DisenPOI: Disentangling sequential and geographical influence for point-of-interest recommendation
Point-of-Interest (POI) recommendation plays a vital role in various location-aware services.
It has been observed that POI recommendation is driven by both sequential and …
It has been observed that POI recommendation is driven by both sequential and …
Towards automated urban planning: When generative and chatgpt-like ai meets urban planning
The two fields of urban planning and artificial intelligence (AI) arose and developed
separately. However, there is now cross-pollination and increasing interest in both fields to …
separately. However, there is now cross-pollination and increasing interest in both fields to …
Traceable group-wise self-optimizing feature transformation learning: A dual optimization perspective
Feature transformation aims to reconstruct an effective representation space by
mathematically refining the existing features. It serves as a pivotal approach to combat the …
mathematically refining the existing features. It serves as a pivotal approach to combat the …
How do you go where? improving next location prediction by learning travel mode information using transformers
Predicting the next visited location of an individual is a key problem in human mobility
analysis, as it is required for the personalization and optimization of sustainable transport …
analysis, as it is required for the personalization and optimization of sustainable transport …
Group-wise reinforcement feature generation for optimal and explainable representation space reconstruction
Representation (feature) space is an environment where data points are vectorized,
distances are computed, patterns are characterized, and geometric structures are …
distances are computed, patterns are characterized, and geometric structures are …
Reinforced explainable knowledge concept recommendation in MOOCs
In this article, we study knowledge concept recommendation in Massive Open Online
Courses (MOOCs) in an explainable manner. Knowledge concepts, composing course units …
Courses (MOOCs) in an explainable manner. Knowledge concepts, composing course units …
Interactive reinforced feature selection with traverse strategy
In this paper, we propose a single-agent Monte Carlo-based reinforced feature selection
method, as well as two efficiency improvement strategies, ie, early stopping strategy and …
method, as well as two efficiency improvement strategies, ie, early stopping strategy and …
Road planning for slums via deep reinforcement learning
Y Zheng, H Su, J Ding, D Jin, Y Li - … of the 29th ACM SIGKDD Conference …, 2023 - dl.acm.org
Millions of slum dwellers suffer from poor accessibility to urban services due to inadequate
road infrastructure within slums, and road planning for slums is critical to the sustainable …
road infrastructure within slums, and road planning for slums is critical to the sustainable …
A multi-view confidence-calibrated framework for fair and stable graph representation learning
Graph Neural Networks (GNNs) are prone to adversarial attacks and discriminatory biases.
The cutting-edge studies usually adopt a perturbation-invariant consistency regularization …
The cutting-edge studies usually adopt a perturbation-invariant consistency regularization …
Efficient reinforced feature selection via early stopping traverse strategy
In this paper, we propose a single-agent Monte Carlo based reinforced feature selection
(MCRFS) method, as well as two efficiency improvement strategies, ie, early stopping (ES) …
(MCRFS) method, as well as two efficiency improvement strategies, ie, early stopping (ES) …