Contrastive graph learning long and short-term interests for POI recommendation
Modeling users' short-term dynamic and long-term static interests to enhance Point-of-
Interests (POI) recommendation performance has shown lots of advantages. Since users' …
Interests (POI) recommendation performance has shown lots of advantages. Since users' …
Disentangled contrastive hypergraph learning for next POI recommendation
Next point-of-interest (POI) recommendation has been a prominent and trending task to
provide next suitable POI suggestions for users. Most existing sequential-based and graph …
provide next suitable POI suggestions for users. Most existing sequential-based and graph …
Backtime: Backdoor attacks on multivariate time series forecasting
Abstract Multivariate Time Series (MTS) forecasting is a fundamental task with numerous
real-world applications, such as transportation, climate, and epidemiology. While a myriad of …
real-world applications, such as transportation, climate, and epidemiology. While a myriad of …
[HTML][HTML] NSEP: Early fake news detection via news semantic environment perception
X Fang, H Wu, J **g, Y Meng, B Yu, H Yu… - Information Processing & …, 2024 - Elsevier
The abundance of heavy data on social media enables users to share opinions freely,
leading to the rapid spread of misleading content. However, existing fake news detection …
leading to the rapid spread of misleading content. However, existing fake news detection …
A diffusion model for poi recommendation
Next Point-of-Interest (POI) recommendation is a critical task in location-based services that
aim to provide personalized suggestions for the user's next destination. Previous works on …
aim to provide personalized suggestions for the user's next destination. Previous works on …
[HTML][HTML] How can we create a recommender system for tourism? A location centric spatial binning-based methodology using social networks
Point of Interest (POI) recommendation is an important Location-Based Social Network
(LBSN) task that has become a hotspot in the past decade. It aims to exploit the user's …
(LBSN) task that has become a hotspot in the past decade. It aims to exploit the user's …
[HTML][HTML] Improving the spatial–temporal aware attention network with dynamic trajectory graph learning for next Point-Of-Interest recommendation
G Cao, S Cui, I Joe - Information Processing & Management, 2023 - Elsevier
Abstract Next Point-Of-Interest (POI) recommendation aim to predict users' next visits by
mining their movement patterns. Existing works attempt to extract spatial–temporal …
mining their movement patterns. Existing works attempt to extract spatial–temporal …
Kernel-based substructure exploration for next POI recommendation
Point-of-Interest (POI) recommendation, which benefits from the proliferation of GPS-
enabled devices and location-based social networks (LBSNs), plays an increasingly …
enabled devices and location-based social networks (LBSNs), plays an increasingly …
Heterogeneous spatio-temporal graph contrastive learning for point-of-interest recommendation
As one of the most crucial topics in the recommendation system field, point-of-interest (POI)
recommendation aims to recommending potential interesting POIs to users. Recently, graph …
recommendation aims to recommending potential interesting POIs to users. Recently, graph …
Smart IoE-integrated traffic control: Dynamic multi-semantic graph attention and reinforcement learning for optimizing urban mobility
KK Tseng, Z Yang, H Tang, CM Chen… - IEEE Internet of …, 2024 - ieeexplore.ieee.org
The rapid advancement of Internet of Everything (IoE) technologies has transformed the
landscape of urban mobility management, necessitating innovative approaches to optimize …
landscape of urban mobility management, necessitating innovative approaches to optimize …