{"id":1034,"date":"2022-01-27T19:18:04","date_gmt":"2022-01-27T19:18:04","guid":{"rendered":"https:\/\/meetings.informs.org\/wordpress\/analytics2022\/speakers\/cathy-huyghe-copy\/"},"modified":"2022-01-27T19:27:10","modified_gmt":"2022-01-27T19:27:10","slug":"lili-zhang","status":"publish","type":"page","link":"https:\/\/meetings.informs.org\/wordpress\/analytics2022\/speakers\/lili-zhang\/","title":{"rendered":"Lili Zhang"},"content":{"rendered":"<!--themify_builder_content-->\n<div id=\"themify_builder_content-1034\" data-postid=\"1034\" class=\"themify_builder_content themify_builder_content-1034 themify_builder tf_clear\">\n                    <div  data-lazy=\"1\" class=\"module_row themify_builder_row dropPadding-bottom tb_p5bj7 tb_first tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-1 tb_58sr8 first\">\n                    <!-- module image -->\n<div  class=\"module module-image tb_tac08 image-top  track machine tf_mw\" data-lazy=\"1\">\n        <div class=\"image-wrap tf_rel tf_mw\">\n            <img decoding=\"async\" src=\"\/wordpress\/analytics2022\/files\/2022\/01\/liliZhang.jpg\" title=\"Lili Zhang\" alt=\"Lili Zhang headshot\">    \n        <\/div>\n    <!-- \/image-wrap -->\n    \n        <div class=\"image-content\">\n                    <h3 class=\"image-title\">\n                                    Lili Zhang                            <\/h3>\n                    <\/div>\n    <!-- \/image-content -->\n        <\/div>\n<!-- \/module image -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-3 tb_mujq9 last\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_7fb810   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h1>Lili Zhang<\/h1>\n<h5>Research Engineer at Hewlett Packard Enterprise<\/h5>\n<p>Lili Zhang is a Research Engineer at Hewlett Packard Enterprise in the Marketing Analytics organization. She received her Ph.D. in Analytics and Data Science from Kennesaw State University, M.S. in Industrial Engineering from the University of Tennessee-Knoxville, and B.S. in Electronic Information Science and Technology from Central South University. Her research is focused on graph analytics and imbalanced learning.<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-css_id=\"pp257\" data-lazy=\"1\" class=\"module_row themify_builder_row fullwidth_row_container tb_pp257 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_1 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col-full tb_lzay11 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_pj2f11 level associate  \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>Associate<\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module text -->\n<div  class=\"module module-text tb_e4rf11 track machine  \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h6>Track: Machine Learning Applications in Marketing<\/h6>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module text -->\n<div  class=\"module module-text tb_aklo11   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h2>Measuring Customer Similarity and Identifying Cross-Selling Products<\/h2>\n<p>Product affinity segmentation discovers the groups of customers with similar purchase preferences for cross-selling opportunities to increase sales and customer loyalty. However, this concept can be challenging to implement efficiently and effectively for actionable strategies, due to the skew and sparsity of the product-level data and the computational complexity of traditional clustering methods. In this work, we propose to partition customers into groups by maximizing their product purchase similarity within the communities in the customer-product bipartite graph. Through a case study using data from a large U.S. retailer, we demonstrate that the proposed method generates interpretable clustering results with distinct product purchase patterns, yields higher response rates, and addresses the computational complexity in the context of big data.<\/p>\n<div>\u00a0<\/div>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n        <\/div>\n<!--\/themify_builder_content-->","protected":false},"excerpt":{"rendered":"<p>Lili Zhang Lili Zhang Research Engineer at Hewlett Packard Enterprise Lili Zhang is a Research Engineer at Hewlett Packard Enterprise in the Marketing Analytics organization. She received her Ph.D. in Analytics and Data Science from Kennesaw State University, M.S. in Industrial Engineering from the University of Tennessee-Knoxville, and B.S. in Electronic Information Science and Technology [&hellip;]<\/p>\n","protected":false},"author":1001094,"featured_media":984,"parent":256,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"class_list":["post-1034","page","type-page","status-publish","has-post-thumbnail","hentry","has-post-title","has-post-date","has-post-category","has-post-tag","has-post-comment","has-post-author",""],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.0 (Yoast SEO v26.0) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Lili Zhang - 2022 INFORMS Business Analytics Conference<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/meetings.informs.org\/wordpress\/analytics2022\/speakers\/lili-zhang\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Lili Zhang\" \/>\n<meta property=\"og:description\" content=\"Lili Zhang Lili Zhang Research Engineer at Hewlett Packard Enterprise Lili Zhang is a Research Engineer at Hewlett Packard Enterprise in the Marketing Analytics organization. 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She received her Ph.D. in Analytics and Data Science from Kennesaw State University, M.S. in Industrial Engineering from the University of Tennessee-Knoxville, and B.S. in Electronic Information Science and Technology from Central South University. Her research is focused on graph analytics and imbalanced learning.<\/p>\n<p>Associate<\/p>\n<h6>Track: Machine Learning Applications in Marketing<\/h6>\n<h2>Measuring Customer Similarity and Identifying Cross-Selling Products<\/h2> <p>Product affinity segmentation discovers the groups of customers with similar purchase preferences for cross-selling opportunities to increase sales and customer loyalty. However, this concept can be challenging to implement efficiently and effectively for actionable strategies, due to the skew and sparsity of the product-level data and the computational complexity of traditional clustering methods. 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