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

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题名:	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
作者:	張翔 Chang, Hsiang
贡献者:	胡毓忠 Hu, Yuh-Jong 張翔 Chang, Hsiang
关键词:	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
日期:	2017
上传时间:	2017-08-10 09:58:35 (UTC+8)
摘要:	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.
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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.
描述:	碩士國立政治大學資訊科學學系 103753038
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0103753038
数据类型:	thesis
显示于类别:	[資訊科學系] 學位論文

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