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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/82364完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳建錦(JIAN-JIN CHEN) | |
| dc.contributor.author | Po-Lin Lai | en |
| dc.contributor.author | 賴柏霖 | zh_TW |
| dc.date.accessioned | 2022-11-25T07:29:46Z | - |
| dc.date.available | 2023-08-01 | |
| dc.date.copyright | 2021-08-18 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-07-07 | |
| dc.identifier.citation | [1] A. Antoniou, A. Storkey, H. Edwards, Data augmentation generative adversarial networks, arXiv preprint arXiv:1711.04340, (2017). [2] D.-K. Chae, J.-S. Kang, S.-W. Kim, J. Choi, Rating Augmentation with Generative Adversarial Networks towards Accurate Collaborative Filtering, in: The World Wide Web Conference, Association for Computing Machinery, San Francisco, CA, USA, 2019, pp. 2616–2622. [3] D.-K. Chae, J.-S. Kang, S.-W. Kim, J. Choi, Rating augmentation with generative adversarial networks towards accurate collaborative filtering, in: The World Wide Web Conference, 2019, pp. 2616-2622. [4] D.-K. Chae, J.-S. Kang, S.-W. Kim, J.-T. Lee, Cfgan: A generic collaborative filtering framework based on generative adversarial networks, in: Proceedings of the 27th ACM international conference on information and knowledge management, 2018, pp. 137-146. [5] C.C. Chen, Y.-H. Wan, M.-C. Chung, Y.-C. Sun, An effective recommendation method for cold start new users using trust and distrust networks, Information Sciences, 224 (2013) 19-36. [6] M. Deshpande, G. Karypis, Item-based top-N recommendation algorithms, ACM Trans. Inf. Syst., 22 (2004) 143–177. [7] M.J. Eppler, J. Mengis, The Concept of Information Overload: A Review of Literature from Organization Science, Accounting, Marketing, MIS, and Related Disciplines, The Information Society, 20 (2004) 325-344. [8] I. Fernández-Tobías, I. Cantador, M. Kaminskas, F. Ricci, Cross-domain recommender systems: A survey of the state of the art, in: Spanish conference on information retrieval, sn, 2012, pp. 1-12. [9] R. Forsati, M. Mahdavi, M. Shamsfard, M. Sarwat, Matrix factorization with explicit trust and distrust side information for improved social recommendation, ACM Transactions on Information Systems (TOIS), 32 (2014) 1-38. [10] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks, in: International conference on neural information processing systems, 2014, pp. 2672–2680. [11] P. Goyal, E. Ferrara, Graph embedding techniques, applications, and performance: A survey, Knowledge-Based Systems, 151 (2018) 78-94. [12] S. Gupta, D. Hanssens, B. Hardie, W. Kahn, V. Kumar, N. Lin, N. Ravishanker, S. Sriram, Modeling customer lifetime value, Journal of service research, 9 (2006) 139-155. [13] X. He, L. Liao, H. Zhang, L. Nie, X. Hu, T.-S. Chua, Neural collaborative filtering, in: 26th international conference on world wide web, 2017, pp. 173-182. [14] X. He, L. Liao, H. Zhang, L. Nie, X. Hu, T.-S. Chua, Neural collaborative filtering, in: Proceedings of the 26th international conference on world wide web, 2017, pp. 173-182. [15] J.H. Huang, Y.F. Chen, Herding in online product choice, Psychology Marketing, 23 (2006) 413-428. [16] P. Isola, J.-Y. Zhu, T. Zhou, A.A. Efros, Image-to-image translation with conditional adversarial networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 1125-1134. [17] Y. Jiang, J. Shang, Y. Liu, Maximizing customer satisfaction through an online recommendation system: A novel associative classification model, Decision Support Systems, 48 (2010) 470-479. [18] K. Järvelin, J. Kekäläinen, Cumulated gain-based evaluation of IR techniques, ACM Transactions on Information Systems (TOIS), 20 (2002) 422-446. [19] Y. Koren, R. Bell, C. Volinsky, Matrix factorization techniques for recommender systems, Computer, 42 (2009) 30-37. [20] B. Li, Q. Yang, X. Xue, Can movies and books collaborate? cross-domain collaborative filtering for sparsity reduction, in: Proceedings of the 21st international jont conference on Artifical intelligence, Morgan Kaufmann Publishers Inc., Pasadena, California, USA, 2009, pp. 2052–2057. [21] P. Li, A. Tuzhilin, DDTCDR: Deep Dual Transfer Cross Domain Recommendation, in: Proceedings of the 13th International Conference on Web Search and Data Mining, Association for Computing Machinery, Houston, TX, USA, 2020, pp. 331–339. [22] P. Li, A. Tuzhilin, DDTCDR: Deep dual transfer cross domain recommendation, in: Proceedings of the 13th International Conference on Web Search and Data Mining, 2020, pp. 331-339. [23] T. Liang, C. Xia, Y. Yin, P.S. Yu, Joint Training Capsule Network for Cold Start Recommendation, in: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, pp. 1769-1772. [24] T. Liang, C. Xia, Y. Yin, P.S. Yu, Joint Training Capsule Network for Cold Start Recommendation, in: 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, pp. 1769-1772. [25] J. Lin, K. Sugiyama, M.