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20212024
most citedTemporal aware Multi-Interest Graph Neural Network For Session-based Recommendation

7 citations · 8 across the 6 of their papers we have counts for

collaborators

6 papers

cs.AI2024

On the Essence and Prospect: An Investigation of Alignment Approaches for Big Models

Xinpeng Wang, Shitong Duan, Xiaoyuan Yi +7

Big models have achieved revolutionary breakthroughs in the field of AI, but they might also pose potential concerns. Addressing such concerns, alignment technologies were introduc…

cs.IR20231 cited

Text2Bundle: Towards Personalized Query-based Bundle Generation

Shixuan Zhu, Chuan Cui, JunTong Hu +3

Bundle generation aims to provide a bundle of items for the user, and has been widely studied and applied on online service platforms. Existing bundle generation methods mainly uti…

cs.IR2023

Towards Multi-Subsession Conversational Recommendation

Yu Ji, Qi Shen, Shixuan Zhu +4

Conversational recommendation systems (CRS) could acquire dynamic user preferences towards desired items through multi-round interactive dialogue. Previous CRS mainly focuses on th…

cs.CL2023

Large-Scale and Multi-Perspective Opinion Summarization with Diverse Review Subsets

Han Jiang, Rui Wang, Zhihua Wei +2

Opinion summarization is expected to digest larger review sets and provide summaries from different perspectives. However, most existing solutions are deficient in epitomizing exte…

cs.IR20217 cited

Temporal aware Multi-Interest Graph Neural Network For Session-based Recommendation

Qi Shen, Shixuan Zhu, Yitong Pang +2

Session-based recommendation (SBR) is a challenging task, which aims at recommending next items based on anonymous interaction sequences. Despite the superior performance of existi…

cs.IR2021

Intention Adaptive Graph Neural Network for Category-aware Session-based Recommendation

Chuan Cui, Qi Shen, Shixuan Zhu +4

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…