{"type":"ScholarlyArticle","title":"Intention2Basket: A Neural Intention-driven Approach for Dynamic Next-basket Planning","authors":[{"name":"Shoujin Wang","slug":"shoujin-wang","given":"Shoujin","family":"Wang","affiliation":"Department of Computing , Macquarie University"},{"name":"Liang Hu","slug":"liang-hu","given":"Liang","family":"Hu","affiliation":"Advanced Analytics Institute , University of Technology Sydney; University of Shanghai for Science and Technology"},{"name":"Yan Wang","slug":"yan-wang","given":"Yan","family":"Wang","affiliation":"Department of Computing , Macquarie University"},{"name":"Quan Z. Sheng","slug":"quan-z-sheng","given":"Quan Z.","family":"Sheng","affiliation":"Department of Computing , Macquarie University"},{"name":"Mehmet Orgun","slug":"mehmet-orgun","given":"Mehmet","family":"Orgun","affiliation":"Department of Computing , Macquarie University"},{"name":"Longbing Cao","slug":"longbing-cao","given":"Longbing","family":"Cao","affiliation":"Advanced Analytics Institute , University of Technology Sydney"}],"abstract":"User  purchase  behaviours  are  complex  and  dynamic,  which  are  usually  observed  as  multiple choice actions across a sequence of shopping baskets.   Most of the existing next-basket prediction approaches model user actions as homogeneous sequence data without considering complex and heterogeneous user intentions, impeding deep under-standing of user behaviours from the perspective of human inside drivers and thus reducing the prediction performance.  Psychological theories have indicated that user actions are essentially driven by certain underlying intentions (e.g., diet and entertainment).  Moreover, different intentions may influence each other while different choices usually have different utilities to accomplish an  intention.   Inspired by such psychological insights, we formalize the next-basket prediction as an Intention Recognition, Modelling and Accomplishing problem and  further design the Intention2Basket (Int2Ba in short) model. In Int2Ba, an Intention Recognizer, a Coupled Intention Chain Net, and a Dynamic Basket Planner are specifically designed to respectively recognize, model and accomplish the heterogeneous intentions behind a sequence of baskets to better plan the next-basket. Extensive experiments on real-world datasets show the superiority of Int2Ba over the state-of-the-art approaches.","doi":"10.24963/ijcai.2020/323","url":"https://ijcai.org/proceedings/2020/323","pdf":"https://ijcai.org/proceedings/2020/0323.pdf","markdown":"https://ijcai.org/proceedings/2020/323.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/323","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Machine Learning","track":"Main track","number":323,"pages":{"first":2333,"last":2339},"keywords":["Data Mining: Applications","Humans and AI: Personalization and User Modeling","Machine Learning: Recommender Systems","Multidisciplinary Topics and Applications: Recommender Systems"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24052/file_24052.m3u8","length_sec":300,"provider":"hls"}],"references":[{"position":1,"raw":"[Ajzen et al., 2009] Icek Ajzen, Cornelia Czasch, and et al. 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