Search Results for author: Lianghao Xia

Found 9 papers, 8 papers with code

Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Networks

1 code implementation8 Oct 2021 Huance Xu, Chao Huang, Yong Xu, Lianghao Xia, Hao Xing, Dawei Yin

Social recommendation which aims to leverage social connections among users to enhance the recommendation performance.

Recommendation Systems

Social Recommendation with Self-Supervised Metagraph Informax Network

1 code implementation8 Oct 2021 Xiaoling Long, Chao Huang, Yong Xu, Huance Xu, Peng Dai, Lianghao Xia, Liefeng Bo

To model relation heterogeneity, we design a metapath-guided heterogeneous graph neural network to aggregate feature embeddings from different types of meta-relations across users and items, em-powering SMIN to maintain dedicated representations for multi-faceted user- and item-wise dependencies.

Collaborative Filtering Recommendation Systems

Graph Meta Network for Multi-Behavior Recommendation

1 code implementation8 Oct 2021 Lianghao Xia, Yong Xu, Chao Huang, Peng Dai, Liefeng Bo

Modern recommender systems often embed users and items into low-dimensional latent representations, based on their observed interactions.

Meta-Learning Recommendation Systems +1

Knowledge-aware Coupled Graph Neural Network for Social Recommendation

1 code implementation8 Oct 2021 Chao Huang, Huance Xu, Yong Xu, Peng Dai, Lianghao Xia, Mengyin Lu, Liefeng Bo, Hao Xing, Xiaoping Lai, Yanfang Ye

While many recent efforts show the effectiveness of neural network-based social recommender systems, several important challenges have not been well addressed yet: (i) The majority of models only consider users' social connections, while ignoring the inter-dependent knowledge across items; (ii) Most of existing solutions are designed for singular type of user-item interactions, making them infeasible to capture the interaction heterogeneity; (iii) The dynamic nature of user-item interactions has been less explored in many social-aware recommendation techniques.

Collaborative Filtering Recommendation Systems

Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation

no code implementations8 Oct 2021 Chao Huang, Jiahui Chen, Lianghao Xia, Yong Xu, Peng Dai, Yanqing Chen, Liefeng Bo, Jiashu Zhao, Jimmy Xiangji Huang

The learning process of intra- and inter-session transition dynamics are integrated, to preserve the underlying low- and high-level item relationships in a common latent space.

Multi-Task Learning Session-Based Recommendations

Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation

1 code implementation8 Oct 2021 Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Xiyue Zhang, Hongsheng Yang, Jian Pei, Liefeng Bo

In particular: i) complex inter-dependencies across different types of user behaviors; ii) the incorporation of knowledge-aware item relations into the multi-behavior recommendation framework; iii) dynamic characteristics of multi-typed user-item interactions.

Graph Attention Recommendation Systems

Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network

1 code implementation8 Oct 2021 Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Bo Zhang, Liefeng Bo

The overlook of multiplex behavior relations can hardly recognize the multi-modal contextual signals across different types of interactions, which limit the feasibility of current recommendation methods.

Recommendation Systems

Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network

1 code implementation8 Oct 2021 Xiyue Zhang, Chao Huang, Yong Xu, Lianghao Xia, Peng Dai, Liefeng Bo, Junbo Zhang, Yu Zheng

Accurate forecasting of citywide traffic flow has been playing critical role in a variety of spatial-temporal mining applications, such as intelligent traffic control and public risk assessment.

Traffic Prediction

Spatial-Temporal Sequential Hypergraph Network for Crime Prediction

1 code implementation IJCAI 2021 Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Liefeng Bo, Xiyue Zhang, Tianyi Chen

Crime prediction is crucial for public safety and resource optimization, yet is very challenging due to two aspects: i) the dynamics of criminal patterns across time and space, crime events are distributed unevenly on both spatial and temporal domains; ii) time-evolving dependencies between different types of crimes (e. g., Theft, Robbery, Assault, Damage) which reveal fine-grained semantics of crimes.

Crime Prediction

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