政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/111785

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/111785

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | Items with full text/Total items : 113451/144438 (79%)
Visitors : 51328789 Online Users : 906

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

Scope

please add "double quotation mark" for query phrases to get precise results

please goto advance search for comprehansive author search

Adv. Search

Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer

Goto mobile version

政大典藏 > College of Informatics > Department of Computer Science > Theses > Item 140.119/111785

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/111785

Title:	結合線性上信賴邊界與K-平均算法來分析個人化點擊率 : 以雅虎新聞推薦為例 Integrating LinUCB with K-Means to Analyze Personal CTR : Yahoo News Recommendation as an Example
Authors:	張翔 Chang, Hsiang
Contributors:	胡毓忠 Hu, Yuh-Jong 張翔 Chang, Hsiang
Keywords:	Top-N推薦情境式多拉桿賭博問題線性上信賴邊界 K-平均算法 Top-N recommendation Contextual Multi-Armed bandit problem Linear upper confidence bound K-Means
Date:	2017
Issue Date:	2017-08-10 09:58:35 (UTC+8)
Abstract:	隸屬於Top-N推薦演算法之一的線性上信賴邊界，根據使用者特徵以及對商品喜好程度的線性組合，進行好感度評估和個人化推薦，同時也將商品賦予了矩陣特性，匯集曾經匹配過的使用者特徵。不過在使用者樣本不充裕、或是未曾被推薦過某種使用者類型，演算法無法有效評估使用者對商品的喜好，因此也造成推薦失敗的次數增加。本研究藉由分析個人化點擊率案例，變更線性上信賴邊界的規則：對於推薦內容不感興趣的使用者，嘗試利用商品特徵的分群結果，強制群組內其它商品紀錄同名使用者特徵，防止類似（使用者-項目）組合再次發生。透過策略最佳化原則以及離線評估方式，並且採用Yahoo Front Page Today Module數據集做為點擊率期望值的評估來源。 Linear upper confidence bound (LinUCB) is one of the Top-N implementations for contextual multi-armed bandit problem. The purpose is to evaluate user preference depending on the linear combination of user features and feedbacks from the recommendation. LinUCB collects user characteristics for sample evaluation, but insufficient samples also makes statistic evaluation poor. This study revises the original rule when recommendation fails, our algorithm shares user features to the other similar and unselected items based on K-Means item-feature clustering, to avoid similar user-item pair recommendation. In the final stage, we use off-policy evaluation to improve the expected click-through-rate (CTR) under the policy optimization technique.
Reference:	[1] F. Ricci, L. Rokach, and B. Shapira, "Introduction to recommender systems handbook," in Recommender systems handbook, ed: Springer, 2011, pp. 1-35. [2] G. Adomavicius and A. Tuzhilin, "Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions," IEEE transactions on knowledge and data engineering, vol. 17, pp. 734-749, 2005. [3] M. Deshpande and G. Karypis, "Item-based top-n recommendation algorithms," ACM Transactions on Information Systems (TOIS), vol. 22, pp. 143-177, 2004. [4] G. Linden, B. Smith, and J. York, "Amazon. com recommendations: Item-to-item collaborative filtering," IEEE Internet computing, vol. 7, pp. 76-80, 2003. [5] 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, 2001, pp. 285-295. [6] J. B. Schafer, J. Konstan, and J. Riedl, "Recommender systems in e-commerce," in Proceedings of the 1st ACM conference on Electronic commerce, 1999, pp. 158-166. [7] C. D. Manning, P. Raghavan, and H. Schütze, Introduction to information retrieval vol. 1: Cambridge university press Cambridge, 2008. [8] L. Terveen, W. Hill, B. Amento, D. McDonald, and J. Creter, "PHOAKS: A system for sharing recommendations," Communications of the ACM, vol. 40, pp. 59-62, 1997. [9] B. Mobasher, H. Dai, T. Luo, and M. Nakagawa, "Discovery and evaluation of aggregate usage profiles for web personalization," Data mining and knowledge discovery, vol. 6, pp. 61-82, 2002. [10] Y. G. Jung, M. S. Kang, and J. Heo, "Clustering performance comparison using K-means and expectation maximization algorithms," Biotechnology & Biotechnological Equipment, vol. 28, pp. S44-S48, 2014. [11] J. Vermorel and M. Mohri, "Multi-armed bandit algorithms and empirical evaluation," in ECML, 2005, pp. 437-448. [12] A. Mahajan and D. Teneketzis, "Multi-armed bandit problems," Foundations and Applications of Sensor Management, pp. 121-151, 2008. [13] M. Coggan, "Exploration and exploitation in reinforcement learning," Research supervised by Prof. Doina Precup, CRA-W DMP Project at McGill University, 2004. [14] L. Li, W. Chu, J. Langford, and R. E. Schapire, "A contextual-bandit approach to personalized news article recommendation," in Proceedings of the 19th international conference on World wide web, 2010, pp. 