no code implementations • 27 Oct 2023 • Shixuan Zhu, Chuan Cui, JunTong Hu, Qi Shen, Yu Ji, Zhihua Wei
Bundle generation aims to provide a bundle of items for the user, and has been widely studied and applied on online service platforms.
no code implementations • 20 Oct 2023 • Yu Ji, Qi Shen, Shixuan Zhu, Hang Yu, Yiming Zhang, Chuan Cui, Zhihua Wei
Therefore, we propose a novel conversational recommendation scenario named Multi-Subsession Multi-round Conversational Recommendation (MSMCR), where user would still resort to CRS after several subsessions and might preserve vague interests, and system would proactively ask attributes to activate user interests in the current subsession.
no code implementations • 19 Oct 2022 • Shixuan Zhu, Qi Shen, Yiming Zhang, Zhenwei Dong, Zhihua Wei
In this paper, we propose a novel graph learning paradigm called Counterfactual Learning for Bundle Recommendation (CLBR) to mitigate the impact of data sparsity problem and improve bundle recommendation.
1 code implementation • 31 Dec 2021 • Chuan Cui, Qi Shen, Shixuan Zhu, Yitong Pang, Yiming Zhang, Hanning Gao, Zhihua Wei
Session-based recommendation (SBR) is proposed to recommend items within short sessions given that user profiles are invisible in various scenarios nowadays, such as e-commerce and short video recommendation.
no code implementations • 31 Dec 2021 • Qi Shen, Shixuan Zhu, Yitong Pang, Yiming Zhang, Zhihua Wei
Session-based recommendation (SBR) is a challenging task, which aims at recommending next items based on anonymous interaction sequences.