Adaptive Graph Representation Learning for Next POI Recommendation
Figure 1 from Adaptive Graph Representation Learning for Next POI
Adaptive Graph Representation Learning for Next POI Recommendation
Adaptive Graph Representation Learning for Next POI Recommendation
Learning Graph-based Disentangled Representations for Next POI
Electronics
COMMENTS
Adaptive Graph Representation Learning for Next POI Recommendation
the video of "Adaptive Graph Representation Learning for Next POI Recommendation" Download; 21.07 MB; ... Learning Graph-based Disentangled Representations for Next POI Recommendation. In SIGIR. 1154--1163. Google Scholar [33] Zhaobo Wang, Yanmin Zhu, Qiaomei Zhang, Haobing Liu, Chunyang Wang, and Tong Liu. 2022b. Graph-enhanced spatial ...
GitHub
To tackle these challenges, we propose a novel framework Adaptive Spatial-Temporal Hypergraph Fusion Learning (ASTHL) for next POI recommendation. Specifically, we design disentangled POI-centric learning to decouple spatial-temporal factors and utilize cross-view contrastive learning to enhance the quality of POI representations.
Adaptive Graph Representation Learning for Next POI Recommendation
A novel Adaptive Graph Representation-enhanced Attention Network (AGRAN) for next POI recommendation is proposed, which explores the utilization of graph structure learning to replace the pre-defined static graphs for learning more expressive representations of POIs. Next Point-of-Interest (POI) recommendation is an essential part of the flourishing location-based applications, where the ...
GitHub
This repository collects papers and resources about point-of-interest (POI) recommendation, a subfield of recommender systems. It includes a paper on adaptive graph representation learning for next POI recommendation, which is a method to learn graph representations that adapt to the user's preferences and context.
arXiv:2106.15814v2 [cs.IR] 27 Apr 2022
The paper proposes a novel method to discover collaborative signals for next point-of-interest (POI) recommendation with iterative sequence-to-graph augmentation. It jointly learns POI embeddings and user preferences from graph-augmented POI sequences and category-aware transitions.
Adaptive Graph Representation Learning for Next POI Recommendation
Request PDF | On Jul 23, 2023, Zhaobo Wang and others published Adaptive Graph Representation Learning for Next POI Recommendation | Find, read and cite all the research you need on ResearchGate
Adaptive Graph Representation Learning for Next POI Recommendation
Next Point-of-Interest (POI) recommendation is an essential part of the flourishing location-based applications, where the demands of users are not only conditioned by their recent check-in behaviors but also by the critical influence stemming from geographical dependencies among POIs. Existing methods leverage Graph Neural Networks with the aid of pre-defined POI graphs to capture such ...
SNPR
Next Point-of-Interest (POI) recommendation plays an important role in location-based services. The state-of-the-art methods utilize recurrent neural networks (RNNs) to model users' check-in sequences and have shown promising results. However, they tend to recommend POIs similar to those that the user has often visited.
Adaptive Graph Representation Learning for Next POI Recommendation
(DOI: 10.1145/3539618.3591634) Next Point-of-Interest (POI) recommendation is an essential part of the flourishing location-based applications, where the demands of users are not only conditioned by their recent check-in behaviors but also by the critical influence stemming from geographical dependencies among POIs. Existing methods leverage Graph Neural Networks with the aid of pre-defined ...
GitHub
This is the implementation for the paper: "Adaptive Graph Representation Learning for Next POI Recommendation." SIGIR 2023. Datasets. We use Foursquare and Gowalla datasets. For more details of data preprocessing, please refer to our paper. Model Training.
Adaptive Spatial-Temporal Hypergraph Fusion Learning for Next POI
A novel framework for next point-of-interest (POI) recommendation based on adaptive spatial-temporal hypergraph fusion learning. The framework decouples spatial-temporal factors, enhances POI representations, and fuses them through multi-semantic hypergraph learning.
Learning Graph-based Disentangled Representations for Next POI
Adaptive Graph Representation Learning for Next POI Recommendation. ... Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POI Recommendations Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 10.1145/3637528.3671743 (2026-2036) Online publication date: 25-Aug-2024.
A Next POI Recommendation Based on Graph Convolutional Network by
This. paper focuses on the next point-of-interest (POI) recommendation task in LBSNs. We argue that. existing graph-based POI recommendation methods only consider user preferences from several ...
Bi-Level Graph Structure Learning for Next POI Recommendation
This paper proposes a novel method to learn hierarchical graphs for next point-of-interest (POI) recommendation using graph neural networks. The method improves the recommendation accuracy and exploration performance by capturing the fine-to-coarse connectivity and the multi-relational graph structure of POIs and prototypes.
Discovering Collaborative Signals for Next POI Recommendation with
Being an indispensable component in location-based social networks, next point-of-interest (POI) recommendation recommends users unexplored POIs based on their recent visiting histories. However, existing work mainly models check-in data as isolated POI sequences, neglecting the crucial collaborative signals from cross-sequence check-in information. Furthermore, the sparse POI-POI transitions ...
