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系統識別號 U0026-1208202016065900
論文名稱(中文) 結合知識圖與屬性注意力機制以增強推薦系統的可解釋性
論文名稱(英文) Integrate Knowledge Graph and Attribute Attention Mechanism to Achieve Explainable Recommendation System
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 108
學期 2
出版年 109
研究生(中文) 林怡瑄
研究生(英文) Yi-Syuan Lin
電子信箱 linlisa0601@iir.csie.ncku.edu.tw
學號 P76074517
學位類別 碩士
語文別 英文
論文頁數 57頁
口試委員 指導教授-蔣榮先
口試委員-張瑞紘
口試委員-郝沛毅
口試委員-鄞宗賢
口試委員-楊家融
中文關鍵字 推薦系統  可解釋推薦系統  知識圖譜  深度學習 
英文關鍵字 Recommendation System  Explainable Recommendation System  Knowledge Graph  Deep Learning 
學科別分類
中文摘要 推薦系統扮演著替使用者篩選需要資訊的角色,近年來的研究顯示使用深度學習的方法,可以更好的去預測使用者喜好的物品,深度學習模型會自己去調整參數的數值,然而這些由模型自己學出來的參數有著不可解釋的問題,這也讓人們很難了解模型背後推薦的機制,難以對模型進行近一步的分析。
為了進一步提升推薦的效能,近年來開始有研究將知識圖納入了推薦之中,其中一類研究只用知識圖來拿到更多的物品特徵,沒有去解決不可解釋的問題,另一類的研究沒有去學習知識圖的表達以及背景知識,直接將知識圖中的路徑作為模型的輸入,在知識圖的資訊有所遺漏時,會無法做出好的推薦。還有一類研究在解釋上面注重在知試圖的關係層面上,因此當有圖中多個不同的屬性與商品或使用者屬於同一個關係時,模型只能解釋在推薦時考慮的關係比重,而不能在屬性層面上做出解釋。
基於上述推薦系統的限制,本研究提出一個結合知識圖以及擁有屬性可解釋性的推薦系統,由知識圖的推論中得到屬性的解釋,因此當模型推薦進行推薦時,可以說明商品被推薦的原因,是因為模型考慮了商品或是使用者本身的哪些特性。此外,為了解決前述屬性遺失的問題,本研究提出的模型除了學習知識圖的表達以及背景知識以外,還會根據目前學習到的知識去預測知識圖中遺失的資訊,並將資訊加入推薦的考量當中。
為了驗證本研究的有效性,我們採用了兩個不同領域的資料集,並分別實驗模型在使用者喜好預測與遺失屬性預測上的效能,由實驗證明,本研究提出模型在不同的資料集以及不同的任務中,預測的結果優於先前的推薦系統的模型(BPR、NeuMF)、知識圖預測模型(TransE、TransH、SimplE)以及其他結合知識圖的推薦模型(KTUP)。
英文摘要 In the era of information explosion, recommendation systems play an important role for each user in many modern Internet services. Studies used ML/DL method to predict user preference in recent years. However, those recommendation systems have the problem of being unexplainable. The problem causes human distrust of the model because humans cannot understand the mechanism within.
For enhancing recommendation performance, recent studies unify the knowledge graph (KG) in recommended systems to get better explanations. Some studies use KG as a new method to get extra features of items, but they don’t solve the unexplainable problem. Some studies use the existing path in KG as the input of the model to predict user preference. These studies didn’t learn knowledge graph representations or have the room for improvement when the knowledge is missing in KG. Another study learns KG representation and unifies KG in the recommendation. However, this method focuses on producing relation explanation on KG. Under this circumstance, when different attributes are connected to the same relation, the model can’t tell which attribute is more important.
In our study, we build the KG-based recommendation system with the attribute explanation. When generating recommendation, our model can show the recommended logic to illustrate which attribute is more important by the inference of KG. In addition, we can predict missing attributes through background knowledge and consider the missing information in recommendation.
To evaluate the overall performance of our model, we design some experiments on separate tasks of item recommendation and attribute prediction. Two different domain datasets are used to show that our method is robust on different domains. The performance of our model is better than the previous recommendation models (BPR, NeuMF), KG models (TransE, TransH, SimplE), and recommendation model unifying KG (KTUP).
論文目次 中文摘要 I
Abstract III
Acknowledgement V
Contents VII
List of Tables IX
List of Figures X
Chapter 1 Introduction 1
1.1 Background and Motivation 1
1.2 Research Objectives 3
1.3 Thesis Organization 4
Chapter 2 Relative Work 5
2.1 Knowledge Graph (KG) 5
2.2 KG Embedding 6
2.2.1 Translation Base 7
2.2.2 Canonical Polyadic (CP) Decomposition Method 8
2.2.3 Other Method 8
2.3 Recommendation System 9
2.3.1 Item Recommendation 9
2.3.2 KG Based Recommendation 10
Chapter 3 KG Representation in AtriUP 12
3.1 Overview 12
3.2 KG Embedding of User, Item and Attribute 13
3.3 SimplE for KG Representation 15
Chapter 4 AtriUP Recommendation System 18
4.1 Recommendation Enhancement Model 18
4.2 Attribute Attention Mechanism 21
4.3 Loss Function of AttriUP 24
4.4 Ranking and Recommendation 25
Chapter 5 Experiments 27
5.1 Datasets and Preprocessing Methods 27
5.1.1 MovieLens-1M Dataset 27
5.1.2 KKBOX Dataset 29
5.2 Experimental Design 30
5.2.1 Evaluation Metrics 30
5.2.2 . Parameter Setting 31
5.3 Baseline Model 32
5.4 Experiment Result 33
5.4.1 Item Recommendation 34
5.4.2 Attribute Prediction 36
5.4.3 Hyper-Parameter Analyses 40
5.5 Case Study 45
Chapter 6 Conclusion and Future Work 51
6.1 Conclusion 51
6.2 Future Work 52
Reference 54

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