1 code implementation • 28 May 2024 • Zhonghang Li, Lianghao Xia, Yong Xu, Chao Huang
Additionally, we incorporate a distribution mapping mechanism to align the data distributions of pre-training and downstream data, facilitating effective knowledge transfer in spatio-temporal forecasting.
1 code implementation • 4 Apr 2024 • Xubin Ren, Wei Wei, Lianghao Xia, Chao Huang
Recommender systems play a crucial role in tackling the challenge of information overload by delivering personalized recommendations based on individual user preferences.
1 code implementation • 25 Mar 2024 • Qianru Zhang, Lianghao Xia, Xuheng Cai, SiuMing Yiu, Chao Huang, Christian S. Jensen
To address these challenges, we propose a principled framework called GraphAug.
1 code implementation • 2 Mar 2024 • Lianghao Xia, Ben Kao, Chao Huang
By effectively capturing the graph's underlying structure, these GNNs have shown great potential in enhancing performance in graph learning tasks, such as link prediction and node classification.
1 code implementation • 27 Feb 2024 • Wei Wei, Jiabin Tang, Yangqin Jiang, Lianghao Xia, Chao Huang
Additionally, to adjust the impact of inaccuracies in multimedia data, a disentangled multi-modal list-wise distillation is developed with modality-aware re-weighting mechanism.
1 code implementation • 25 Feb 2024 • Zhonghang Li, Lianghao Xia, Jiabin Tang, Yong Xu, Lei Shi, Long Xia, Dawei Yin, Chao Huang
These findings highlight the potential of building large language models for spatio-temporal learning, particularly in zero-shot scenarios where labeled data is scarce.
1 code implementation • 23 Feb 2024 • Zirui Guo, Lianghao Xia, Yanhua Yu, Yuling Wang, Zixuan Yang, Wei Wei, Liang Pang, Tat-Seng Chua, Chao Huang
Graph Structure Learning (GSL) focuses on capturing intrinsic dependencies and interactions among nodes in graph-structured data by generating novel graph structures.
1 code implementation • 28 Dec 2023 • Yangqin Jiang, Yuhao Yang, Lianghao Xia, Chao Huang
To bridge this research gap, we propose a novel knowledge graph diffusion model for recommendation, referred to as DiffKG.
2 code implementations • 28 Nov 2023 • Yuhao Yang, Lianghao Xia, Da Luo, Kangyi Lin, Chao Huang
The temporal prompt mechanism encodes time information on user-item interaction, allowing the model to naturally capture temporal context, while the graph-structural prompt learning mechanism enables the transfer of pre-trained knowledge to adapt to behavior dynamics without the need for continuous incremental training.
1 code implementation • NeurIPS 2023 • Zhonghang Li, Lianghao Xia, Yong Xu, Chao Huang
This strategy guides the mask autoencoder in learning robust spatio-temporal representations and facilitates the modeling of different relationships, ranging from intra-cluster to inter-cluster, in an easy-to-hard training manner.
1 code implementation • 26 Oct 2023 • Jiabin Tang, Lianghao Xia, Chao Huang
Furthermore, we propose a structure distillation approach based on the Graph Information Bottleneck (GIB) principle with an explainable objective, which is instantiated by the STG encoder and decoder.
1 code implementation • 26 Oct 2023 • Jiabin Tang, Lianghao Xia, Jie Hu, Chao Huang
Although recent STGNN models with contrastive learning aim to address these challenges, most of them use pre-defined augmentation strategies that heavily depend on manual design and cannot be customized for different Spatio-Temporal Graph (STG) scenarios.
1 code implementation • 24 Oct 2023 • Xubin Ren, Wei Wei, Lianghao Xia, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, Chao Huang
RLMRec incorporates auxiliary textual signals, develops a user/item profiling paradigm empowered by LLMs, and aligns the semantic space of LLMs with the representation space of collaborative relational signals through a cross-view alignment framework.
1 code implementation • 3 Sep 2023 • Wei Wei, Lianghao Xia, Chao Huang
Personalized recommender systems play a crucial role in capturing users' evolving preferences over time to provide accurate and effective recommendations on various online platforms.
