Exploration. Exploitation, and Engagement in Multi-Armed Bandits with Abandonment

doi:10.1109/Allerton49937.2022.9929390

	Exploration. Exploitation, and Engagement in Multi-Armed Bandits with Abandonment
	Yang, Zixian 1; Liu, Xin2 ; Ying, Lei 1
	2024
会议录名称	JOURNAL OF MACHINE LEARNING RESEARCH
ISSN	0
发表状态	已发表
DOI	10.1109/Allerton49937.2022.9929390
摘要	Recommendation algorithms have become increasingly important in many online platforms such as online education, TikTok, YouTube Shorts, advertising platforms, etc. Multiarmed bandit (MAB) [2] is a classic problem which can model these recommendation systems. Each arm in MAB corresponds to a specific type of item in the recommendation system. The recommendation of an item of the $i\text{th}$ type is regarded as a pull of arm $a_{i}$. Taking recommending short videos as an example, each arm $a_{i}$ represents a class of similar videos (e.g. videos from the same dancer). For simplicity, we assume the reward is 1 if the user likes the recommended item and is 0 otherwise. In a traditional MAB problem, the learner can continue to play the arms with the goal of maximizing the average reward, which either assumes a single user stays in the system for a long period of time or assumes the learner is recommending a single item to each user with a large number of users. While this traditional MAB formulation models recommendation systems such as online advertising well, there are new recommendation systems that are significantly different from these traditional models. In these new recommendation systems, such as TikTok or ALEKS, the learner continuously recommends videos/contents to a user, and the user, other than like or dislike the item, may abandon the system if the recommended items cannot engage the user, and come back later. For example, a user watches TikTok or YouTube Shorts for some period of time, where the duration depends on how interesting/engaging the videos are, then leaves the systems, and comes back later.
关键词	Video on demand Education Watches Advertising Recommender systems Videos
会议地点	Monticello, IL, USA
会议日期	27-30 Sept. 2022
URL	查看原文
收录类别	EI
来源库	IEEE
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/251916
专题	信息科学与技术学院_PI研究组_刘鑫组
通讯作者	Yang, Zixian
作者单位	1.Univ Michigan, Ann Arbor, MI 48109 USA 2.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China
推荐引用方式 GB/T 7714	Yang, Zixian,Liu, Xin,Ying, Lei. Exploration. Exploitation, and Engagement in Multi-Armed Bandits with Abandonment[C],2024.