Study on Contextual Bandit Problem with Multiple Actions

• Main Authors: Ya-Hsuan Chang, 張雅軒
• Other Authors: Hsuan-Tien Lin
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/94665894891939536263

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 101 === The contextual bandit problem is usually used to model online applications like article recommendation. Somehow the problem cannot fully meet some needs of these applica- tions, such as making multiple actions at the same time. We propose a new Contextual Bandit Problem with Multiple Ac- tions (CBMA), which is an extension of the traditional con- textual bandit problem and fits the online applications better. We adapt some existing contextual bandit algorithms for our CBMA problem, and propose a new Pairwise Regression with Upper Confidence Bound (PairUCB) algorithm which utilizes the new properties of the CBMA problem, The experiment re- sults demostrate that PairUCB outperforms other algorithms.