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A comprehensive survey on trustworthy recommender systems
As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …
people make appropriate decisions in an effective and efficient way, by providing …
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 …
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 …
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 …
User perception of recommendation explanation: Are your explanations what users need?
As recommender systems become increasingly important in daily human decision-making,
users are demanding convincing explanations to understand why they get the specific …
users are demanding convincing explanations to understand why they get the specific …
MCN4Rec: Multi-level collaborative neural network for next location recommendation
Next location recommendation plays an important role in various location-based services,
yielding great value for both users and service providers. Existing methods usually model …
yielding great value for both users and service providers. Existing methods usually model …
Context-and category-aware double self-attention model for next POI recommendation
Abstract Point-of-Interest (POI) recommender systems can effectively assist users to find their
preferred POIs. Recent studies mainly focus on extracting users' dynamic context from their …
preferred POIs. Recent studies mainly focus on extracting users' dynamic context from their …
KDRank: Knowledge-driven user-aware POI recommendation
Accurate user modeling is crucial for point-of-interest (POI) recommendation as it can
significantly improve user satisfaction with recommended POIs and enrich user experience …
significantly improve user satisfaction with recommended POIs and enrich user experience …
Revisiting long-and short-term preference learning for next POI recommendation with hierarchical LSTM
Point-of-interest (POI) recommendation has drawn much attention with the widespread
popularity of location-based social networks (LBSNs). Previous works define long-and short …
popularity of location-based social networks (LBSNs). Previous works define long-and short …
Pre-training across different cities for next poi recommendation
The Point-of-Interest (POI) transition behaviors could hold absolute sparsity and relative
sparsity very differently for different cities. Hence, it is intuitive to transfer knowledge across …
sparsity very differently for different cities. Hence, it is intuitive to transfer knowledge across …