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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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.
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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

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