政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/143835

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/143835

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文筆數/總筆數 : 113648/144635 (79%)
造訪人次 : 51589354 線上人數 : 735

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

搜尋範圍

查詢小技巧：

您可在西文檢索詞彙前後加上"雙引號"，以獲取較精準的檢索結果

若欲以作者姓名搜尋，建議至進階搜尋限定作者欄位，可獲得較完整資料

進階搜尋

主頁 ‧ 登入 ‧ 上傳 ‧ 說明 ‧ 關於政大典藏 ‧ 管理

到手機版

政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 > Item 140.119/143835

請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/143835

題名:	基於內容偏好圖卷積網路之完全冷啟動推薦演算法 Improving Complete Cold-Start Recommendation via Content-based Preference Graph Convolution Networks
作者:	王韋勝 Wang, Wei-Sheng
貢獻者:	蔡銘峰 Tsai, ‪Ming-Feng 王韋勝 Wang, Wei-Sheng
關鍵詞:	推薦系統冷起動推薦圖學習表示法 Graph Representation Recommender System Cold-Start Recommendation
日期:	2022
上傳時間:	2023-03-09 18:37:16 (UTC+8)
摘要:	傳統的混合推薦系統旨在結合協同過濾和內容過濾兩種方式進行推薦，利用使用者喜好資訊和過去互動過的商品內容資訊來解決資料稀疏性問題和冷啟動問題。但是，在現實世界中，經常因為產品的性質讓使用者和產品的互動資料相當稀少或是缺少這些資料，從而導致了完全冷啟動(Complete Cold Start, CCS)問題，如新聞推薦和新活動推薦，這是傳統的混合模型無法解決的。在本文中，我們提出了偏好內容卷積(Preference Content Convolu- tion, PCC)方法，這是一種基於圖卷積網絡(Graph Convolution Net- work, GCN)的圖學習表示方法，該方法可在接受缺失資料的前提下同時抽取使用者對內容的喜好特徵並結合內容資訊，進而針對冷啟動問題進行推薦。我們在現實世界中的線上售票服務資料集和圖書資料集上進行的實驗驗證此方法，其性能優於其他傳統基於內容過濾的方法和沒有卷積網路的混合模型，為基於卷積網路的模型指出了一個方向。 Conventional hybrid recommender system aims to address the data spar- sity problem and the cold start problem by leveraging collaborative and content- based filtering, simultaneously leveraging the precious user preference infor- mation and staple item content information. However, in many real-world scenarios, such as news and new event recommendations, the nature of items dictates the complete lack of user-item interaction, leading to the complete cold start (CCS) problem, which traditional hybrid models cannot solve. In this paper, we propose preference-content convolution (PCC), a con- volutional graph network (GCN) based embedding learning method which jointly captures item content information and user preference over item con- tent. The experiments conducted on the real-world online ticket vending service dataset and news recommendation dataset show that the proposed method significantly outperforms traditional content-based filtering methods and hybrid models without convolution, signifying a promising direction for using the convolution-based model in addressing the CCS problem.
參考文獻:	[1] A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko. Translating embeddings for modeling multi-relational data. Advances in neural information processing systems, 26, 2013. [2] I. Cantador, A. Bellog ́ın, and D. Vallet. Content-based recommendation in social tagging systems. In Proceedings of the fourth ACM conference on Recommender systems, pages 237–240, 2010. [3] C.-M. Chen, M.-F. Tsai, Y.-C. Lin, and Y.-H. Yang. Query-based music recommen- dations via preference embedding. In Proceedings of the 10th ACM Conference on Recommender Systems, pages 79–82, 2016. [4] D. Cohen, M. Aharon, Y. Koren, O. Somekh, and R. Nissim. Expediting exploration by attribute-to-feature mapping for cold-start recommendations. In Proceedings of the Eleventh ACM Conference on Recommender Systems, pages 184–192, 2017. [5] M. Elahi, F. Ricci, and N. Rubens. Active learning in collaborative filtering rec- ommender systems. In International Conference on Electronic Commerce and Web Technologies, pages 113–124. Springer, 2014. [6] N. Golbandi, Y. Koren, and R. Lempel. Adaptive bootstrapping of recommender systems using decision trees. In Proceedings of the fourth ACM international con- ference on Web search and data mining, pages 595–604, 2011. [7] J. Gope and S. K. Jain. A survey on solving cold start problem in recommender systems. In 2017 International Conference on Computing, Communication and Au- tomation (ICCCA), pages 133–138. IEEE, 2017. [8] W. Hamilton, Z. Ying, and J. Leskovec. Inductive representation learning on large graphs. Advances in neural information processing systems, 30, 2017. [9] Y. Juan, Y. Zhuang, W.