![\"\"](\"\/cise\/files\/2022\/12\/wanning-chen-464x636.jpeg\")
Wanning Chen<\/span><\/span><\/strong>\nAssistant Professor<\/strong><\/span>
\nUniversity of Washington<\/span><\/strong><\/span><\/h4>\n
Speed Up the Cold-Start Learning in Two-Sided Bandits with Many Arms<\/strong><\/h3>\nMulti-armed bandit (MAB) algorithms are efficient approaches to reduce the opportunity cost of online experimentation and are used by companies to find the best product from periodically refreshed product catalogs. However, these algorithms face the so-called cold-start at the onset of the experiment due to a lack of knowledge of customer preferences for new products, requiring an initial data collection phase known as the burn-in period. During this period, MAB algorithms operate like randomized experiments, incurring large burn-in costs which scale with the large number of products. We attempt to reduce the burn-in by identifying that many products can be cast into two-sided products, and then naturally model the rewards of the products with a matrix, whose rows and columns represent the two sides respectively. Next, we design two-phase bandit algorithms that first use subsampling and low-rank matrix estimation to obtain a substantially smaller targeted set of products and then apply a UCB procedure on the target products to find the best one. We theoretically show that the proposed algorithms lower costs and expedite the experiment in cases when there is limited experimentation time along with a large product set. Our analysis also reveals three regimes of long, short, and ultra-short horizon experiments, depending on dimensions of the matrix. Empirical evidence from both synthetic data and a real-world dataset on music streaming services validates this superior performance.<\/span><\/p>\nWanning Chen<\/strong> is an assistant <\/span>professor in the Information Systems group at Foster School of Business.\u00a0She completed her Ph.D. in business from Stanford University in 2022. Her\u00a0research focuses on developing statistical methodology for data-driven decision making. In particular,\u00a0She<\/span>\u00a0designs novel machine learning algorithms for problems where the underlying data has a natural matrix structure.<\/span><\/span><\/p>\nFaculty Host:<\/strong>\u00a0<\/span>Jinglong Zhao
\nStudent Host:<\/strong>\u00a0<\/span>Zili Wang<\/p>\n","protected":false},"excerpt":{"rendered":"Date: January 20, 2022 Time: 3:00PM-4:00PM Location: 8 St. Mary’s Street, PHO 203 Wanning Chen Assistant Professor University of Washington Speed Up the Cold-Start Learning in Two-Sided Bandits with Many Arms Multi-armed bandit (MAB) algorithms are efficient approaches to reduce the opportunity cost of online experimentation and are used by companies to find the best […]<\/p>\n","protected":false},"author":18605,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-templates\/no-sidebars.php","meta":[],"_links":{"self":[{"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/pages\/37667"}],"collection":[{"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/users\/18605"}],"replies":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/comments?post=37667"}],"version-history":[{"count":6,"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/pages\/37667\/revisions"}],"predecessor-version":[{"id":37684,"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/pages\/37667\/revisions\/37684"}],"wp:attachment":[{"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/media?parent=37667"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Wanning Chen<\/strong> is an assistant <\/span>professor in the Information Systems group at Foster School of Business.\u00a0She completed her Ph.D. in business from Stanford University in 2022. Her\u00a0research focuses on developing statistical methodology for data-driven decision making. In particular,\u00a0She<\/span>\u00a0designs novel machine learning algorithms for problems where the underlying data has a natural matrix structure.<\/span><\/span><\/p>\n Faculty Host:<\/strong>\u00a0<\/span>Jinglong Zhao Date: January 20, 2022 Time: 3:00PM-4:00PM Location: 8 St. Mary’s Street, PHO 203 Wanning Chen Assistant Professor University of Washington Speed Up the Cold-Start Learning in Two-Sided Bandits with Many Arms Multi-armed bandit (MAB) algorithms are efficient approaches to reduce the opportunity cost of online experimentation and are used by companies to find the best […]<\/p>\n","protected":false},"author":18605,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-templates\/no-sidebars.php","meta":[],"_links":{"self":[{"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/pages\/37667"}],"collection":[{"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/users\/18605"}],"replies":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/comments?post=37667"}],"version-history":[{"count":6,"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/pages\/37667\/revisions"}],"predecessor-version":[{"id":37684,"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/pages\/37667\/revisions\/37684"}],"wp:attachment":[{"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/media?parent=37667"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\nStudent Host:<\/strong>\u00a0<\/span>Zili Wang<\/p>\n","protected":false},"excerpt":{"rendered":"