Search Results for author: Gao Cong

Found 34 papers, 11 papers with code

SAGDFN: A Scalable Adaptive Graph Diffusion Forecasting Network for Multivariate Time Series Forecasting

1 code implementation18 Jun 2024 Yue Jiang, Xiucheng Li, Yile Chen, Shuai Liu, Weilong Kong, Antonis F. Lentzakis, Gao Cong

Time series forecasting is essential for our daily activities and precise modeling of the complex correlations and shared patterns among multiple time series is essential for improving forecasting performance.

Multivariate Time Series Forecasting Time Series

Road Network Representation Learning with the Third Law of Geography

no code implementations6 Jun 2024 Haicang Zhou, Weiming Huang, Yile Chen, Tiantian He, Gao Cong, Yew-Soon Ong

In response, we propose to endow road network representation with the principles of the recent Third Law of Geography.

Contrastive Learning Representation Learning

LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency

1 code implementation19 Apr 2024 Zhaodonghui Li, Haitao Yuan, Huiming Wang, Gao Cong, Lidong Bing

In order to maintain equivalence between the rewritten query and the original one during rewriting, traditional query rewrite methods always rewrite the queries following certain rewrite rules.

Language Modelling Large Language Model

Semantic-Enhanced Representation Learning for Road Networks with Temporal Dynamics

no code implementations18 Mar 2024 Yile Chen, Xiucheng Li, Gao Cong, Zhifeng Bao, Cheng Long

In this study, we introduce a novel framework called Toast for learning general-purpose representations of road networks, along with its advanced counterpart DyToast, designed to enhance the integration of temporal dynamics to boost the performance of various time-sensitive downstream tasks.

Representation Learning

LAMP: A Language Model on the Map

no code implementations14 Mar 2024 Pasquale Balsebre, Weiming Huang, Gao Cong

Large Language Models (LLMs) are poised to play an increasingly important role in our lives, providing assistance across a wide array of tasks.

Language Modelling

LIST: Learning to Index Spatio-Textual Data for Embedding based Spatial Keyword Queries

no code implementations12 Mar 2024 Ziqi Yin, Shanshan Feng, Shang Liu, Gao Cong, Yew Soon Ong, Bin Cui

With the proliferation of spatio-textual data, Top-k KNN spatial keyword queries (TkQs), which return a list of objects based on a ranking function that evaluates both spatial and textual relevance, have found many real-life applications.

Pseudo Label

AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction

no code implementations6 Feb 2024 Kethmi Hirushini Hettige, Jiahao Ji, Shili Xiang, Cheng Long, Gao Cong, Jingyuan Wang

Air quality prediction and modelling plays a pivotal role in public health and environment management, for individuals and authorities to make informed decisions.

AdapTraj: A Multi-Source Domain Generalization Framework for Multi-Agent Trajectory Prediction

no code implementations22 Dec 2023 Tangwen Qian, Yile Chen, Gao Cong, Yongjun Xu, Fei Wang

However, the development of multi-source domain generalization in this task presents two notable issues: (1) negative transfer; (2) inadequate modeling for external factors.

Domain Generalization Trajectory Prediction

UnifiedSSR: A Unified Framework of Sequential Search and Recommendation

1 code implementation21 Oct 2023 Jiayi Xie, Shang Liu, Gao Cong, Zhenzhong Chen

In this work, we propose a Unified framework of Sequential Search and Recommendation (UnifiedSSR) for joint learning of user behavior history in both search and recommendation scenarios.

Self-Supervised Learning

Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis

3 code implementations9 Oct 2023 Zezhi Shao, Fei Wang, Yongjun Xu, Wei Wei, Chengqing Yu, Zhao Zhang, Di Yao, Guangyin Jin, Xin Cao, Gao Cong, Christian S. Jensen, Xueqi Cheng

Moreover, based on the proposed BasicTS and rich heterogeneous MTS datasets, we conduct an exhaustive and reproducible performance and efficiency comparison of popular models, providing insights for researchers in selecting and designing MTS forecasting models.

Benchmarking Multivariate Time Series Forecasting +1

City Foundation Models for Learning General Purpose Representations from OpenStreetMap

no code implementations1 Oct 2023 Pasquale Balsebre, Weiming Huang, Gao Cong, Yi Li

This can be attributed to the intrinsic heterogeneity of geospatial data, which encompasses different data types, including points, segments and regions, as well as multiple information modalities, such as a spatial position, visual characteristics and textual annotations.

WISK: A Workload-aware Learned Index for Spatial Keyword Queries

no code implementations28 Feb 2023 Yufan Sheng, Xin Cao, Yixiang Fang, Kaiqi Zhao, Jianzhong Qi, Gao Cong, Wenjie Zhang

In this paper, we propose WISK, a learned index for spatial keyword queries, which self-adapts for optimizing querying costs given a query workload.

Improving the Inference of Topic Models via Infinite Latent State Replications

no code implementations25 Jan 2023 Daniel Rugeles, Zhen Hai, Juan Felipe Carmona, Manoranjan Dash, Gao Cong

In text mining, topic models are a type of probabilistic generative models for inferring latent semantic topics from text corpus.

Topic Models

Region Embedding with Intra and Inter-View Contrastive Learning

1 code implementation15 Nov 2022 Liang Zhang, Cheng Long, Gao Cong

Motivated by the success of contrastive learning for representation learning, we propose to leverage it for multi-view region representation learning and design a model called ReMVC (Region Embedding with Multi-View Contrastive Learning) by following two guidelines: i) comparing a region with others within each view for effective representation extraction and ii) comparing a region with itself across different views for cross-view information sharing.

