4.7 Article

KDRank: Knowledge-driven user-aware POI recommendation

Related references

Note: Only part of the references are listed.
Article Computer Science, Artificial Intelligence

Top-k Socio-Spatial Co-Engaged Location Selection for Social Users

Nur Al Hasan Haldar et al.

Summary: This paper introduces a new problem of selecting top-k Socio-Spatial co-engaged Location (SSLS) for users in a social graph. It has been proved as NP-hard and the authors propose both an exact solution and an approximate solution to address this challenging problem. Extensive experiments are conducted to evaluate the performance of the proposed algorithms against existing methods.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2023)

Article Automation & Control Systems

Uncertainty-Aware Multiview Deep Learning for Internet of Things Applications

Cai Xu et al.

Summary: This study presents an evidential multiview deep learning (EMDL) method to make reliable decisions in high-risk IoT and industrial applications. EMDL synthesizes multiple features to construct multiview common evidence and dynamically fuses different views to achieve reliable prediction.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2023)

Article Computer Science, Information Systems

GNN-based long and short term preference modeling for next-location prediction

Jinbo Liu et al.

Summary: Next-location prediction is a task of recommending the next POIs based on context-dependency such as sequential, temporal, and spatial dependencies. Existing methods fail to capture both temporal and location topology information, which motivated us to propose a GNN-based model that comprehensively represents dynamic preferences by converting POIs into a low-dimensional metric and integrating long-term and short-term user preferences. Our experimental results on real-world datasets demonstrate the effectiveness of our approach over state-of-the-art methods for next-location prediction.

INFORMATION SCIENCES (2023)

Article Computer Science, Artificial Intelligence

Dynamic graph convolutional recurrent imputation network for spatiotemporal traffic missing data

Xiangjie Kong et al.

Summary: In this study, a novel deep learning architecture named DGCRIN is designed to impute missing traffic data in real-world intelligent transportation systems. DGCRIN utilizes a graph generator and dynamic graph convolutional gated recurrent unit (DGCGRU) to model the dynamic spatiotemporal dependencies of the road network. An auxiliary GRU learns the missing pattern information, and a fusion layer with a decay mechanism is introduced to fuse diverse data. Extensive experiments on two datasets demonstrate the superiority of DGCRIN over multiple baseline models.

KNOWLEDGE-BASED SYSTEMS (2023)

Article Computer Science, Artificial Intelligence

Value-aware meta-transfer learning and convolutional mask attention networks for reservoir identification with limited data

Bingyang Chen et al.

Summary: This paper proposes a small sample reservoir identification method combining Convolutional Mask Attention Network (CMAN) and Value-aware Meta-Transfer Learning (VMTL) strategy, which performs well in feature extraction, overcoming geological differences, and insufficient samples.

EXPERT SYSTEMS WITH APPLICATIONS (2023)

Article Computer Science, Artificial Intelligence

Extrapolation over temporal knowledge graph via hyperbolic embedding

Yan Jia et al.

Summary: Predicting potential facts in the future is challenging due to the deep dependence between temporal association and semantic patterns of facts. Most existing methods fail to attach importance to impactful facts and events. Therefore, a novel model called ReTIN is proposed, which integrates real-time influence of historical facts based on hyperbolic geometry to effectively capture hierarchical relations among facts.

CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY (2023)

Article Computer Science, Artificial Intelligence

TransMKR: Translation-based knowledge graph enhanced multi-task point-of-interest recommendation

Bojing Hu et al.

Summary: In this paper, the TransMKR framework, which is a translation-based knowledge graph enhanced multi-task learning framework, is proposed for POI recommendation. The framework improves the KGE module of MKR and quantifies the relationship between POIs and their attributes using TransR. It also captures the deep associated attributes of POIs under different relations, achieving state-of-the-art performance on geographical location-based POI recommendation.

NEUROCOMPUTING (2022)

Article Computer Science, Information Systems

Sequential-Knowledge-Aware Next POI Recommendation: A Meta-Learning Approach

Yue Cui et al.

Summary: In this work, a meta-learned sequential-knowledge-aware recommender (Meta-SKR) is proposed to accurately recommend the next point of interest (POI) in a location-based social network. By utilizing sequential, spatio-temporal, and social knowledge, Meta-SKR achieves high recommendation accuracy even with sparse data.

ACM TRANSACTIONS ON INFORMATION SYSTEMS (2022)

Article Computer Science, Information Systems

Leveraging social influence based on users activity centers for point-of-interest recommendation

Kosar Seyedhoseinzadeh et al.

Summary: This paper proposes an improved recommender system by incorporating social, geographical, and temporal information into matrix factorization technique. The system achieves better performance on two real-world datasets by modeling social influence and considering users' friendships.

INFORMATION PROCESSING & MANAGEMENT (2022)

Article Computer Science, Artificial Intelligence

Robust cross-network node classification via constrained graph mutual information

Shuiqiao Yang et al.

Summary: In this paper, we propose a robust graph domain adaptive learning framework (RGDAL) for cross-network node classification. RGDAL utilizes an information-theoretic principle to filter noisy factors and learns noise-resistant and domain-invariant graph representations through graph convolutional networks and adversarial learning components. Experimental results demonstrate that RGDAL exhibits better robustness for cross-network node classification.

KNOWLEDGE-BASED SYSTEMS (2022)

Article Computer Science, Artificial Intelligence

A survey on deep learning based knowledge tracing

Xiangyu Song et al.

