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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳建錦(Chien-Chin Chen) | |
| dc.contributor.author | Cheng-Han Wu | en |
| dc.contributor.author | 吳承翰 | zh_TW |
| dc.date.accessioned | 2021-06-07T17:40:37Z | - |
| dc.date.copyright | 2020-07-27 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-07-23 | |
| dc.identifier.citation | [1] H.-T. Cheng, et al., Wide deep learning for recommender systems, in: Proceedings of the 1st workshop on deep learning for recommender systems, (2016), pp. 7-10. [2] P. Covington, et al., Deep neural networks for youtube recommendations, in: Proceedings of the 10th ACM conference on recommender systems, (2016), pp. 191-198. [3] M. Fu, et al., A novel deep learning-based collaborative filtering model for recommendation system, IEEE transactions on cybernetics, 49(3) (2018) 1084-1096. [4] O. Georgiou, N. Tsapatsoulis, The importance of similarity metrics for representative users identification in recommender systems, in: IFIP International Conference on Artificial Intelligence Applications and Innovations, (Springer, 2010), pp. 12-21. [5] C.A. Gomez-Uribe, N.J.A.T.o.M.I.S. Hunt, The netflix recommender system: Algorithms, business value, and innovation, ACM Transactions on Management Information Systems, December 2015 Article No.: 13, 6(4) (2015) 1-19. [6] X. He, et al., Neural collaborative filtering, in: Proceedings of the 26th international conference on world wide web, (2017), pp. 173-182. [7] K. Järvelin, J. Kekäläinen, Cumulated gain-based evaluation of IR techniques, ACM Transactions on Information Systems, 20(4) (2002) 422-446. [8] N.N. Liu, et al., Wisdom of the better few: cold start recommendation via representative based rating elicitation, in: Proceedings of the fifth ACM conference on Recommender systems, (2011), pp. 37-44. [9] A. Majumdar, A. Jain, Cold-start, warm-start and everything in between: an autoencoder based approach to recommendation, in: 2017 International Joint Conference on Neural Networks (IJCNN), (IEEE, 2017), pp. 3656-3663. [10] A.M. Rashid, et al., Learning preferences of new users in recommender systems: an information theoretic approach, ACM Sigkdd Explorations Newsletter, 10(2) (2008) 90-100. [11] A. Rodriguez, A. Laio, Clustering by fast search and find of density peaks, Science, 344(6191) (2014) 1492-1496. [12] L. Shi, et al., Local representative-based matrix factorization for cold-start recommendation, ACM Transactions on Information Systems, 36(2) (2017) 1-28. [13] S. Shi, et al., Attention-based adaptive model to unify warm and cold starts recommendation, in: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, (2018), pp. 127-136. [14] P. Vincent, et al., Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, Journal of machine learning research, 11(Dec) (2010) 3371-3408. [15] M. Volkovs, et al., Dropoutnet: Addressing cold start in recommender systems, in: Advances in Neural Information Processing Systems, (2017), pp. 4957-4966. [16] J. Wei, et al., Collaborative filtering and deep learning based recommendation system for cold start items, Expert Systems with Applications, 69(2017) 29-39. [17] Y. Wu, et al., Collaborative denoising auto-encoders for top-n recommender systems, in: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, (2016), pp. 153-162. [18] S. Zhang, et al., Deep learning based recommender system: A survey and new perspectives, ACM Computing Surveys, 52(1) (2019) 1-38. [19] F. Zhuang, et al., Representation learning via Dual-Autoencoder for recommendation, Neural Networks, 90(2017) 83-89. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/15438 | - |
