2 Training Procedure of Our Framework . . . . . . . . . . . . . . . . . . 31
3.5.3 Recommendation of Our Framework . . . . . . . . . . . . . . . . . . 33
4 Explainable Mutual Fund Recommendation System . . . . . . . . . . . . . . . . 35
4.1 Recommendation Explanation with Knowledge Graph . . . . . . . . . . . . . 35
4.2 Composition of Explanations . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.2.1 Training of the Composition of Explanations . . . . . . . . . . . . . . 38
4.2.2 Generating Composition of Explanations . . . . . . . . . . . . . . . . 41
4.3 Recommendation Explanation Concerning Rarity . . . . . . . . . . . . . . . . 41
4.3.1 Computing Uniqueness of Explanations . . . . . . . . . . . . . . . . . 42
4.3.2 Rating for Explanations . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.4 Extended Applications of Explainable Recommendation . . . . . . . . . . . . 46
5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
5.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
5.1.1 Data Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
5.1.2 Data Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
5.1.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
5.2 Performance of Knowledge Graph Based Recommendation System . . . . . . 53
5.2.1 Experimental Settings . . . . . . . . . . . . . . . . . . . . . . . . . . 53
5.2.2 Parameter Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . 54
5.2.3 Performance Evaluation of Knowledge Graph Based Recommendation
System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.3 Recommendation Performance of Different Relations Sets With Entity Features 61
5.3.1 Embedding Without Entity Description . . . . . . . . . . . . . . . . . 62
5.3.2 Embedding With Entity Description . . . . . . . . . . . . . . . . . . . 63
5.4 System Demonstration of Recommendation with Explanations . . . . . . . . . 65
5.4.1 Recommendation with Explanations . . . . . . . . . . . . . . . . . . . 66
5.4.2 Recommendation with Composition of Explanations . . . . . . . . . . 67
5.4.3 Composition of Explanations with Ratings . . . . . . . . . . . . . . . 68
5.4.4 Recommendation Performance of Explanations Composition . . . . . . 71
5.5 Demonstration of Extended Applications . . . . . . . . . . . . . . . . . . . . . 72
6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

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