{"type":"ScholarlyArticle","title":"Matching User with Item Set: Collaborative Bundle Recommendation with Deep Attention Network","authors":[{"name":"Liang Chen","slug":"liang-chen","given":"Liang","family":"Chen","affiliation":"School of Data and Computer Science , Sun Yat-sen University , Guangzhou , China; Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion , The school of Communication and Design , Sun Yat-sen University , Guangzhou , China"},{"name":"Yang Liu","slug":"yang-liu","given":"Yang","family":"Liu","affiliation":"School of Data and Computer Science , Sun Yat-sen University , Guangzhou , China; Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion , The school of Communication and Design , Sun Yat-sen University , Guangzhou , China"},{"name":"Xiangnan He","slug":"xiangnan-he","given":"Xiangnan","family":"He","affiliation":"University of Science and Technology of China"},{"name":"Lianli Gao","slug":"lianli-gao","given":"Lianli","family":"Gao","affiliation":"The University of Electronic Science and Technology of China"},{"name":"Zibin Zheng","slug":"zibin-zheng","given":"Zibin","family":"Zheng","affiliation":"School of Data and Computer Science , Sun Yat-sen University , Guangzhou , China; Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion , The school of Communication and Design , Sun Yat-sen University , Guangzhou , China"}],"abstract":"Most recommendation research has been concentrated on recommending single items to users, such as the considerable work on collaborative filtering that models the interaction between a user and an item. However, in many real-world scenarios, the platform needs to show users a set of items, e.g., the marketing strategy that offers multiple items for sale as one bundle.In this work, we consider recommending a set of items to a user, i.e., the Bundle Recommendation task, which concerns the interaction modeling between a user and a set of items. We contribute a neural network solution named DAM, short for Deep Attentive Multi-Task model, which is featured with two special designs: 1) We design a factorized attention network to aggregate the item embeddings in a bundle to obtain the bundle's representation; 2) We jointly model user-bundle interactions and user-item interactions in a multi-task manner to alleviate the scarcity of user-bundle interactions. Extensive experiments on a real-world dataset show that DAM outperforms the state-of-the-art solution, verifying the effectiveness of our attention design and multi-task learning in DAM.","doi":"10.24963/ijcai.2019/290","url":"https://ijcai.org/proceedings/2019/290","pdf":"https://ijcai.org/proceedings/2019/0290.pdf","markdown":"https://ijcai.org/proceedings/2019/290.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/290","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning A-L","track":"Main track","number":290,"pages":{"first":2095,"last":2101},"keywords":["Machine Learning: Recommender Systems","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Beladev et al., 2016] Moran Beladev, Lior Rokach, and Bracha Shapira. 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