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Found 44 papers, 16 papers with code
Mixture-of-Experts for Personalized and Semantic-Aware Next Location Prediction
Next location prediction plays a critical role in understanding human mobility patterns.
TransferTraj: A Vehicle Trajectory Learning Model for Region and Task Transferability
To address these challenges, we propose TransferTraj, a vehicle GPS trajectory learning model that excels in both region and task transferability.
Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey
Spatio-Temporal (ST) data science, which includes sensing, managing, and mining large-scale data across space and time, is fundamental to understanding complex systems in domains such as urban computing, climate science, and intelligent transportation.
CORAG: A Cost-Constrained Retrieval Optimization System for Retrieval-Augmented Generation
We employ a Monte Carlo Tree Search (MCTS) based policy framework to find optimal chunk combinations sequentially, allowing for a comprehensive consideration of correlations among chunks.
FlexTSF: A Universal Forecasting Model for Time Series with Variable Regularities
Developing a foundation model for time series forecasting across diverse domains has attracted significant attention in recent years.
PTR: A Pre-trained Language Model for Trajectory Recovery
PTR incorporates an explicit trajectory prompt and is trained on datasets with multiple sampling intervals, enabling it to generalize effectively across different intervals in sparse trajectories.
Context-Enhanced Multi-View Trajectory Representation Learning: Bridging the Gap through Self-Supervised Models
To align the learning process across multiple views, we utilize GPS trajectories as a bridge and employ self-supervised pretext tasks to capture and distinguish movement patterns across different spatial views.
nextlocllm: next location prediction using LLMs
Experiments show that NextLocLLM outperforms existing models in next location prediction, excelling in both supervised and zero-shot settings.
CAMAL: Optimizing LSM-trees via Active Learning
We use machine learning to optimize LSM-tree structure, aiming to reduce the cost of processing various read/write operations.
Self-supervised Learning for Geospatial AI: A Survey
The proliferation of geospatial data in urban and territorial environments has significantly facilitated the development of geospatial artificial intelligence (GeoAI) across various urban applications.
SAGDFN: A Scalable Adaptive Graph Diffusion Forecasting Network for Multivariate Time Series Forecasting
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.
UrbanLLM: Autonomous Urban Activity Planning and Management with Large Language Models
Location-based services play an critical role in improving the quality of our daily lives.
Road Network Representation Learning with the Third Law of Geography
In response, we propose to endow road network representation with the principles of the recent Third Law of Geography.
LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency
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.
Semantic-Enhanced Representation Learning for Road Networks with Temporal Dynamics
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.
LAMP: A Language Model on the Map
Large Language Models (LLMs) are poised to play an increasingly important role in our lives, providing assistance across a wide array of tasks.
LIST: Learning to Index Spatio-Textual Data for Embedding based Spatial Keyword Queries
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 considers both spatial and textual relevance, have found many real-life applications.
AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction
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
However, the development of multi-source domain generalization in this task presents two notable issues: (1) negative transfer; (2) inadequate modeling for external factors.
UnifiedSSR: A Unified Framework of Sequential Search and Recommendation
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.
Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis
Multivariate Time Series (MTS) analysis is crucial to understanding and managing complex systems, such as traffic and energy systems, and a variety of approaches to MTS forecasting have been proposed recently.
City Foundation Models for Learning General Purpose Representations from OpenStreetMap
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.
On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence
In this work, we explore the promises and challenges of developing multimodal foundation models for GeoAI.
WISK: A Workload-aware Learned Index for Spatial Keyword Queries
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
In text mining, topic models are a type of probabilistic generative models for inferring latent semantic topics from text corpus.
Region Embedding with Intra and Inter-View Contrastive Learning
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.
Online Anomalous Subtrajectory Detection on Road Networks with Deep Reinforcement Learning
Detecting anomalous trajectories has become an important task in many location-based applications.
Not All Neighbors Are Worth Attending to: Graph Selective Attention Networks for Semi-supervised Learning
We further propose Graph selective attention networks (SATs) to learn representations from the highly correlated node features identified and investigated by different SA mechanisms.
Entity Resolution with Hierarchical Graph Attention Networks
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.
Ranked #1 on
Entity Resolution
on WDC Watches-small
Points-of-Interest Relationship Inference with Spatial-enriched Graph Neural Networks
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.
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation
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
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.
A Reinforcement Learning Based R-Tree for Spatial Data Indexing in Dynamic Environments
Learned indices have been proposed to replace classic index structures like B-Tree with machine learning (ML) models.
Exploring Global Information for Session-based Recommendation
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.
Efficient and Effective Similar Subtrajectory Search with Deep Reinforcement Learning
Similar trajectory search is a fundamental problem and has been well studied over the past two decades.
Interaction-aware Kalman Neural Networks for Trajectory Prediction
Forecasting the motion of surrounding obstacles (vehicles, bicycles, pedestrians and etc.)
Robotics I.2.9; I.2.0
Representation Learning for Spatial Graphs
Recently, the topic of graph representation learning has received plenty of attention.
HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems
This paper investigates the notion of learning user and item representations in non-Euclidean space.
Ranked #1 on
Recommendation Systems
on MovieLens 20M
Interact and Decide: Medley of Sub-Attention Networks for Effective Group Recommendation
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.
Heron Inference for Bayesian Graphical Models
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.
A Novel Cascade Model for Learning Latent Similarity from Heterogeneous Sequential Data of MOOC
Recent years have witnessed the proliferation of Massive Open Online Courses (MOOCs).
Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation
In this paper, we propose a ranking based geographical factorization method, called Rank-GeoFM, for POI recommendation, which addresses the two challenges.
Graph-based Point-of-interest Recommendation with Geographical and Temporal Influences
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.
Collective Spatial Keyword Querying
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.
