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Author: 鄒承翰
Cheng-Han Chou
Thesis Title: 基於文字之語意分析及偏好汲取用於資料非均衡問題
Semantic Analysis and Preference Capturing onData Imbalance Problem
Advisor: 戴碧如
Bi-Ru Dai
Committee: 戴志華
Chih-Hua Tai
沈之涯
Chih-Ya Shen
陳怡伶
Yi-Ling Chen
Degree: 碩士
Master
Department: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
Thesis Publication Year: 2021
Graduation Academic Year: 109
Language: 英文
Pages: 46
Keywords (in Chinese): 推薦系統注意力機制深度學習資料非均衡
Keywords (in other languages): Recommendation System, Attention, Deep Learning, Data Imbalance
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在現今社會中人們每天都會收到大量的信息。然而,他們只對符合他們偏好的信息感興趣。因此,檢索此類信息成為一項重要任務。基於矩陣分解 (MF) 的方法在推薦任務上取得了相當好的表現。然而,基於 矩陣分解 的方法存在幾個關鍵的問題,例如冷啟動問題和數據稀疏性問題。為了解決上述提到的問題,有許多獲得出色表現的推薦模型被提出。儘管如此,我們認為沒有一個更全面的架構可以通過汲取用戶偏好和項目趨勢來提高模型的表現。因此,我們提出了一種解決上述問題的新方法。在這個嶄新的框架中採用了分層結構。此外,還提出並試驗了額外的採樣技術,以提高所提出模型的性能。我們所提出的模型性能優於最先進的模型並且在幾個現實世界的數據集上取得優異的表現。實驗結果驗證了我們的框架即使在稀疏數據下也能提取有用的特徵。


Nowadays, people receive an enormous amount of information from day to day. However, they are only interested in information which matches their preferences. Thus, retrieving such information becomes an significant task. Matrix Factorization (MF) based methods achieve fairly good performances on recommendation tasks and in our case, reviews on e-commerce platforms. However, there exist several crucial issues with MF-based methods such as cold-start problems and data sparseness. In order to address the above issues, numerous recommendation models are proposed which obtained stellar performances. Nonetheless, we figured that there is not a more comprehensive framework that enhances its performance through retrieving user preference and item trend. Hence, we propose a novel approach to tackle the aforementioned issues. A hierarchical construction is employed in this proposed framework. Furthermore, additional sampling techniques are proposed and experimented in order to enhance the performance of the proposed model. The performance excels in comparison to state-of-the-art models by testing on several real-world datasets. Experimental results verified that our framework can extract useful features even under sparse data.

Recommendation Letter Approval Letter Abstract in Chinese Abstract in English Acknowledgements Contents List of Figures List of Tables List of Algorithms 1. Introduction 2. Related Works 2.1 Traditional Recommendation Systems 2.2 Recommendation Systems with Neural Networks 2.3 Data Imbalance 2.3.1 Data-Level Methods 2.3.2 Algorithms-Level Methods 2.3.3 Hybrid Methods 3. The Proposed FrameWork 3.1 User-Item Correlation Module 3.1.1 Embedding Lookup Layer 3.1.2 Co-Attention Review Filtering Layer 3.1.3 User Preference/Item Trend Extraction 3.1.4 Attentive Correlation Layer 3.2 Factorization Machine 3.3 Ratio Sampling 3.4 Model Composition Overview and Design Summary 4. Experiments 4.1 Experimental Settings 4.2 Comparison Methods 4.3 Experiment Results and Performance Discussion 4.4 Ablation Studies 4.5 Data Imbalance 4.5.1 Sampling Techniques 4.5.2 Objective Function 5. Conclusions References Letter of Authority

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