Search Results for author:
Found 48 papers, 25 papers with code
Large Language Model for Participatory Urban Planning
To deal with the different facilities needs of residents, we initiate a discussion among the residents in each community about the plan, where residents provide feedback based on their profiles.
Spatio-Temporal Few-Shot Learning via Diffusive Neural Network Generation
Spatio-temporal modeling is foundational for smart city applications, yet it is often hindered by data scarcity in many cities and regions.
UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction
Urban spatio-temporal prediction is crucial for informed decision-making, such as transportation management, resource optimization, and urban planning.
Mixed Attention Network for Cross-domain Sequential Recommendation
Recent proposed cross-domain sequential recommendation models such as PiNet and DASL have a common drawback relying heavily on overlapped users in different domains, which limits their usage in practical recommender systems.
Inverse Learning with Extremely Sparse Feedback for Recommendation
In this paper, we propose a meta-learning method to annotate the unlabeled data from loss and gradient perspectives, which considers the noises in both positive and negative instances.
Stance Detection with Collaborative Role-Infused LLM-Based Agents
Next, in the reasoning-enhanced debating stage, for each potential stance, we designate a specific LLM-based agent to advocate for it, guiding the LLM to detect logical connections between text features and stance, tackling the second challenge.
How enlightened self-interest guided global vaccine sharing benefits all: a modelling study
We explore the possibility of the enlightened self-interest incentive mechanism, i. e., improving one's own epidemic outcomes by sharing vaccines with other countries, by evaluating the number of infections and deaths under various vaccine sharing strategies using the proposed model.
Towards Generative Modeling of Urban Flow through Knowledge-enhanced Denoising Diffusion
To capture the effect of multiple factors on urban flow, such as region features and urban environment, we employ diffusion model to generate urban flow for regions under different conditions.
Alleviating Video-Length Effect for Micro-video Recommendation
Micro-videos platforms such as TikTok are extremely popular nowadays.
Learning and Optimization of Implicit Negative Feedback for Industrial Short-video Recommender System
Specifically, in short-video recommendation, the easiest-to-collect user feedback is the skipping behavior, which leads to two critical challenges for the recommendation model.
Understanding and Modeling Passive-Negative Feedback for Short-video Sequential Recommendation
To enhance the robustness of our model, we then introduce a multi-task learning module to simultaneously optimize two kinds of feedback -- passive-negative feedback and traditional randomly-sampled negative feedback.
Uncertainty-aware Consistency Learning for Cold-Start Item Recommendation
Graph Neural Network (GNN)-based models have become the mainstream approach for recommender systems.
Detecting Vulnerable Nodes in Urban Infrastructure Interdependent Network
Understanding and characterizing the vulnerability of urban infrastructures, which refers to the engineering facilities essential for the regular running of cities and that exist naturally in the form of networks, is of great value to us.
Multi-Scale Simulation of Complex Systems: A Perspective of Integrating Knowledge and Data
In this survey, we will systematically review the literature on multi-scale simulation of complex systems from the perspective of knowledge and data.
Carbon emissions and sustainability of launching 5G mobile networks in China
We show that the decline in carbon efficiency leads to a carbon efficiency trap, estimated to cause additional carbon emissions of 23. 82 +- 1. 07 megatons in China.
Road Planning for Slums via Deep Reinforcement Learning
Existing re-blocking or heuristic methods are either time-consuming which cannot generalize to different slums, or yield sub-optimal road plans in terms of accessibility and construction costs.
Spatio-temporal Diffusion Point Processes
To enhance the learning of each step, an elaborated spatio-temporal co-attention module is proposed to capture the interdependence between the event time and space adaptively.
Robust Preference-Guided Denoising for Graph based Social Recommendation
Graph Neural Network(GNN) based social recommendation models improve the prediction accuracy of user preference by leveraging GNN in exploiting preference similarity contained in social relations.
Advancements in Federated Learning: Models, Methods, and Privacy
Based on these frameworks, we have instantiated FedOpt algorithms.
Learning to Simulate Daily Activities via Modeling Dynamic Human Needs
To enhance the fidelity and utility of the generated activity data, our core idea is to model the evolution of human needs as the underlying mechanism that drives activity generation in the simulation model.
Dual-interest Factorization-heads Attention for Sequential Recommendation
In this paper, we propose Dual-interest Factorization-heads Attention for Sequential Recommendation (short for DFAR) consisting of feedback-aware encoding layer, dual-interest disentangling layer and prediction layer.
MAPDP: Cooperative Multi-Agent Reinforcement Learning to Solve Pickup and Delivery Problems
It is of great importance to efficiently provide high-quality solutions of cooperative PDP.
Mutual Harmony: Sequential Recommendation with Dual Contrastive Network
Such user-centric recommendation will make it impossible for the provider to expose their new items, failing to consider the accordant interactions between user and item dimensions.
DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction
In particular, we first unify the fine-grained user similarity and the complex matching between user preferences and spatiotemporal activity into a heterogeneous hypergraph.
DVR: Micro-Video Recommendation Optimizing Watch-Time-Gain under Duration Bias
Recommender systems are prone to be misled by biases in the data.
Disentangling Long and Short-Term Interests for Recommendation
Modeling user's long-term and short-term interests is crucial for accurate recommendation.
LoSAC: An Efficient Local Stochastic Average Control Method for Federated Optimization
Federated optimization (FedOpt), which targets at collaboratively training a learning model across a large number of distributed clients, is vital for federated learning.
Progressive Feature Interaction Search for Deep Sparse Network
Deep sparse networks (DSNs), of which the crux is exploring the high-order feature interactions, have become the state-of-the-art on the prediction task with high-sparsity features.
Inhomogeneous Social Recommendation with Hypergraph Convolutional Networks
With the hypergraph convolutional networks, the social relations can be modeled in a more fine-grained manner, which more accurately depicts real users' preferences, and benefits the recommendation performance.
Spatio-Temporal Urban Knowledge Graph Enabled Mobility Prediction
Further, a complex embedding model with elaborately designed scoring functions is proposed to measure the plausibility of facts in STKG to solve the knowledge graph completion problem, which considers temporal dynamics of the mobility patterns and utilizes PoI categories as the auxiliary information and background knowledge.
Improving Location Recommendation with Urban Knowledge Graph
We first conduct information propagation on two sub-graphs to learn the representations of POIs and users.
A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions
In this survey, we conduct a comprehensive review of the literature on graph neural network-based recommender systems.
DGCN: Diversified Recommendation with Graph Convolutional Networks
These years much effort has been devoted to improving the accuracy or relevance of the recommendation system.
Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey
For each problem, we comprehensively introduce the existing DRL solutions.
Sequential Recommendation with Graph Neural Networks
This helps explicitly distinguish users' core interests, by forming dense clusters in the interest graph.
Efficient Data-specific Model Search for Collaborative Filtering
In this way, we can combinatorially generalize data-specific CF models, which have not been visited in the literature, from SOTA ones.
Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution
Additionally, there is a severe lack of fair comparison among different methods on the same datasets.
Ranked #2 on
Traffic Prediction
on NE-BJ
Policy-Aware Mobility Model Explains the Growth of COVID-19 in Cities
With the continued spread of coronavirus, the task of forecasting distinctive COVID-19 growth curves in different cities, which remain inadequately explained by standard epidemiological models, is critical for medical supply and treatment.
Learnable Embedding Sizes for Recommender Systems
Existing works that try to address the problem always cause a significant drop in recommendation performance or suffers from the limitation of unaffordable training time cost.
Rewriting by Generating: Learn Heuristics for Large-scale Vehicle Routing Problems
The large-scale vehicle routing problem is defined based on the classical VRP with usually more than one thousand customers.
Group-Buying Recommendation for Social E-Commerce
Group-buying recommendation for social e-commerce, which recommends an item list when users want to launch a group, plays an important role in the group success ratio and sales.
Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering
Negative sampling approaches are prevalent in implicit collaborative filtering for obtaining negative labels from massive unlabeled data.
Disentangling User Interest and Conformity for Recommendation with Causal Embedding
We further demonstrate that the learned embeddings successfully capture the desired causes, and show that DICE guarantees the robustness and interpretability of recommendation.
Bundle Recommendation with Graph Convolutional Networks
Existing solutions integrate user-item interaction modeling into bundle recommendation by sharing model parameters or learning in a multi-task manner, which cannot explicitly model the affiliation between items and bundles, and fail to explore the decision-making when a user chooses bundles.
Price-aware Recommendation with Graph Convolutional Networks
Price, an important factor in marketing --- which determines whether a user will make the final purchase decision on an item --- surprisingly, has received relatively little scrutiny.
UrbanRhythm: Revealing Urban Dynamics Hidden in Mobility Data
In this paper, to reveal urban dynamics, we propose a novel system UrbanRhythm to reveal the urban dynamics hidden in human mobility data.
Learning to Recommend with Multiple Cascading Behaviors
To fully exploit the signal in the data of multiple types of behaviors, we perform a joint optimization based on the multi-task learning framework, where the optimization on a behavior is treated as a task.
Trajectory Recovery From Ash: User Privacy Is NOT Preserved in Aggregated Mobility Data
By conducting experiments on two real-world datasets collected from both mobile application and cellular network, we reveal that the attack system is able to recover users' trajectories with accuracy about 73%~91% at the scale of tens of thousands to hundreds of thousands users, which indicates severe privacy leakage in such datasets.
Computers and Society Cryptography and Security
