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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98581完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 魏志平 | zh_TW |
| dc.contributor.advisor | Chih-Ping Wei | en |
| dc.contributor.author | 白日明 | zh_TW |
| dc.contributor.author | Jih-Ming Pai | en |
| dc.date.accessioned | 2025-08-18T00:57:39Z | - |
| dc.date.available | 2025-08-18 | - |
| dc.date.copyright | 2025-08-15 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-08-05 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98581 | - |
| dc.description.abstract | 在推薦系統中，如何同時兼顧準確性與多樣性一直是長期存在的挑戰。雖然多樣化方法能提升內容的多元性，但常忽略使用者對多樣性的個別偏好，導致推薦結果不符合使用者的興趣，降低使用者滿意度。近期個人化的多樣化方法研究試圖透過建模使用者的多樣性偏好來解決此問題，但大多數方法仍依賴於後處理的優化策略，將使用者的多樣性偏好視為外部訊號，於分離的第二階段根據此資訊重新排序推薦結果。
本研究提出一種全新的多目標學習架構，將細緻的多樣性偏好內嵌至訓練過程中。透過設計多樣性偏好建模任務，以建構使用者對商品類別屬性的興趣分布，我們的方法能學得同時捕捉「相關性」與「多樣性偏好」的豐富表徵。 在真實世界資料集上的實驗結果顯示，我們的方法不僅能同時提升準確性與個人化多樣化表現，亦能有效緩解兩者間的權衡困境。這些成果突顯了將多樣性偏好視為可學習訊號的重要性，為打造更具適應性與使用者導向的推薦系統開啟了新的方向。 | zh_TW |
| dc.description.abstract | Balancing accuracy and diversity has been a long-standing challenge in recommender systems. While diversification methods enrich content variety, they often ignore user-specific preferences for diversity, leading to irrelevant recommendations and degraded user satisfaction. Recent efforts in personalized diversification attempt to address this by modeling users’ diversity preferences, but most rely on post-processing heuristics or external signals detached from core user modeling. In this work, we propose a novel multi-objective learning framework that internalizes fine-grained diversity preferences directly into the training process. By designing diversity preference modeling tasks that model user interest distributions across multiple item attributes, our method learns richer user/item representations that jointly capture both relevance and diversity preferences. Extensive experiments on a real-world dataset demonstrate that our approach not only improves accuracy and diversification personalization simultaneously, but also mitigates the typical trade-off between them. Our results highlight the value of treating diversity preference as a learnable signal, opening new possibilities for more adaptive, user-centric recommender systems. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-08-18T00:57:39Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-08-18T00:57:39Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 致謝 i
摘要 ii Abstract iii Table of Contents iv List of Figures vii List of Tables viii Chapter 1 Introduction 1 1.1 Background 1 1.2 Research Motivation 4 1.3 Research Objectives 6 Chapter 2 Literature Review 8 2.1 Diversity in Recommender System 8 2.1.1 Recommendation Diversification Methods 8 2.1.2 Personalized Diversification Methods 11 2.2 Representation Learning in Recommender System 15 2.2.1 User-Item Representation Learning 15 2.2.2 Multi-objective Learning for User/Item Representations 16 2.3 Summary 17 Chapter 3 Methodology 18 3.1 Problem Formulation 18 3.2 Overview of the Proposed Methods 19 3.2.1 Multi-task Learning (MTL) 20 3.2.2 Finetuning 20 3.3 Design of Diversity Preference Modeling Tasks 22 3.3.1 Diversity Preference Matching (DPM) 23 3.3.2 Diversity Preference Score Prediction (DPS) 25 3.2.3 Diversity Preference Regularization (DPR) 27 3.4 Loss Weighting Strategy 28 3.4.1 Static Weighting 28 3.4.2 Dynamic Weighting 29 Chapter 4 Experiments 31 4.1 Data Collection 31 4.2 Benchmark Design 34 4.3 Evaluation Procedure 36 4.3.1 Experimental Data 36 4.3.2 Evaluation Protocol 37 4.3.3 Hyperparameter Settings 39 4.4 Experimental Results 40 4.4.1 RQ1: How do our methods perform? 41 4.4.2 RQ2: How does each proposed task contribute? 45 4.4.3 RQ3: How do different model designs and configurations affect performance? 47 4.4.4 RQ4: How does our method generalize across different base recommendation models? 50 4.4.5 RQ5: What is the qualitative impact of our method? 53 Chapter 5 Conclusion 59 5.1 Conclusion 59 5.2 Future Works 60 References 62 | - |
| dc.language.iso | en | - |
| dc.subject | 個人化多樣化 | zh_TW |
| dc.subject | 推薦系統 | zh_TW |
| dc.subject | 多目標優化 | zh_TW |
| dc.subject | 表徵學習 | zh_TW |
| dc.subject | 多樣性偏好 | zh_TW |
| dc.subject | Diversity Preference | en |
| dc.subject | Representation Learning | en |
| dc.subject | Multi-objective Optimization | en |
| dc.subject | Personalized Diversification | en |
| dc.subject | Recommender Systems | en |
| dc.title | 以表徵學習方法實現考量多樣性偏好的推薦系統 | zh_TW |
| dc.title | Learning to Personalized Diversification: A Representation Learning Approach to Diversity Preference-Aware Recommendation | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 向倩儀;楊錦生 | zh_TW |
| dc.contributor.oralexamcommittee | Chien-Yi Hsiang;Chin-Sheng Yang | en |
| dc.subject.keyword | 推薦系統,個人化多樣化,多樣性偏好,表徵學習,多目標優化, | zh_TW |
| dc.subject.keyword | Recommender Systems,Personalized Diversification,Diversity Preference,Representation Learning,Multi-objective Optimization, | en |
| dc.relation.page | 71 | - |
| dc.identifier.doi | 10.6342/NTU202503366 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-08-08 | - |
| dc.contributor.author-college | 管理學院 | - |
| dc.contributor.author-dept | 資訊管理學系 | - |
| dc.date.embargo-lift | 2025-08-18 | - |
| 顯示於系所單位： | 資訊管理學系 | |
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