Graph learning based recommender systems: A review

S Wang, L Hu, Y Wang, X He, QZ Sheng… - ar** users to effectively retrieve items of their
interests from a large catalogue. For a quite long time, researchers and practitioners have …

Attention-based transactional context embedding for next-item recommendation

S Wang, L Hu, L Cao, X Huang, D Lian… - Proceedings of the AAAI …, 2018 - ojs.aaai.org
To recommend the next item to a user in a transactional context is practical yet challenging
in applications such as marketing campaigns. Transactional context refers to the items that …

Large language models for intent-driven session recommendations

Z Sun, H Liu, X Qu, K Feng, Y Wang… - Proceedings of the 47th …, 2024 - dl.acm.org
The goal of intent-aware session recommendation (ISR) approaches is to capture user
intents within a session for accurate next-item prediction. However, the capability of these …

Modeling multi-purpose sessions for next-item recommendations via mixture-channel purpose routing networks

S Wang, L Hu, Y Wang, QZ Sheng… - … joint conference on …, 2019 - opus.lib.uts.edu.au
© 2019 International Joint Conferences on Artificial Intelligence. All rights reserved. A
session-based recommender system (SBRS) suggests the next item by modeling the …

M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations

W Shalaby, S Oh, A Afsharinejad, S Kumar… - Proceedings of the 16th …, 2022 - dl.acm.org
Session-based recommender systems (SBRSs) have shown superior performance over
conventional methods. However, they show limited scalability on large-scale industrial …

Intention nets: psychology-inspired user choice behavior modeling for next-basket prediction

S Wang, L Hu, Y Wang, QZ Sheng, M Orgun… - Proceedings of the AAAI …, 2020 - ojs.aaai.org
Human behaviors are complex, which are often observed as a sequence of heterogeneous
actions. In this paper, we take user choices for shop** baskets as a typical case to study …