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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳建錦(Chien-Chin Chen) | |
| dc.contributor.author | Rong-Peng Yang | en |
| dc.contributor.author | 楊鎔篷 | zh_TW |
| dc.date.accessioned | 2021-06-07T17:41:20Z | - |
| dc.date.copyright | 2020-08-25 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-07-24 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/15504 | - |
| dc.description.abstract | 在這個資訊爆炸的時代，推薦系統在人們的生活中扮演很重要的角色，能有效地幫助人們在眾多的選擇中，快速地找到感興趣的商品。近年來，因為深度學習非線性轉換的能力，且在很多領域像是電腦視覺、語音辨識和自然語言處理都獲得重大的成功，許多研究開始使用深度學習設計推薦系統。本論文提出一個結合降噪自動編碼器（Denoising Autoencoder, DAE）與注意力機制（Attention Mechanism）的推薦方法。模型利用降噪自動編碼器產生使用者的特徵代表，解決資料稀疏的問題，再利用注意力機制計算商品評分資訊對於使用者的影響程度，讓模型針對每位使用者不同的商品評分資訊，調整使用者的特徵代表，產生更準確的個人化推薦。實驗的資料集使用MovieLens 1M的資料集，進行個人化的電影推薦，實驗結果也顯示本論文提出的模型比現行的模型表現來得好。 | zh_TW |
| dc.description.abstract | In the era of information explosion, the recommendation system plays an important role in people's lives. It can effectively help people find products that interest them with having a lot of choices. In recent years, many studies have begun to apply deep learning to recommendation systems due to its nonlinear modeling capacity and significant success in other domains such as computer vision, speech recognition and natural language processing. In this paper, we propose a user’s recommendation method combining DAE vector dimension compression and attention mechanism. The model uses DAE to generate the hidden representation of user’s preference and solves the problem of data sparsity. The attention mechanism is utilized to encode the user’s rated items information into the hidden representation of user’s preference, which incorporates personalized rated items information with each user’s preference to provide more accurate personalized recommendations. Experiments conducted on real-world datasets from MovieLens 1M on movie recommendations demonstrate the effectiveness of our proposed model over the state-of-the-art recommendation systems. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-07T17:41:20Z (GMT). No. of bitstreams: 1 U0001-1407202002051400.pdf: 2678819 bytes, checksum: 2856f073bf36afa3c9aa48a36a440553 (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 誌謝 i 摘要 ii Abstract iii 目錄 iv 圖目錄 v 表目錄 vi 第一章 緒論 1 第二章 文獻探討 5 2.1 現行基於深度學習的推薦系統介紹 5 2.2 自動編碼器介紹 9 2.3 現行基於自動編碼器的推薦系統介紹 10 2.4 現行使用attention機制的推薦系統介紹 16 第三章 研究方法 18 3.1 DAE的基本架構 18 3.2 DAE與基於使用者協同過濾法推薦系統的連結 19 3.3 DAE搭配商品評分資訊與attention機制調整使用者embedding進行推薦 22 第四章 實驗 26 4.1 資料集與評估方法介紹 26 4.2 實驗架構介紹 26 4.3 自身模型比較 29 4.3.1 有無attention機制的影響 30 4.3.2 有無商品評分資訊的影響 30 4.4 其他模型比較 31 第五章 結論與未來展望 33 參考文獻 34 | |
| dc.language.iso | zh-TW | |
| dc.subject | 向量壓縮 | zh_TW |
| dc.subject | 推薦系統 | zh_TW |
| dc.subject | 降噪自動編碼器 | zh_TW |
| dc.subject | 注意力機制 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | Denoising Autoencoders | en |
| dc.subject | Deep Learning | en |
| dc.subject | Vector Compression | en |
| dc.subject | Recommendation Systems | en |
| dc.subject | Attention Mechanism | en |
| dc.title | 結合DAE向量維度壓縮與attention機制之使用者推薦方法 | zh_TW |
| dc.title | The User’s Recommendation Method Combining DAE Vector Dimension Compression and Attention Mechanism | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳孟彰(Meng-Chang Chen),張詠淳(Yung-Chun Chang) | |
| dc.subject.keyword | 推薦系統,降噪自動編碼器,注意力機制,向量壓縮,深度學習, | zh_TW |
| dc.subject.keyword | Recommendation Systems,Denoising Autoencoders,Attention Mechanism,Vector Compression,Deep Learning, | en |
| dc.relation.page | 38 | |
| dc.identifier.doi | 10.6342/NTU202001492 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2020-07-26 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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