Clustering Contrastive Learning +1

Not All Neighbors Are Worth Attending to: Graph Selective Attention Networks for Semi-supervised Learning

no code implementations14 Oct 2022 Tiantian He, Haicang Zhou, Yew-Soon Ong, Gao Cong

We further propose Graph selective attention networks (SATs) to learn representations from the highly correlated node features identified and investigated by different SA mechanisms.

Graph Attention

Entity Resolution with Hierarchical Graph Attention Networks

1 code implementation SIGMOD/PODS 2022 Dezhong Yao, Yuhong Gu, Gao Cong, Hai Jin, Xinqiao Lv

However, there is often interdependence between different pairs of ER decisions, e. g., the entities from the same data source are usually semantically related to each other.

Attribute Entity Resolution +2

Points-of-Interest Relationship Inference with Spatial-enriched Graph Neural Networks

no code implementations28 Feb 2022 Yile Chen, Xiucheng Li, Gao Cong, Cheng Long, Zhifeng Bao, Shang Liu, Wanli Gu, Fuzheng Zhang

As a fundamental component in location-based services, inferring the relationship between points-of-interests (POIs) is very critical for service providers to offer good user experience to business owners and customers.

Graph Neural Network

Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation

1 code implementation13 Sep 2021 Yuxing Han, Ziniu Wu, Peizhi Wu, Rong Zhu, Jingyi Yang, Liang Wei Tan, Kai Zeng, Gao Cong, Yanzhao Qin, Andreas Pfadler, Zhengping Qian, Jingren Zhou, Jiangneng Li, Bin Cui

Therefore, we propose a new metric P-Error to evaluate the performance of CardEst methods, which overcomes the limitation of Q-Error and is able to reflect the overall end-to-end performance of CardEst methods.

Global Context Enhanced Graph Neural Networks for Session-based Recommendation

2 code implementations9 Jun 2021 Ziyang Wang, Wei Wei, Gao Cong, Xiao-Li Li, Xian-Ling Mao, Minghui Qiu

In GCE-GNN, we propose a novel global-level item representation learning layer, which employs a session-aware attention mechanism to recursively incorporate the neighbors' embeddings of each node on the global graph.

Representation Learning Session-Based Recommendations

Exploring Global Information for Session-based Recommendation

no code implementations20 Nov 2020 Ziyang Wang, Wei Wei, Gao Cong, Xiao-Li Li, Xian-Ling Mao, Minghui Qiu, Shanshan Feng

Based on BGNN, we propose a novel approach, called Session-based Recommendation with Global Information (SRGI), which infers the user preferences via fully exploring global item-transitions over all sessions from two different perspectives: (i) Fusion-based Model (SRGI-FM), which recursively incorporates the neighbor embeddings of each node on global graph into the learning process of session level item representation; and (ii) Constrained-based Model (SRGI-CM), which treats the global-level item-transition information as a constraint to ensure the learned item embeddings are consistent with the global item-transition.

Session-Based Recommendations

Interaction-aware Kalman Neural Networks for Trajectory Prediction

no code implementations28 Feb 2019 Ce Ju, Zheng Wang, Cheng Long, Xiao-Yu Zhang, Gao Cong, Dong Eui Chang

Forecasting the motion of surrounding obstacles (vehicles, bicycles, pedestrians and etc.)

Robotics I.2.9; I.2.0

Representation Learning for Spatial Graphs

no code implementations17 Dec 2018 Zheng Wang, Ce Ju, Gao Cong, Cheng Long

Recently, the topic of graph representation learning has received plenty of attention.

Clustering Denoising +1

Interact and Decide: Medley of Sub-Attention Networks for Effective Group Recommendation

no code implementations12 Apr 2018 Lucas Vinh Tran, Tuan-Anh Nguyen Pham, Yi Tay, Yiding Liu, Gao Cong, Xiao-Li Li

Our proposed approach hinges upon the key intuition that the decision making process (in groups) is generally dynamic, i. e., a user's decision is highly dependent on the other group members.

Decision Making

Heron Inference for Bayesian Graphical Models

1 code implementation19 Feb 2018 Daniel Rugeles, Zhen Hai, Gao Cong, Manoranjan Dash

Bayesian graphical models have been shown to be a powerful tool for discovering uncertainty and causal structure from real-world data in many application fields.

Computational Efficiency Variational Inference

Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation

1 code implementation1 Aug 2015 Xutao Li, Gao Cong, Xiaoli Li, Tuan Anh Nguyen Pham, Shonali Priyadarsini Krishnaswamy

In this paper, we propose a ranking based geographical factorization method, called Rank-GeoFM, for POI recommendation, which addresses the two challenges.

Recommendation Systems

Graph-based Point-of-interest Recommendation with Geographical and Temporal Influences

no code implementations1 Nov 2014 Quan Yuan, Gao Cong, Aixin Sun

In this paper, we focus on the problem of time-aware POI recommendation, which aims at recommending a list of POIs for a user to visit at a given time.

Recommendation Systems

Collective Spatial Keyword Querying

no code implementations 2011 2011 Xin Cao1, Gao Cong

We define the problem of retrieving a group of spatial web objects such that the group’s keywords cover the query’s keywords and such that objects are nearest to the query location and have the lowest inter-object distances.

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