Summary: This paper focuses on deep learning-based knowledge tracing models. By systematically investigating, comparing, and discussing different aspects of these models, researchers can be better assisted in this field. The findings of this study contribute significantly to the progress of online education, particularly in the context of the current global pandemic. Future research directions in the field of deep learning-based knowledge tracing are also discussed.

KNOWLEDGE-BASED SYSTEMS (2022)

Article Engineering, Electrical & Electronic

RMGen: A Tri-Layer Vehicular Trajectory Data Generation Model Exploring Urban Region Division and Mobility Pattern

Xiangjie Kong et al.

Summary: This paper proposes a tri-layer framework to generate private car data sets for research on the Internet of Vehicles (IoV). The framework includes a novel region division scheme, a spatial-temporal interaction model, and an evaluation pipeline. The generated data provides strong support for IoV and mobility research tasks.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2022)

Article Computer Science, Artificial Intelligence

Next-point-of-interest recommendation based on joint mining of regularity and randomness

Xixi Li et al.

Summary: In this paper, a novel method for next-POI recommendation is proposed. It combines long-term and short-term modules to enhance the accuracy of recommendation by learning users' complex behavior. Experimental results demonstrate that the proposed method outperforms other methods in POI recommendation.

KNOWLEDGE-BASED SYSTEMS (2022)

Article Computer Science, Cybernetics

Urban Overtourism Detection Based on Graph Temporal Convolutional Networks

Xiangjie Kong et al.

Summary: In this article, a novel urban overtourism detection (UOD) framework based on graph temporal convolutional networks (TCNs) is proposed to tackle the challenges of ambiguity, sparsity, and complex spatiotemporal relations of urban overtourism. The framework includes a grid overtourism mode (GOM) to detect urban overtourism on a grid level, an overtourism detection mechanism to give a quantitative definition of overtourism and screen out candidate regions, and the use of graph TCNs to model the complex spatiotemporal relations and predict future urban overtourism. The evaluation results demonstrate the effectiveness of the proposed methods.

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS (2022)

Proceedings Paper Computer Science, Information Systems

GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendation

Song Yang et al.

Summary: The study aims to address the complexity of the next POI recommendation problem by predicting users' immediate future movements based on their current status and historical information. To achieve a more accurate prediction, a user-agnostic global trajectory flow map and a novel Graph Enhanced Transformer model (GETNext) are proposed, which successfully tackle the cold start problem.

PROCEEDINGS OF THE 45TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '22) (2022)

Article Computer Science, Artificial Intelligence

A graph embedding based model for fine-grained POI recommendation

Xiaojiao Hu et al.

Summary: This study proposes a novel POI-based item recommendation model via graph embedding, which effectively addresses data sparsity and cold start issues, as well as accurately capturing dynamic user preferences. Experimental results demonstrate that the model significantly outperforms existing baselines on three datasets.

NEUROCOMPUTING (2021)

Article Computer Science, Artificial Intelligence

Explainability in deep reinforcement learning

Alexandre Heuillet et al.

Summary: The study explores the development of Explainable Reinforcement Learning (XRL) and the application of XAI techniques in helping to understand the behavior and internal workings of models in reinforcement learning. The evaluation focuses on studies directly linking explainability to RL, categorizing the explanation generation into transparent algorithms and post-hoc explainability. Furthermore, it reviews prominent XAI works and their potential impact on the latest advances in RL, addressing present and future everyday problems.

KNOWLEDGE-BASED SYSTEMS (2021)

Article Computer Science, Artificial Intelligence

Multiview Concept Learning Via Deep Matrix Factorization

Wei Zhao et al.

Summary: Multiview representation learning leverages information from multiple views to summarize the consistency and complementarity in multiview data, but previous methods often neglect complex hierarchical information. The deep multiview concept learning (DMCL) method hierarchically factorizes multiview data to explicitly model consistent and complementary information, capturing semantic structures at the highest abstraction level.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)

Article Automation & Control Systems

Recommendation by Users' Multimodal Preferences for Smart City Applications

Cai Xu et al.

Summary: This article introduces a deep users' multimodal preferences-based recommendation (UMPR) method to capture the textual and visual matching of users and items. The method is applied to restaurant and product recommendation in smart city applications, showing superior performance compared to competitive baseline methods in experiments.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

Article Computer Science, Information Systems

Geographic Diversification of Recommended POIs in Frequently Visited Areas

Jungkyu Han et al.

ACM TRANSACTIONS ON INFORMATION SYSTEMS (2020)

Article Computer Science, Artificial Intelligence

Exploiting geographical-temporal awareness attention for next point-of-interest recommendation

Tongcun Liu et al.

NEUROCOMPUTING (2020)

Article Computer Science, Information Systems

Explainable Recommendation: A Survey and New Perspectives

Yongfeng Zhang et al.

FOUNDATIONS AND TRENDS IN INFORMATION RETRIEVAL (2020)

Proceedings Paper Computer Science, Theory & Methods

Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach

Dingqi Yang et al.

WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019) (2019)

Proceedings Paper Computer Science, Information Systems

Reinforcement Knowledge Graph Reasoning for Explainable Recommendation

Yikun Xian et al.

PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19) (2019)

Article Computer Science, Artificial Intelligence

A personalized point-of-interest recommendation model via fusion of geo-social information

Rong Gao et al.

NEUROCOMPUTING (2018)

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)

Proceedings Paper Computer Science, Theory & Methods

GeoSoCa: Exploiting Geographical, Social and Categorical Correlations for Point-of-Interest Recommendations

Jia-Dong Zhang et al.

SIGIR 2015: PROCEEDINGS OF THE 38TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (2015)