1 code implementation • 22 Aug 2023 • Xuheng Cai, Lianghao Xia, Xubin Ren, Chao Huang
Most existing works adopt the graph isomorphism test as the metric of expressiveness, but this graph-level task may not effectively assess a model's ability in recommendation, where the objective is to distinguish nodes of different closeness.
1 code implementation • 10 Aug 2023 • Xubin Ren, Lianghao Xia, Yuhao Yang, Wei Wei, Tianle Wang, Xuheng Cai, Chao Huang
Our SSLRec platform covers a comprehensive set of state-of-the-art SSL-enhanced recommendation models across different scenarios, enabling researchers to evaluate these cutting-edge models and drive further innovation in the field.
1 code implementation • 6 Jul 2023 • Yuhao Yang, Chao Huang, Lianghao Xia, Chunzhen Huang
By masking important knowledge with high rational scores, KGRec is trained to rebuild and highlight useful knowledge connections that serve as rationales.
1 code implementation • 19 Jun 2023 • Qianru Zhang, Chao Huang, Lianghao Xia, Zheng Wang, SiuMing Yiu, Ruihua Han
In addition, we introduce a cross-view contrastive learning paradigm to model the inter-dependencies across view-specific region representations and preserve underlying relation heterogeneity.
1 code implementation • 4 Jun 2023 • Chaoliu Li, Lianghao Xia, Xubin Ren, Yaowen Ye, Yong Xu, Chao Huang
This paper presents a novel approach to representation learning in recommender systems by integrating generative self-supervised learning with graph transformer architecture.
1 code implementation • 22 May 2023 • Tianle Wang, Lianghao Xia, Chao Huang
Social recommendation is gaining increasing attention in various online applications, including e-commerce and online streaming, where social information is leveraged to improve user-item interaction modeling.
2 code implementations • 18 May 2023 • Yangqin Jiang, Chao Huang, Lianghao Xia
These approaches conduct self-supervised learning through creating contrastive views, but they depend on the tedious trial-and-error selection of augmentation methods.
2 code implementations • 8 May 2023 • Yaowen Ye, Lianghao Xia, Chao Huang
While some powerful neural network architectures (e. g., Transformer, Graph Neural Networks) have achieved improved performance in sequential recommendation with high-order item dependency modeling, they may suffer from poor representation capability in label scarcity scenarios.
1 code implementation • 6 May 2023 • Qianru Zhang, Chao Huang, Lianghao Xia, Zheng Wang, Zhonghang Li, SiuMing Yiu
In this paper, we tackle the above challenges by exploring the Automated Spatio-Temporal graph contrastive learning paradigm (AutoST) over the heterogeneous region graph generated from multi-view data sources.
1 code implementation • 4 May 2023 • Xubin Ren, Lianghao Xia, Jiashu Zhao, Dawei Yin, Chao Huang
Recent studies show that graph neural networks (GNNs) are prevalent to model high-order relationships for collaborative filtering (CF).
1 code implementation • 21 Mar 2023 • Yuhao Yang, Chao Huang, Lianghao Xia, Chunzhen Huang, Da Luo, Kangyi Lin
This solution is designed to tackle the popularity bias issue in recommendation systems.
1 code implementation • 15 Mar 2023 • Lianghao Xia, Chao Huang, Jiao Shi, Yong Xu
Motivated by these limitations, we propose a simple and effective collaborative filtering model (SimRec) that marries the power of knowledge distillation and contrastive learning.
2 code implementations • 14 Mar 2023 • Lianghao Xia, Chao Huang, Chunzhen Huang, Kangyi Lin, Tao Yu, Ben Kao
This does not generalize across different datasets and downstream recommendation tasks, which is difficult to be adaptive for data augmentation and robust to noise perturbation.
1 code implementation • 14 Mar 2023 • Lianghao Xia, Yizhen Shao, Chao Huang, Yong Xu, Huance Xu, Jian Pei
In this work, we design a Disentangled Graph Neural Network (DGNN) with the integration of latent memory units, which empowers DGNN to maintain factorized representations for heterogeneous types of user and item connections.
