Intention2Basket: a neural intention-driven approach for dynamic next-basket planning

Shoujin Wang, Liang Hu, Yan Wang*, Quan Z. Sheng, Mehmet Orgun, Longbing Cao

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

12 Citations (Scopus)

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 understanding 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.

Original languageEnglish
Title of host publicationProceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020
EditorsChristian Bessiere
Place of PublicationCalifornia
PublisherInternational Joint Conferences on Artificial Intelligence
Pages2333-2339
Number of pages7
ISBN (Electronic)9780999241165
DOIs
Publication statusPublished - 2020
Event29th International Joint Conference on Artificial Intelligence, IJCAI 2020 - Yokohama, Japan
Duration: 1 Jan 2021 → …

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume2021-January
ISSN (Print)1045-0823

Conference

Conference29th International Joint Conference on Artificial Intelligence, IJCAI 2020
CountryJapan
CityYokohama
Period1/01/21 → …

Keywords

  • Recommender Systems
  • Personalization and User Modeling
  • Applications

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