661-670. [15] T. Lu, D. Pál, and M. Pál, "Contextual multi-armed bandits," in Proceedings of the Thirteenth international conference on Artificial Intelligence and Statistics, 2010, pp. 485-492. [16] M. Fukushima, T. Takayama, and M. Takayama, "How Developers Explore and Exploit Instant Innovation from Experiment to Implementing New Product Development," in IFIP International Conference on Product Lifecycle Management, 2014, pp. 507-517. [17] J. C. Gittins, "Bandit processes and dynamic allocation indices," Journal of the Royal Statistical Society. Series B (Methodological), pp. 148-177, 1979. [18] X. Wang, Y. Wang, D. Hsu, and Y. Wang, "Exploration in interactive personalized music recommendation: a reinforcement learning approach," ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), vol. 11, p. 7, 2014. [19] R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction vol. 1: MIT press Cambridge, 1998. [20] M. Tokic, "Adaptive ε-greedy exploration in reinforcement learning based on value differences," in Annual Conference on Artificial Intelligence, 2010, pp. 203-210. [21] P. Auer, N. Cesa-Bianchi, Y. Freund, and R. E. Schapire, "The nonstochastic multiarmed bandit problem," SIAM journal on computing, vol. 32, pp. 48-77, 2002. [22] J. Langford and T. Zhang, "The epoch-greedy algorithm for multi-armed bandits with side information," in Advances in neural information processing systems, 2008, pp. 817-824. [23] O. Chapelle and L. Li, "An empirical evaluation of thompson sampling," in Advances in neural information processing systems, 2011, pp. 2249-2257. [24] S. L. Scott, "A modern Bayesian look at the multi‐armed bandit," Applied Stochastic Models in Business and Industry, vol. 26, pp. 639-658, 2010. [25] W. R. Thompson, "On the likelihood that one unknown probability exceeds another in view of the evidence of two samples," Biometrika, vol. 25, pp. 285-294, 1933. [26] P. Auer, "Using confidence bounds for exploitation-exploration trade-offs," Journal of Machine Learning Research, vol. 3, pp. 397-422, 2002. [27] P. Auer, N. Cesa-Bianchi, and P. Fischer, "Finite-time analysis of the multiarmed bandit problem," Machine learning, vol. 47, pp. 235-256, 2002. [28] N. Hansen and A. Ostermeier, "Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaptation," in Evolutionary Computation, 1996., Proceedings of IEEE International Conference on, 1996, pp. 312-317. [29] R. Allesiardo, R. Féraud, and D. Bouneffouf, "A neural networks committee for the contextual bandit problem," in International Conference on Neural Information Processing, 2014, pp. 374-381. [30] R. Féraud, R. Allesiardo, T. Urvoy, and F. Clérot, "Random Forest for the Contextual Bandit Problem," in Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016, pp. 93-101. [31] M. Valko, N. Korda, R. Munos, I. Flaounas, and N. Cristianini, "Finite-time analysis of kernelised contextual bandits," arXiv preprint arXiv:1309.6869, 2013. [32] L. Tang, Y. Jiang, L. Li, and T. Li, "Ensemble contextual bandits for personalized recommendation," in Proceedings of the 8th ACM Conference on Recommender Systems, 2014, pp. 73-80. [33] K.-H. Huang and H.-T. Lin, "Linear Upper Confidence Bound Algorithm for Contextual Bandit Problem with Piled Rewards," in Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2016, pp. 143-155. [34] A. Swaminathan, A. Krishnamurthy, A. Agarwal, M. Dudík, J. Langford, D. Jose, et al., "Off-policy evaluation for slate recommendation," arXiv preprint arXiv:1605.04812, 2016. [35] P. Thomas and E. Brunskill, "Data-efficient off-policy policy evaluation for reinforcement learning," in International Conference on Machine Learning, 2016, pp. 2139-2148. [36] L. Li, W. Chu, J. Langford, and X. Wang, "Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms," in Proceedings of the fourth ACM international conference on Web search and data mining, 2011, pp. 297-306. [37] S. Li, A. Karatzoglou, and C. Gentile, "Collaborative filtering bandits," in Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, 2016, pp. 539-548.
Description:	碩士國立政治大學資訊科學學系 103753038
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0103753038
Data Type:	thesis
Appears in Collections:	[Department of Computer Science ] Theses

Files in This Item:

File	Size	Format
303801.pdf	1147Kb	Adobe PDF2	32	View/Open

All items in 政大典藏 are protected by copyright, with all rights reserved.

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 © - Feedback