Graph-Enhanced Spatial-Temporal Network for Next POI Recommendation
In Proceedings of the 4th International Conference on Learning Representations, ICLR 2016 ... Ke Li, Guoshuai Zhao, and Xueming Qian. 2019. Long- and short-term preference learning for next POI recommendation. In Proceedings of the 28th ACM ... Learning graph-based POI embedding for location-based recommendation. In Proceedings of the 25th ...
Adaptive Graph Representation Learning for Next POI Recommendation
Adaptive Graph Representation Learning for Next POI Recommendation; research-article . Share on. Adaptive Graph Representation Learning for Next POI Recommendation ...
Adaptive Graph Representation Learning for Next POI Recommendation
Adaptive Graph Representation Learning for Next POI Recommendation. In Hsin-Hsi Chen , Wei-Jou (Edward) Duh , Hen-Hsen Huang , Makoto P. Kato , Josiane Mothe , Barbara Poblete , editors, Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, Taipei, Taiwan, July 23-27, 2023 .
Adaptive Graph Representation Learning for Next POI Recommendation
Figure 2: The overall architecture of AGRAN. We learn an adaptive graph to depict geographical dependencies among POIs for obtaining expressive representations and introduce adaptive graph regularization to refine the learned graph. With the learned POI representations, we aggregate multiple dependencies with the self-attention mechanism for user preference estimation. - "Adaptive Graph ...
A survey on graph neural network-based next POI recommendation for
Wang Z, Zhu Y, Wang C, Ma W, Li B, Yu J (2023) Adaptive graph representation learning for next POI recommendation. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR '23, pp. 393-402. Association for Computing Machinery, New York, NY, USA.
KDD'23｜Adaptive Graph Contrastive Learning for Recommendation
KDD'23｜Adaptive Graph Contrastive Learning for Recommendation. 一、Motivation. 图对比学习虽然已经有了很好的效果，但是里面的噪声信息没有经过很好的处理，因此有可能产生误导使得学习的结果不够准确。. 在此基础上作者提出了一个对比学习方法，去燥生成器作用下的两种 ...
MMPOI: A Multi-Modal Content-Aware Framework for POI Recommendations
Adaptive Graph Representation Learning for Next POI Recommendation. In SIGIR. 393--402. Google Scholar [33] Wei Wei, Chao Huang, Lianghao Xia, and Chuxu Zhang. 2023. Multi-Modal Self-Supervised Learning for Recommendation. ... Jianshan He, Jiaotuan Wang, Ruopeng Li, and Wei Chu. 2023. Spatio-Temporal Hypergraph Learning for Next POI ...
Location Representations for Accelerating the Training of Next POI
The proposed location representations that contain relative location information of all POIs can be applied to initialize POI recommendation models to accelerate model convergence. Experiments on our approach show that the proposed method on a sequential recommendation model improves Hit@10 by 4%, and the convergence rate (average regret before ...
Defending Against Membership Inference Attack for Counterfactual
As such, tailored to the specifics of private learning, we propose a counterfactual interactive recommendation system that builds a differentially private representation learning based defender (CIRDP) to capture and mitigate the adversarial threats, augmenting causal inference-based interactive recommendation of FedRecs.
IMAGES
COMMENTS
the video of "Adaptive Graph Representation Learning for Next POI Recommendation" Download; 21.07 MB; ... Learning Graph-based Disentangled Representations for Next POI Recommendation. In SIGIR. 1154--1163. Google Scholar [33] Zhaobo Wang, Yanmin Zhu, Qiaomei Zhang, Haobing Liu, Chunyang Wang, and Tong Liu. 2022b. Graph-enhanced spatial ...
To tackle these challenges, we propose a novel framework Adaptive Spatial-Temporal Hypergraph Fusion Learning (ASTHL) for next POI recommendation. Specifically, we design disentangled POI-centric learning to decouple spatial-temporal factors and utilize cross-view contrastive learning to enhance the quality of POI representations.
A novel Adaptive Graph Representation-enhanced Attention Network (AGRAN) for next POI recommendation is proposed, which explores the utilization of graph structure learning to replace the pre-defined static graphs for learning more expressive representations of POIs. Next Point-of-Interest (POI) recommendation is an essential part of the flourishing location-based applications, where the ...
This repository collects papers and resources about point-of-interest (POI) recommendation, a subfield of recommender systems. It includes a paper on adaptive graph representation learning for next POI recommendation, which is a method to learn graph representations that adapt to the user's preferences and context.
The paper proposes a novel method to discover collaborative signals for next point-of-interest (POI) recommendation with iterative sequence-to-graph augmentation. It jointly learns POI embeddings and user preferences from graph-augmented POI sequences and category-aware transitions.
