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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.
|Title of host publication||Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020|
|Place of Publication||California|
|Publisher||International Joint Conferences on Artificial Intelligence|
|Number of pages||7|
|Publication status||Published - 2020|
|Event||29th International Joint Conference on Artificial Intelligence, IJCAI 2020 - Yokohama, Japan|
Duration: 1 Jan 2021 → …
|Name||IJCAI International Joint Conference on Artificial Intelligence|
|Conference||29th International Joint Conference on Artificial Intelligence, IJCAI 2020|
|Period||1/01/21 → …|
- Recommender Systems
- Personalization and User Modeling
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