-Y. Kan, T.-S. Chua, Addressing cold-start in app recommendation: latent user models constructed from twitter followers, in: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, 2013, pp. 283-292. [26] J. Lin, K. Sugiyama, M.-Y. Kan, T.-S. Chua, Addressing cold-start in app recommendation: latent user models constructed from twitter followers, in: 36th international ACM SIGIR conference on Research and development in information retrieval, 2013, pp. 283-292. [27] M.-Y. Liu, T. Breuel, J. Kautz, Unsupervised image-to-image translation networks, arXiv preprint arXiv:1703.00848, (2017). [28] S. Liu, I. Ounis, C. Macdonald, Z. Meng, A Heterogeneous Graph Neural Model for Cold-Start Recommendation, in: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, pp. 2029-2032. [29] N. Mirbakhsh, C.X. Ling, Improving Top-N Recommendation for Cold-Start Users via Cross-Domain Information, ACM Trans. Knowl. Discov. Data, 9 (2015) Article 33. [30] L. Nguyen, T. Ishigaki, Domain-to-Domain Translation Model for Recommender System, arXiv preprint arXiv:1812.06229, (2018). [31] L. Nguyen, T. Ishigaki, D2D-TM: A Cycle VAE-GAN for Multi-Domain Collaborative Filtering, in: 2019 IEEE International Conference on Big Data (Big Data), 2019, pp. 1175-1180. [32] S.J. Pan, Q. Yang, A survey on transfer learning, IEEE Transactions on knowledge and data engineering, 22 (2009) 1345-1359. [33] A. Paterek, Improving regularized singular value decomposition for collaborative filtering, in: Proceedings of KDD cup and workshop, 2007, pp. 5-8. [34] A. Paterek, Improving regularized singular value decomposition for collaborative filtering, in: Proceedings of KDD cup and workshop,13th ACM Int. Conf. on Knowledge Discovery and Data Mining, San Jose, CA, USA, 2007, pp. 39-42. [35] T. Qian, Y. Liang, Q. Li, Solving cold start problem in recommendation with attribute graph neural networks, arXiv preprint arXiv:1912.12398, (2019). [36] F. Ricci, L. Rokach, B. Shapira, Introduction to recommender systems handbook, in: Recommender systems handbook, Springer, 2011, pp. 1-35. [37] S. Ruder, An overview of gradient descent optimization algorithms, arXiv preprint arXiv:1609.04747, (2016). [38] B. Sarwar, G. Karypis, J. Konstan, J. Riedl, Item-based collaborative filtering recommendation algorithms, in: Proceedings of the 10th international conference on World Wide Web, Association for Computing Machinery, Hong Kong, Hong Kong, 2001, pp. 285–295. [39] S. Sedhain, A.K. Menon, S. Sanner, L. Xie, Autorec: Autoencoders meet collaborative filtering, in: Proceedings of the 24th international conference on World Wide Web, 2015, pp. 111-112. [40] S. Sedhain, S. Sanner, D. Braziunas, L. Xie, J. Christensen, Social collaborative filtering for cold-start recommendations, in: 8th ACM Conference on Recommender systems, 2014, pp. 345-348. [41] A.P. Singh, G.J. Gordon, Relational learning via collective matrix factorization, in: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, 2008, pp. 650-658. [42] F. Strub, J. Mary, Collaborative filtering with stacked denoising autoencoders and sparse inputs, in: NIPS workshop on machine learning for eCommerce, 2015. [43] X. Su, T.M. Khoshgoftaar, A survey of collaborative filtering techniques, Advances in artificial intelligence, 2009 (2009). [44] Y. Taigman, A. Polyak, L. Wolf, Unsupervised cross-domain image generation, arXiv preprint arXiv:1611.02200, (2016). [45] P. Vincent, H. Larochelle, Y. Bengio, P.