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| Title: | 基於貝氏IRT模型之線上學習演算法
Online Learning Algorithms based on Bayesian IRT models |
| Authors: | 賴翔偉 |
| Contributors: | 翁久幸
賴翔偉 |
| Keywords: | 項目反應理論
潛在變量
貝氏
動差配對法
靜態學習
動態學習
Bayesian
Dynamical learning
Item response theory
Latent trait
Moment-matching method
Statical learning |
| Date: | 2018 |
| Issue Date: | 2018-07-27 11:28:29 (UTC+8) |
| Abstract: | 我們在此篇論文中呈現兩種類型的線上學習演算法─靜態與動態，用以即時的量化網路評分類型資料中，使用者與被評分項的潛在變量。靜態學習演算法是延續Weng and Coad (2018)的結果，在他們所採用Ho and Quinn (2008)的Bayesian ordinal IRT 模型中的截斷點加上常態型先驗分配；動態學習演算法則是試行Graepel et al.(2010)中所採用的動態概念來改進靜態學習演算法下可能產生的缺失。
透過實驗，我們得到以下兩個結論：(1)截斷點加上先驗分配後，經過序列化的修正所得到的結果，會比截斷點沒有設置先驗並固定下所得到的結果來的好；(2)雖然靜態學習演算法的運算時間少於動態學習演算法，但動態學習在某些配置下，可能會表現的比靜態學習好。
在文末，針對 Ho and Quinn的Bayesian ordinal IRT 模型中的潛在變量，我們給出幾個比較合適的先驗參數配置。
In this paper, we present two types of online learning algorithms--statical and dynamical--to capture users’ and items’ latent traits’ information through online product rating data in a real-time manner. The statical one extends Weng and Coad (2018)’s deterministic moment-matching method by adding priors to cutpoints, and the dynamical one extends the statical one with the dynamical ideas adopted in Graepel et al. (2010) for taking users’ and items’ time-dependent latent traits into account. Both learning algorithms are designed for the Bayesian ordinal IRT model proposed by Ho and Quinn (2008).
Through experiments, we have verified two things: First, updating cutpoints sequentially produces better results. Second, statical learning’s computational time is almost twice as less as dynamical learning’s, but dynamical learning can
slightly outperform statical learning under some configurations.
At the end of the paper, we give some useful configurations for setting up the priors of the latent variables of Ho and Quinn’s ordinal IRT model. |
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Graepel, T., Candela, J. Q., Borchert, T., and Herbrich, R. (2010). Web-scale bayesian click-through rate prediction for sponsored search advertising in microsoft’s bing search engine. Omnipress.
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Van De Schoot, R., Broere, J. J., Perryck, K. H., Zondervan-Zwijnenburg, M., and Van Loey, N. E. (2015). Analyzing small data sets using bayesian estimation: the case of post-traumatic stress symptoms following mechanical ventilation in burn survivors. European Journal of Psychotraumatology, 6(1):25216.
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| Description: | 碩士
國立政治大學
統計學系
105354011 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0105354011 |
| Data Type: | thesis |
| DOI: | 10.6814/THE.NCCU.STAT.006.2018.B03 |
| Appears in Collections: | [統計學系] 學位論文
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