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姓名	鍾昌桓(Chang-Huan Zhong)  查詢紙本館藏	畢業系所	資訊管理學系
論文名稱	(T3CRec: A Contextual Awareness Transformer-Based Recommendation System)
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檔案	[Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2028-7-1以後開放)
摘要(中)	隨著電子商務蓬勃興起，網路上的項目種類亦持續增加，使用者搜尋符合需求和偏好的項目時，經常需投入大量時間與精力。在這種情況下，推薦系統能藉由提供專屬且量身訂作的推薦，以實現商業目標，顯示出其不可或缺的重要性。因此，我們在本研究中介紹一種創新的推薦模型，名為 T3CRec，以解決現有方法常常無法有效捕捉到用戶與項目互動的更全面的上下文與特性。透過將項目類別、用戶特性及項目流行度等三種上下文因素融合至基於變壓器的架構中，T3CRec 能提供更具多樣性和個人化的推薦。我們將項目類別整合到項目的表徵中，並利用矩陣分解的方法，將用戶與項目的互動資訊映射到低維空間以進行資料增強。此外，我們引進了一種新穎的計算項目流行度的方式，考量用戶與項目之間的平均互動間隔的項目熱門度。通過在真實數據集上進行的廣泛實驗，我們證明了我們的模型在所有評價指標上優於現有的最先進的推薦模型，並平均提升 2.63% 推薦性能。
摘要(英)	As e-commerce thrives, the variety of online items continues to surge, with users often requiring users to invest significant time and energy in searching for items that meet their needs and preferences. In this scenario, the indispensable role of recommendation systems becomes evident, as they can deliver tailored recommendations to achieve business goals. Therefore, in this paper, we introduce an innovative recommendation model named T3CRec, designed to overcome the common inability of existing methods to effectively capture a more comprehensive context and characteristics of user-item interactions. By incorporating three contextual factors - item category, user characteristics, and item popularity - into a transformer-based framework, T3CRec can provide more diverse and personalized recommendations. We integrate item categories into item representations and use matrix factorization to map user-item interaction information into a low-dimensional space for data augmentation. Furthermore, we propose a novel way to compute item popularity, considering the average interaction interval between users and items. Through extensive experiments on real-world datasets, we demonstrated that our model outperforms the existing state-of-the-art recommendation models across all evaluation metrics, achieving an average improvement of 2.63% in recommendation performance.
關鍵字(中)	★ 推薦系統 ★ 變壓器 ★ 深度學習	關鍵字(英)	★ Recommendation System ★ Transformer ★ Deep Learning
論文目次	摘 要 i Abstract ii Table of Contents iii List of Figures iv List of Tables v 1. Introduction 1 2. Related Work 7 2.1 Traditional Recommendation 7 2.2 Sequential Recommendation 9 2.3 Popularity 11 3. Proposed Method 12 3.1 Categorical Context Attachment & Personality Context Augmentation 13 3.2 T3CRec Constraining 15 3.3 Popularity Context Customization & Prediction 18 4. Experiments and Evaluation 20 4.1 Evaluation Metrics and Baseline Models 21 4.2 Performance Comparison 23 4.3 Balance Weight Significance Discussion 26 4.4 Dimensionality Setting Analysis 28 4.5 Max Length Influence Discussion 30 4.6 Ablation Study 32 4.7 Case Study 33 5. Conclusion 35 Reference 36
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指導教授	陳以錚(Yi-Cheng Chen)	審核日期	2023-7-20
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