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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/82570完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳建錦(Chien Chin Chen) | |
| dc.contributor.author | Chih-Yun Chen | en |
| dc.contributor.author | 陳芝妘 | zh_TW |
| dc.date.accessioned | 2022-11-25T07:46:59Z | - |
| dc.date.available | 2023-08-01 | |
| dc.date.copyright | 2021-11-17 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-07-07 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/82570 | - |
| dc.description.abstract | 隨著網際網路快速的發展，促進網路服務的多元開發，也讓現代人的生活越來越依賴網路服務平台，其中，對網路服務平台供應商而言，是否能獲得新進使用者的青睞是影響收益的關鍵因素。由於傳統的推薦系統演算法是基於使用者行為來進行產品推薦，在面對新進使用者(new users)時會因為只有他們少許的行為資料，推薦演算法難以從中分析出使用者興趣，而此問題被稱為「新進使用者冷啟動問題」(new user cold-start problem)，這樣的情境使得推薦系統難以了解新進使用者的興趣，更降低了推薦的效果。過去的研究試圖使用額外的資訊來解決新進使用者冷啟動問題，例如：使用者的性別、職業或社群網路資訊等等，但是資訊隱私與個人資訊保護使得先前的研究無法應用。 在本篇論文中，我們提出了一個端到端(end-to-end)基於生成式對抗網路(GAN)的推薦演算法來緩解使用者冷啟動的問題，此方法不需使用到額外的資訊，此方法由兩個神經網路組成：生成器與判別器，利用豐富的使用者資訊來訓練生成器，而生成器會學習模擬新進使用者變成豐富使用者的評分分佈，同時，判別器會負責分辨生成器模擬出的資訊與真實資訊，最後訓練完成的生成器就可以替冷啟動使用者進行推薦。此外，我們設計了「返老還童」機制來將豐富使用者還原到他剛加入平台時的冷啟動狀態，根據在三個不同領域資料集的實驗結果，我們提出的方法都遠遠優於其他冷啟動推薦演算法。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-25T07:46:59Z (GMT). No. of bitstreams: 1 U0001-0707202109463000.pdf: 2247951 bytes, checksum: 3b742d432abc6ba77f2b55f52a9a3091 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員會審定書………………………….………………………………….…… i 誌謝…………………………….……….…………………………………………... ii 中文摘要……………………………..……………………………………………… iii ABSTRACT ………………………………………………………………………… iv 1 Introduction…………………………………………………………………….... 1 2 Related Works………………………………………………………………........ 4 2.1 Collaborative Filtering-based Recommendation……..……...……......…. 4 2.2 GAN-based Recommendation………………………………...…………. 7 2.3 New User Cold-Start Recommendation………...…….………………...… 9 3 The Proposed Model….……………………………………………………….... 12 3.1 GAN Training……..……………………….………………….………..… 14 3.2 The Cold-Start Recommendation Generation.……………..…………..… 18 4 Experiments…………………………………………………………….………. 18 4.1 Experiment settings………………………..………………...………….. 19 4.2 Examination of System Components…………………...…...………….. 23 4.3 Comparison with Other Methods………………………..……..……….. 28 5 Conclusions……………………………………………………….……………. 31 Reference………...…………………………………………………….……………. 33 | |
| dc.language.iso | en | |
| dc.subject | 對抗式生成網路 | zh_TW |
| dc.subject | 推薦系統 | zh_TW |
| dc.subject | 冷啟動使用者問題 | zh_TW |
| dc.subject | Recommendation Systems | en |
| dc.subject | Generative Adversarial Networks | en |
| dc.subject | New User Cold-Start Problem | en |
| dc.title | 運用生成式對抗網路緩解推薦系統冷啟動使用者問題 | zh_TW |
| dc.title | Mitigating New User Cold-Start Problem in Recommendation Systems with Generative Adversarial Networks | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳孟彰(Hsin-Tsai Liu),張詠淳(Chih-Yang Tseng) | |
| dc.subject.keyword | 推薦系統,冷啟動使用者問題,對抗式生成網路, | zh_TW |
| dc.subject.keyword | Recommendation Systems,New User Cold-Start Problem,Generative Adversarial Networks, | en |
| dc.relation.page | 37 | |
| dc.identifier.doi | 10.6342/NTU202101317 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-07-08 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| dc.date.embargo-lift | 2023-08-01 | - |
| 顯示於系所單位： | 資訊管理學系 | |
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