{"type":"ScholarlyArticle","title":"Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search","authors":[{"name":"Tao Zhuang","slug":"tao-zhuang","given":"Tao","family":"Zhuang","affiliation":"Taobao Search , Alibaba Group Holding Limited"},{"name":"Wenwu Ou","slug":"wenwu-ou","given":"Wenwu","family":"Ou","affiliation":"Taobao Search , Alibaba Group Holding Limited"},{"name":"Zhirong Wang","slug":"zhirong-wang","given":"Zhirong","family":"Wang","affiliation":"Taobao Search , Alibaba Group Holding Limited"}],"abstract":"In web search, mutual influences between documents have been studied from the perspective of\r\nsearch result diversification. But the methods in web search is not directly applicable to e-commerce\r\nsearch because of their differences. And little research has been done on the mutual influences between \r\nitems in e-commerce search. We propose a global optimization framework for mutual influence aware ranking \r\nin e-commerce search. Our framework directly optimizes the Gross Merchandise Volume (GMV) for ranking, \r\nand decomposes ranking into two tasks. The first task is mutual influence aware purchase probability estimation. \r\nWe propose a global feature extension method to incorporate mutual influences into the features of\r\nan item. We also use Recurrent Neural Network (RNN) to capture influences related to ranking orders in purchase \r\nprobability estimation. The second task is to find the best ranking order based on the purchase probability estimations. \r\nWe treat the second task as a sequence generation problem and solved it using the beam search algorithm. We\r\nperformed online A/B test on a large e-commerce search engine. The results show that our method brings a 5% increase \r\nin GMV for the search engine over a strong baseline.","doi":"10.24963/ijcai.2018/518","url":"https://ijcai.org/proceedings/2018/518","pdf":"https://ijcai.org/proceedings/2018/0518.pdf","markdown":"https://ijcai.org/proceedings/2018/518.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/518","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning Applications","track":"Main track","number":518,"pages":{"first":3725,"last":3731},"keywords":["Machine Learning Applications: Applications of Supervised Learning","Machine Learning Applications: Big data ; Scalability","Uncertainty in AI: Sequential Decision Making","Multidisciplinary Topics and Applications: Information Retrieval"],"videos":[],"references":[{"position":1,"raw":"[Agrawal et al., 2009] Rakesh Agrawal, Sreenivas Golla- pudi, Alan Halverson, and Samuel Ieong. 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