1 code implementation • 2 Mar 2023 • Mengru Chen, Chao Huang, Lianghao Xia, Wei Wei, Yong Xu, Ronghua Luo
In light of this, we propose a Heterogeneous Graph Contrastive Learning (HGCL), which is able to incorporate heterogeneous relational semantics into the user-item interaction modeling with contrastive learning-enhanced knowledge transfer across different views.
2 code implementations • 21 Feb 2023 • Wei Wei, Chao Huang, Lianghao Xia, Chuxu Zhang
The online emergence of multi-modal sharing platforms (eg, TikTok, Youtube) is powering personalized recommender systems to incorporate various modalities (eg, visual, textual and acoustic) into the latent user representations.
no code implementations • 17 Feb 2023 • Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Liefeng Bo
Recent years have witnessed the emerging success of many deep learning-based recommendation models for augmenting collaborative filtering architectures with various neural network architectures, such as multi-layer perceptron and autoencoder.
1 code implementation • 16 Feb 2023 • Xuheng Cai, Chao Huang, Lianghao Xia, Xubin Ren
In this paper, we propose a simple yet effective graph contrastive learning paradigm LightGCL that mitigates these issues impairing the generality and robustness of CL-based recommenders.
1 code implementation • 28 Jul 2022 • Lianghao Xia, Chao Huang, Chuxu Zhang
With the distilled global context, a cross-view generative self-supervised learning component is proposed for data augmentation over the user-item interaction graph, so as to enhance the robustness of recommender systems.
1 code implementation • 12 Jul 2022 • Yuhao Yang, Chao Huang, Lianghao Xia, Yuxuan Liang, Yanwei Yu, Chenliang Li
Further ablation studies validate the effectiveness of our model design and benefits of the new MBHT framework.
1 code implementation • 6 Jun 2022 • Lianghao Xia, Chao Huang, Yong Xu, Jian Pei
The new TGT method endows the sequential recommendation architecture to distill dedicated knowledge for type-specific behavior relational context and the implicit behavior dependencies.
1 code implementation • 2 May 2022 • Yuhao Yang, Chao Huang, Lianghao Xia, Chenliang Li
However, the success of such methods relies on the high quality knowledge graphs, and may not learn quality representations with two challenges: i) The long-tail distribution of entities results in sparse supervision signals for KG-enhanced item representation; ii) Real-world knowledge graphs are often noisy and contain topic-irrelevant connections between items and entities.
1 code implementation • 26 Apr 2022 • Lianghao Xia, Chao Huang, Yong Xu, Jiashu Zhao, Dawei Yin, Jimmy Xiangji Huang
Additionally, our HCCF model effectively integrates the hypergraph structure encoding with self-supervised learning to reinforce the representation quality of recommender systems, based on the hypergraph-enhanced self-discrimination.
1 code implementation • 18 Apr 2022 • Zhonghang Li, Chao Huang, Lianghao Xia, Yong Xu, Jian Pei
Crime has become a major concern in many cities, which calls for the rising demand for timely predicting citywide crime occurrence.
1 code implementation • 17 Feb 2022 • Wei Wei, Chao Huang, Lianghao Xia, Yong Xu, Jiashu Zhao, Dawei Yin
In addition, to capture the diverse multi-behavior patterns, we design a contrastive meta network to encode the customized behavior heterogeneity for different users.
1 code implementation • 10 Jan 2022 • Lianghao Xia, Chao Huang, Yong Xu, Huance Xu, Xiang Li, WeiGuo Zhang
As the deep learning techniques have expanded to real-world recommendation tasks, many deep neural network based Collaborative Filtering (CF) models have been developed to project user-item interactions into latent feature space, based on various neural architectures, such as multi-layer perceptron, auto-encoder and graph neural networks.
1 code implementation • 7 Jan 2022 • Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Mengyin Lu, Liefeng Bo
Due to the overlook of user's multi-behavioral patterns over different items, existing recommendation methods are insufficient to capture heterogeneous collaborative signals from user multi-behavior data.
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.
1 code implementation • 8 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.
1 code implementation • 8 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.
1 code implementation • 8 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.
1 code implementation • 8 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.
1 code implementation • 8 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.
1 code implementation • 8 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.
1 code implementation • 8 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.
1 code implementation • 8 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.