{"type":"ScholarlyArticle","title":"Toward Optimal Solution for the Context-Attentive Bandit Problem","authors":[{"name":"Djallel Bouneffouf","slug":"djallel-bouneffouf","given":"Djallel","family":"Bouneffouf","affiliation":""},{"name":"Raphael Feraud","slug":"raphael-feraud","given":"Raphaël","family":"Féraud","affiliation":""},{"name":"Sohini Upadhyay","slug":"sohini-upadhyay","given":"Sohini","family":"Upadhyay","affiliation":""},{"name":"Irina Rish","slug":"irina-rish","given":"Irina","family":"Rish","affiliation":""},{"name":"Yasaman Khazaeni","slug":"yasaman-khazaeni","given":"Yasaman","family":"Khazaeni","affiliation":""}],"abstract":"In various recommender system applications, from medical diagnosis to dialog systems, due to observation costs only a small subset of a potentially large number of context variables can be observed at each iteration; however, the agent has a freedom to choose which variables to observe.  In this paper, we analyze and  extend an online learning framework known as Context-Attentive Bandit, We derive a novel algorithm, called Context-Attentive Thompson Sampling (CATS), which builds upon the Linear Thompson Sampling approach,  adapting it to Context-Attentive Bandit  setting.  We provide a theoretical regret analysis and an extensive empirical evaluation demonstrating  advantages of the proposed approach over several baseline methods  on a variety of real-life datasets.","doi":"10.24963/ijcai.2021/481","url":"https://ijcai.org/proceedings/2021/481","pdf":"https://ijcai.org/proceedings/2021/0481.pdf","markdown":"https://ijcai.org/proceedings/2021/481.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/481","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Machine Learning Applications","track":"Main Track","number":481,"pages":{"first":3493,"last":3500},"keywords":["Data Mining: Recommender Systems","Machine Learning Applications: Applications of Reinforcement Learning","Data Mining: Big Data, Large-Scale Systems"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}