Request PDF | On Jul 23, 2023, Zhaobo Wang and others published Adaptive Graph Representation Learning for Next POI Recommendation | Find, read and cite all the research you need on ResearchGate
Next Point-of-Interest (POI) recommendation is an essential part of the flourishing location-based applications, where the demands of users are not only conditioned by their recent check-in behaviors but also by the critical influence stemming from geographical dependencies among POIs. Existing methods leverage Graph Neural Networks with the aid of pre-defined POI graphs to capture such ...
Next Point-of-Interest (POI) recommendation plays an important role in location-based services. The state-of-the-art methods utilize recurrent neural networks (RNNs) to model users' check-in sequences and have shown promising results. However, they tend to recommend POIs similar to those that the user has often visited.
(DOI: 10.1145/3539618.3591634) Next Point-of-Interest (POI) recommendation is an essential part of the flourishing location-based applications, where the demands of users are not only conditioned by their recent check-in behaviors but also by the critical influence stemming from geographical dependencies among POIs. Existing methods leverage Graph Neural Networks with the aid of pre-defined ...
This is the implementation for the paper: "Adaptive Graph Representation Learning for Next POI Recommendation." SIGIR 2023. Datasets. We use Foursquare and Gowalla datasets. For more details of data preprocessing, please refer to our paper. Model Training.
A novel framework for next point-of-interest (POI) recommendation based on adaptive spatial-temporal hypergraph fusion learning. The framework decouples spatial-temporal factors, enhances POI representations, and fuses them through multi-semantic hypergraph learning.
Adaptive Graph Representation Learning for Next POI Recommendation. ... Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POI Recommendations Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 10.1145/3637528.3671743 (2026-2036) Online publication date: 25-Aug-2024.
This. paper focuses on the next point-of-interest (POI) recommendation task in LBSNs. We argue that. existing graph-based POI recommendation methods only consider user preferences from several ...
This paper proposes a novel method to learn hierarchical graphs for next point-of-interest (POI) recommendation using graph neural networks. The method improves the recommendation accuracy and exploration performance by capturing the fine-to-coarse connectivity and the multi-relational graph structure of POIs and prototypes.
Being an indispensable component in location-based social networks, next point-of-interest (POI) recommendation recommends users unexplored POIs based on their recent visiting histories. However, existing work mainly models check-in data as isolated POI sequences, neglecting the crucial collaborative signals from cross-sequence check-in information. Furthermore, the sparse POI-POI transitions ...
In Proceedings of the 4th International Conference on Learning Representations, ICLR 2016 ... Ke Li, Guoshuai Zhao, and Xueming Qian. 2019. Long- and short-term preference learning for next POI recommendation. In Proceedings of the 28th ACM ... Learning graph-based POI embedding for location-based recommendation. In Proceedings of the 25th ...
Adaptive Graph Representation Learning for Next POI Recommendation; research-article . Share on. Adaptive Graph Representation Learning for Next POI Recommendation ...
Adaptive Graph Representation Learning for Next POI Recommendation. In Hsin-Hsi Chen , Wei-Jou (Edward) Duh , Hen-Hsen Huang , Makoto P. Kato , Josiane Mothe , Barbara Poblete , editors, Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, Taipei, Taiwan, July 23-27, 2023 .
Figure 2: The overall architecture of AGRAN. We learn an adaptive graph to depict geographical dependencies among POIs for obtaining expressive representations and introduce adaptive graph regularization to refine the learned graph. With the learned POI representations, we aggregate multiple dependencies with the self-attention mechanism for user preference estimation. - "Adaptive Graph ...
Wang Z, Zhu Y, Wang C, Ma W, Li B, Yu J (2023) Adaptive graph representation learning for next POI recommendation. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR '23, pp. 393-402. Association for Computing Machinery, New York, NY, USA.
KDD'23｜Adaptive Graph Contrastive Learning for Recommendation. 一、Motivation. 图对比学习虽然已经有了很好的效果，但是里面的噪声信息没有经过很好的处理，因此有可能产生误导使得学习的结果不够准确。. 在此基础上作者提出了一个对比学习方法，去燥生成器作用下的两种 ...
Adaptive Graph Representation Learning for Next POI Recommendation. In SIGIR. 393--402. Google Scholar [33] Wei Wei, Chao Huang, Lianghao Xia, and Chuxu Zhang. 2023. Multi-Modal Self-Supervised Learning for Recommendation. ... Jianshan He, Jiaotuan Wang, Ruopeng Li, and Wei Chu. 2023. Spatio-Temporal Hypergraph Learning for Next POI ...
The proposed location representations that contain relative location information of all POIs can be applied to initialize POI recommendation models to accelerate model convergence. Experiments on our approach show that the proposed method on a sequential recommendation model improves Hit@10 by 4%, and the convergence rate (average regret before ...
As such, tailored to the specifics of private learning, we propose a counterfactual interactive recommendation system that builds a differentially private representation learning based defender (CIRDP) to capture and mitigate the adversarial threats, augmenting causal inference-based interactive recommendation of FedRecs.