-S. Chin, and C.-J. Lin. Field-aware factorization machines for ctr prediction. In Proceedings of the 10th ACM conference on recommender systems, pages 43–50, 2016. [10] Y.Koren,R.Bell,andC.Volinsky.Matrixfactorizationtechniquesforrecommender systems. Computer, 42(8):30–37, 2009. [11] M. Kula. Metadata embeddings for user and item cold-start recommendations. In T. Bogers and M. Koolen, editors, Proceedings of the 2nd Workshop on New Trends on Content-Based Recommender Systems co-located with 9th ACM Conference on Recommender Systems (RecSys 2015), Vienna, Austria, September 16-20, 2015., volume 1448 of CEUR Workshop Proceedings, pages 14–21. CEUR-WS.org, 2015. [12] B. Lika, K. Kolomvatsos, and S. Hadjiefthymiades. Facing the cold start problem in recommender systems. Expert systems with applications, 41(4):2065–2073, 2014. [13] P. Melville, R. J. Mooney, R. Nagarajan, et al. Content-boosted collaborative filter- ing for improved recommendations. Aaai/iaai, 23:187–192, 2002. [14] R. Mihalcea and P. Tarau. Textrank: Bringing order into text. In Proceedings of the 2004 conference on empirical methods in natural language processing, pages 404–411, 2004. [15] T. Mikolov, K. Chen, G. Corrado, and J. Dean. Efficient estimation of word repre- sentations in vector space. arXiv preprint arXiv:1301.3781, 2013. [16] B. Perozzi, R. Al-Rfou, and S. Skiena. Deepwalk: Online learning of social repre- sentations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 701–710, 2014. [17] S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618, 2012. [18] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web, pages 285–295, 2001. [19] M. Saveski and A. Mantrach. Item cold-start recommendations: learning local col- lective embeddings. In Proceedings of the 8th ACM Conference on Recommender systems, pages 89–96, 2014. [20] B. Shapira, L. Rokach, and S. Freilikhman. Facebook single and cross domain data for recommendation systems. User Modeling and User-Adapted Interaction, 23(2):211–247, 2013. [21] X. Su and T. M. Khoshgoftaar. A survey of collaborative filtering techniques. Ad- vances in artificial intelligence, 2009, 2009. [22] M. Sun, F. Li, J. Lee, K. Zhou, G. Lebanon, and H. Zha. Learning multiple-question decision trees for cold-start recommendation. In Proceedings of the sixth ACM in- ternational conference on Web search and data mining, pages 445–454, 2013. [23] J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei. Line: Large-scale infor- mation network embedding. In Proceedings of the 24th international conference on world wide web, pages 1067–1077, 2015. [24] H. Wang, M. Zhao, X. Xie, W. Li, and M. Guo. Knowledge graph convolutional net- works for recommender systems. In The world wide web conference, pages 3307– 3313, 2019. [25] J. Wei, J. He, K. Chen, Y. Zhou, and Z. Tang. Collaborative filtering and deep learning based recommendation system for cold start items. Expert Systems with Applications, 69:29–39, 2017. [26] K. Zhou, S.-H. Yang, and H. Zha. Functional matrix factorizations for cold-start recommendation. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, pages 315–324, 2011.
描述:	碩士國立政治大學資訊科學系 109753110
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0109753110
資料類型:	thesis
顯示於類別:	[資訊科學系] 學位論文

文件中的檔案:

檔案	描述	大小	格式	瀏覽次數
311001.pdf		6469Kb	Adobe PDF2	64	檢視/開啟

在政大典藏中所有的資料項目都受到原著作權保護.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - 回饋