Detecting communities from heterogeneous graphs: A context path-based graph neural network model

L Luo, Y Fang, X Cao, X Zhang, W Zhang - Proceedings of the 30th ACM …, 2021 - dl.acm.org
Community detection, aiming to group the graph nodes into clusters with dense inner-
connection, is a fundamental graph mining task. Recently, it has been studied on the …

User recommendation in social metaverse with VR

BJ Chen, DN Yang - Proceedings of the 31st ACM International …, 2022 - dl.acm.org
Social metaverse with VR has been viewed as a paradigm shift for social media. However,
most traditional VR social platforms ignore emerging characteristics in a metaverse, thereby …

Reciprocal Sequential Recommendation

B Zheng, Y Hou, WX Zhao, Y Song, H Zhu - Proceedings of the 17th …, 2023 - dl.acm.org
Reciprocal recommender system (RRS), considering a two-way matching between two
parties, has been widely applied in online platforms like online dating and recruitment …

[HTML][HTML] Motif-based graph attentional neural network for web service recommendation

G Wang, J Yu, M Nguyen, Y Zhang… - Knowledge-Based …, 2023 - Elsevier
Abstract Deep Neural Networks (DNN) based collaborative filtering has been successful in
recommending services by effectively generalizing graph-structured data. However, most …

MAMDR: A model agnostic learning framework for multi-domain recommendation

L Luo, Y Li, B Gao, S Tang, S Wang, J Li… - 2023 IEEE 39th …, 2023 - ieeexplore.ieee.org
Large-scale e-commercial platforms in the real-world usually contain various
recommendation scenarios (domains) to meet demands of diverse customer groups. Multi …

LLM-Powered Explanations: Unraveling Recommendations Through Subgraph Reasoning

G Shi, X Deng, L Luo, L Xia, L Bao, B Ye, F Du… - arXiv preprint arXiv …, 2024 - arxiv.org
Recommender systems are pivotal in enhancing user experiences across various web
applications by analyzing the complicated relationships between users and items …

MAMDR: a model agnostic learning method for multi-domain recommendation

L Luo, Y Li, B Gao, S Tang, S Wang, J Li, T Zhu… - arXiv preprint arXiv …, 2022 - arxiv.org
Large-scale e-commercial platforms in the real-world usually contain various
recommendation scenarios (domains) to meet demands of diverse customer groups. Multi …

GSim: a graph neural network based relevance measure for heterogeneous graphs

L Luo, Y Fang, M Lu, X Cao, X Zhang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Heterogeneous graphs, which contain nodes and edges of multiple types, are prevalent in
various domains, including bibliographic networks, social media, and knowledge graphs. As …

H-mgsr: a hierarchical motif-based graph attention neural network for service recommendation

X Zheng, G Wang, J Zhang, Y Zhang… - … conference on web …, 2023 - ieeexplore.ieee.org
The rapid development of web services has made it increasingly challenging for developers
to find desired web services. To address this issue, researchers have developed various …

DCRS: a deep contrast reciprocal recommender system to simultaneously capture user interest and attractiveness for online dating

L Luo, X Zhang, X Chen, K Liu, D Peng… - Neural Computing and …, 2022 - Springer
Recently, the reciprocal recommendation, especially for online dating applications, has
attracted increasing research attention. Different from the conventional recommendation …