{"type":"ScholarlyArticle","title":"A Brand-level Ranking System with the Customized Attention-GRU Model","authors":[{"name":"Yu Zhu","slug":"yu-zhu","given":"Yu","family":"Zhu","affiliation":"Alibaba-Zhejiang University Joint Institute of Frontier Technologies"},{"name":"Junxiong Zhu","slug":"junxiong-zhu","given":"Junxiong","family":"Zhu","affiliation":"Alibaba Group , Hangzhou , China"},{"name":"Jie Hou","slug":"jie-hou","given":"Jie","family":"Hou","affiliation":"Alibaba Group , Hangzhou , China"},{"name":"Yongliang Li","slug":"yongliang-li","given":"Yongliang","family":"Li","affiliation":"Alibaba Group , Hangzhou , China"},{"name":"Beidou Wang","slug":"beidou-wang","given":"Beidou","family":"Wang","affiliation":"State Key Lab of CAD&CG , College of Computer Science , Zhejiang University , China"},{"name":"Ziyu Guan","slug":"ziyu-guan","given":"Ziyu","family":"Guan","affiliation":"School of Information and Technology , Northwest University of China"},{"name":"Deng Cai","slug":"deng-cai","given":"Deng","family":"Cai","affiliation":"Alibaba-Zhejiang University Joint Institute of Frontier Technologies"}],"abstract":"In e-commerce websites like Taobao, brand is playing a more important role in influencing users' decision of click/purchase, partly because users are now attaching more importance to the quality of products and brand is an indicator of quality. However, existing ranking systems are not specifically designed to satisfy this kind of demand. Some design tricks may partially alleviate this problem, but still cannot provide satisfactory results or may create additional interaction cost. In this paper, we design the first brand-level ranking system to address this problem. The key challenge of this system is how to sufficiently exploit users' rich behavior in e-commerce websites to rank the brands. In our solution, we firstly conduct the feature engineering specifically tailored for the personalized brand ranking problem and then rank the brands by an adapted Attention-GRU model containing three important modifications. Note that our proposed modifications can also apply to many other machine learning models on various tasks. We conduct a series of experiments to evaluate the effectiveness of our proposed ranking model and test the response to the brand-level ranking system from real users on a large-scale e-commerce platform, i.e. Taobao.","doi":"10.24963/ijcai.2018/549","url":"https://ijcai.org/proceedings/2018/549","pdf":"https://ijcai.org/proceedings/2018/0549.pdf","markdown":"https://ijcai.org/proceedings/2018/549.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/549","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":"Multidisciplinary Topics and Applications","track":"Main track","number":549,"pages":{"first":3947,"last":3953},"keywords":["Machine Learning: Neural Networks","Machine Learning: Recommender Systems","Multidisciplinary Topics and Applications: Information Retrieval","Machine Learning: Learning Preferences or Rankings"],"videos":[],"references":[{"position":1,"raw":"[Berger, 2013] James O Berger. 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