-A. Manzagol, Extracting and composing robust features with denoising autoencoders, in: Proceedings of the 25th international conference on Machine learning, 2008, pp. 1096-1103. [46] M. Volkovs, G.W. Yu, T. Poutanen, DropoutNet: Addressing Cold Start in Recommender Systems, in: 31st International Conference on Neural Information Processing Systems, 2017, pp. 4964–4973. [47] M. Volkovs, G.W. Yu, T. Poutanen, DropoutNet: Addressing Cold Start in Recommender Systems, in: NIPS, 2017, pp. 4957-4966. [48] J. Wang, L. Yu, W. Zhang, Y. Gong, Y. Xu, B. Wang, P. Zhang, D. Zhang, IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models, in: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Association for Computing Machinery, Shinjuku, Tokyo, Japan, 2017, pp. 515–524. [49] J. Wang, L. Yu, W. Zhang, Y. Gong, Y. Xu, B. Wang, P. Zhang, D. Zhang, Irgan: A minimax game for unifying generative and discriminative information retrieval models, in: Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval, 2017, pp. 515-524. [50] Q. Wang, Z. Mao, B. Wang, L. Guo, Knowledge graph embedding: A survey of approaches and applications, IEEE Transactions on Knowledge and Data Engineering, 29 (2017) 2724-2743. [51] Y. Wu, C. DuBois, A.X. Zheng, M. Ester, 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. [52] J. Xu, Y. Yao, H. Tong, X. Tao, J. Lu, R a P are: A generic strategy for cold-start rating prediction problem, IEEE Transactions on Knowledge and Data Engineering, 29 (2016) 1296-1309. [53] H.-J. Xue, X. Dai, J. Zhang, S. Huang, J. Chen, Deep Matrix Factorization Models for Recommender Systems, in: 26th International Joint Conference on Artificial Intelligence, Melbourne, Australia, 2017, pp. 3203-3209. [54] H.-J. Xue, X. Dai, J. Zhang, S. Huang, J. Chen, Deep Matrix Factorization Models for Recommender Systems, in: IJCAI, Melbourne, Australia, 2017, pp. 3203-3209. [55] H. Yin, L. Zou, Q.V.H. Nguyen, Z. Huang, X. Zhou, Joint Event-Partner Recommendation in Event-Based Social Networks, in: 2018 IEEE 34th International Conference on Data Engineering (ICDE), 2018, pp. 929-940. [56] S. Zhang, L. Yao, A. Sun, Y. Tay, Deep learning based recommender system: A survey and new perspectives, ACM Computing Surveys (CSUR), 52 (2019) 1-38. [57] Z.-D. Zhao, M.-S. Shang, User-based collaborative-filtering recommendation algorithms on hadoop, in: 2010 third international conference on knowledge discovery and data mining, IEEE, 2010, pp. 478-481. [58] J.-Y. Zhu, R. Zhang, D. Pathak, T. Darrell, A.A. Efros, O. Wang, E. Shechtman, Toward multimodal image-to-image translation, arXiv preprint arXiv:1711.11586, (2017). [59] J.-Y. Zhu, R. Zhang, D. Pathak, T. Darrell, A.A. Efros, O. Wang, E. Shechtman, Toward multimodal image-to-image translation, in: Proceedings of the 31st International Conference on Neural Information Processing Systems, Curran Associates Inc., Long Beach, California, USA, 2017, pp. 465–476. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/82364 | - |
| dc.description.abstract | 解決新進使用者冷啟動問題在網路平台的推薦系統中是一個很重要的議題，許多在跨領域學習的方法中都會利用額外領域的資訊來解決目標領域上資料不足的問題。然而，這些研究只針對在不同的領域上互相轉換。在這篇論文中，我們用了一個以生成對抗式網路為基底的領域轉移學習模型，來解決新進使用者冷啟動問題。我們將該使用者剛進入平台時的冷啟動狀態，以及之後擁有豐富經驗的狀態視為兩個不同的領域，希望能透過利用使用者額外的資訊例如性別、年齡、職業等其他特徵，成功的將冷啟動狀態的使用者轉移成有豐富經驗的狀態，再以此進行推薦。透過實驗證明，我們的方法成功的超過許多現有知名的推薦系統算法，成為目前最好的冷啟動推薦方法之一。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-25T07:29:46Z (GMT). No. of bitstreams: 1 U0001-0507202116061800.pdf: 2049961 bytes, checksum: 291cfda1abebaa3ba2b0365432d6db46 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員會審定書 i 謝辭………………………………………………………………………ii ABSTRACT iii 摘要………………………………………………………………………iv LIST OF FIGURES ………………………………………………………vi Chapter 1. Introduction ……………………………………………1 Chapter2. Related work………………………………………………6 Chapter3. Proposed method ………………………………………..14 Chapter4. Experiments ……………………………………………..24 Chapter5. Conclusion ……………………………………………….33 Chapter6. Future work……………………………………………….34 Reference ............................................... 35 | |
| dc.language.iso | en | |
| dc.title | 以領域轉移學習來解決新進使用者推薦問題 | zh_TW |
| dc.title | A Domain Transfer Learning For New User Cold-Start Recommendation Problem | 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,領域轉移學習,深度學習,冷啟動問題,生成對抗式網路, | zh_TW |
| dc.subject.keyword | Recommendation System,Domain transfer,GAN,Deep Learning,Cold start problem, | en |
| dc.relation.page | 41 | |
| dc.identifier.doi | 10.6342/NTU202101275 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-07-07 | |
| 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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| U0001-0507202116061800.pdf | 2 MB | Adobe PDF | 檢視/開啟 |
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