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POI recommendation for random groups based on cooperative graph neural networks
Abstract Group Point-of-Interests (POI) recommendation devotes to find the optimal POIs for
groups, which has extracted extensive attention. This work first brings forward a novel POI …
groups, which has extracted extensive attention. This work first brings forward a novel POI …
Keywords-enhanced contrastive learning model for travel recommendation
Travel recommendation aims to infer travel intentions of users by analyzing their historical
behaviors on Online Travel Agencies (OTAs). However, crucial keywords in clicked travel …
behaviors on Online Travel Agencies (OTAs). However, crucial keywords in clicked travel …
POI recommendation for occasional groups based on hybrid graph neural networks
Abstract Recently, POI (Point-of-interest) recommendation for groups has become a critical
challenge when hel** groups to discover potentially interesting new places, and some …
challenge when hel** groups to discover potentially interesting new places, and some …
Hybrid structural graph attention network for POI recommendation
In the era of big data, information overload poses a challenge, complicating user decision-
making. Recommender systems aim to assist in this process. In recent years, research on …
making. Recommender systems aim to assist in this process. In recent years, research on …
Safety: A spatial and feature mixed outlier detection method for big trajectory data
Trajectories, as sequential data records generated by continuously collecting sample points
from positioning sensors, have the capability to effectively depict the motion patterns of …
from positioning sensors, have the capability to effectively depict the motion patterns of …
SCFL: Spatio-temporal consistency federated learning for next POI recommendation
Existing personalized federated learning frameworks fail to significantly improve the
personalization of user preference learning in next Point-Of-Interest (POI) recommendations …
personalization of user preference learning in next Point-Of-Interest (POI) recommendations …
Global and local hypergraph learning method with semantic enhancement for POI recommendation
The deep semantic information mining extracts deep semantic features from textual data and
effectively utilizes the world knowledge embedded in these features, so it is widely …
effectively utilizes the world knowledge embedded in these features, so it is widely …
Siamese learning based on graph differential equation for Next-POI recommendation
Abstract Next Point-of-Interest (POI) recommendation is highly challenging in its ill-
posedness of data sparsity and elusive motives. Many models, including sequence-and …
posedness of data sparsity and elusive motives. Many models, including sequence-and …
Negative Sampling in Next-POI Recommendations: Observation, Approach, and Evaluation
To recommend the points of interest (POIs) that a user would check-in next, most deep-
learning (DL)-based existing studies have employed random negative (RN) sampling during …
learning (DL)-based existing studies have employed random negative (RN) sampling during …
Multivariate Hawkes Spatio-Temporal Point Process with attention for point of interest recommendation
Abstract Point-of-interest (POI) recommendation is one of the most essential services in
modern Location-Based Social Networks (LBSNs) to address the problem of information …
modern Location-Based Social Networks (LBSNs) to address the problem of information …