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研究生: 王樸
Pu Wang
論文名稱: 基於原理的具注意力之遮罩變分自編碼器應用於增強知識感知推薦
Enhancing Knowledge-Aware Recommendations with Rationale-based Attentive Masked Variational Autoencoders
指導教授: 戴碧如
Bi-Ru Dai
口試委員: 沈之涯
Chih-Ya Shen
戴志華
Chih-Hua Tai
陳怡伶
Yi-Ling Chen
戴碧如
Bi-Ru Dai
學位類別: 碩士
Master
系所名稱: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
論文出版年: 2024
畢業學年度: 112
語文別: 英文
論文頁數: 80
中文關鍵詞: 推薦系統協同過濾變分推斷最大似然估計證據下界圖神經網路注意力機制自監督式學習自動編碼器
外文關鍵詞: Recommender system, Collaborative filtering, Variational inference, Maximum likelihood estimation, Evidence lower bound, Graph neural networks, Attention mechanism, Self-supervised learning, Autoencoder
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Abstract in Chinese iii Abstract in English iv Acknowledgements v Contents vi List of Figures x List of Tables xii 1 Introduction 1 2 Related Works 6 2.1 Traditional Recommendation System 6 2.1.1 Content-Based Recommendation System 6 2.1.2 Collaborative Filtering Recommendation System 7 2.2 Graph-Based Recommendation System 8 2.3 Knowledge-Aware Recommendation System 10 2.3.1 Embedding-Based Methods 10 2.3.2 Path-Based Methods 11 2.3.3 Hybrid Methods 11 2.4 Self-Supervised Recommendation System 12 2.4.1 Autoencoder-Based Recommendation System 13 2.4.2 Contrastive Learning-Based Recommendation System 15 3 Proposed Method 17 3.1 Problem Formulation 20 3.2 Overall Framework of RAMVAE 22 3.3 Rationale-Aware Masking 24 3.3.1 Rationale Score Calculation 25 3.3.2 Masking via Rationale Scores 25 3.4 Heterogeneous Graph Aggregation 27 3.4.1 Aggregation on Knowledge Graph 28 3.4.2 Aggregation on user-item interaction graph 29 3.5 Rationale-Based Attentive Variational Masked Autoencoder 30 3.5.1 Evidence Lower Bound 31 3.5.2 Stochastic Encoding of Graphs 34 3.5.3 Reconstruction Process 36 3.6 Model Learning 37 4 Experiments 39 4.1 Experimental Settings 39 4.1.1 Datasets 39 4.1.2 Evaluation Metrics 41 4.1.3 Baselines 43 4.1.4 Hyperparameter Settings 46 4.2 Performance Comparison 47 4.3 Ablation Study 50 4.3.1 Ablation Study of the ELBO 50 4.3.2 Ablation Studies of VI and Parameter-Sharing Strategy 51 4.4 Performance under Different Sparsity 52 4.5 Time Complexity Analysis 54 4.6 Case Study 55 5 Conclusions and Future Works 58 5.1 Conclusions 58 5.2 Future Works 59 References 60 Appendix A Trade-off of Negative Sample Numbers 69 Appendix B RAMVAECL 71 B.1 Knowledge-Aware Contrastive Learning 72 B.1.1 Graph Augmentation via Rationale Scores 72 B.1.2 Contrastive Learning with Cross-View Item Embeddings 74 B.1.3 Model Learning 77 B.2 Experiments 77 B.2.1 Performance Comparison with RAMVAE 78 B.2.2 Ablation Studies of VI and Parameter-Sharing Strategy 79 B.2.3 Effect of Layer Numbers 79 B.3 Time Complexity Analysis 80

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