| dc.description.abstract | 本篇論文主要專注於解決推薦系統中常見的冷啟動使用者問題，我們提出了一種稱為「使用者返老還童」的機制，並且結合深度學習模型Denoising Autoencoder，以此來解決冷啟動使用者問題。首先，「使用者返老還童」機制會為不同使用者族群選出代表該群使用者的商品，我們稱這些選出的商品為「具代表性商品」，接著透過將使用者對於所有商品的評分向量隨機覆蓋大部分的不具代表性商品維度為0分，以及覆蓋小部分的具代表性商品維度為0分後，以此來模擬使用者冷啟動的狀態。當我們得到受「使用者返老還童」機制還原的冷啟動使用者後，接著我們可以為每一群使用者訓練一個深度學習模型Denoising Autoencoder。Denoising Autoencoder具備將受到雜訊干擾的輸入向量還原回未受雜訊干擾的向量，此種特性會有助於將冷啟動狀態的使用者向量還原回一般狀態的使用者。因此，當Denoising Autoencoder模型訓練完成後，模型便有能力把輸入的冷啟動使用者順利還原成富有評分資訊的使用者狀態，並且透過預測出的使用者評分向量來進行推薦。 | zh_TW |
| dc.description.abstract | In our thesis, we focus on the cold start user problem in the field of recommender systems. We propose a mechanism called “User Rejuvenation” and combined it with the deep learning model Denoising Autoencoder to solve the cold start user problem. The “User Rejuvenation” first choose representative items for each group of users, after that we randomly set the dimensions corresponding to representative items in user vector to zero score with lower probability, and we randomly set the dimensions corresponding to non-representative items in user vector to zero score with higher probability. The main purpose for “User Rejuvenation” is to turn the non-cold start user vectors back to cold start user vectors for each group of users. After getting the group-specific cold start user vectors generated from “User Rejuvenation” mechanism, we can use them to train a Denoising Autoencoder model for the user group. When the training process is complete, the model will have capacity for restoring the cold start user vectors to non-cold start user vectors, and the recommendation is made through the predicted non-cold start user vectors. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-07T17:40:37Z (GMT). No. of bitstreams: 1 U0001-2307202011160700.pdf: 2390617 bytes, checksum: 36764ed16405fd4ed7edf7140f62d79f (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 中文摘要/英文摘要………………………………………………………………………… ⅰ、ⅱ 目錄……………………………………………………………………………… ⅲ 圖目錄…………………………………………………………………………. ⅳ 表目錄…………………………………………………………………………. v 第一章 研究動機………………………………………………………………….. 1 1.1 推薦系統的重要性………………………………………… 1 1.2 冷啟動問題的難度與重要性………………………………………… 2 1.3 運用深度學習模型解決冷啟動問題………………………………………… 3 1.4 我們與現行深度學習方法的不同之處………………………………………… 5 第二章 文獻回顧………………………………………………………………….. 6 2.1 以深度學習為基礎之推薦系統………………………………………… 7 2.2 運用深度學習之冷啟動推薦… … … … … … … … … … … … … … … … 14 2.3 代表性商品探勘… … … … … … … … … … … … … … … … … … … … … … .. 17 第三章 論文方法………………………………………………………………….. 20 3.1 DAE基本架構………………………………….…………………………………… 21 3.2 採用DAE架構原因與初步方法………………………………………… 23 3.3 使用者返老還童階段………………………………….…………………………………… 24 3.4 冷啟動使用者推薦………………………………….…………………………………… 29 第四章 論文方法實驗與分析………………………………………………………………….. 29 4.1 實驗資料集與評估指標………………………………….…………………………………… 29 4.2 訓練流程與實驗參數設定………………………………….………………………………… 31 4.3所有模型效能比較………………………………….…………………………………… 35 第五章 結論…………………………………………………………………….…… 37 第六章 參考文獻整理…………………………………………………………………….…… 38 | |
| dc.language.iso | zh-TW | |
| dc.subject | 使用者冷啟動問題 | zh_TW |
| dc.subject | 具代表性商品選取 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 推薦系統 | zh_TW |
| dc.subject | 自動去噪編碼器 | zh_TW |
| dc.subject | Deep Learning | en |
| dc.subject | Representative items selection | en |
| dc.subject | Denoising Autoencoder | en |
| dc.subject | User Cold Start Problem | en |
| dc.subject | Recommendation system | en |
| dc.title | 以DAE向量雜訊移除為基礎之新進使用者冷啟動推薦 | zh_TW |
| dc.title | A Cold Start Recommendation Method for New Users Based on DAE Vector Noise Removal | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳孟彰(Meng-Zhang Chen),張詠淳(Yong-Chun Zhang) | |
| dc.subject.keyword | 深度學習,推薦系統,使用者冷啟動問題,自動去噪編碼器,具代表性商品選取, | zh_TW |
| dc.subject.keyword | Deep Learning,Recommendation system,User Cold Start Problem,Denoising Autoencoder,Representative items selection, | en |
| dc.relation.page | 39 | |
| dc.identifier.doi | 10.6342/NTU202001765 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2020-07-24 | |
| 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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