Results 91 to 100 of about 975 (190)

Context-Adaptive Graph Neural Networks for Next POI Recommendation

CoRR
12 pages, 6 ...
Yu Lei, Limin Shen, Zhu Sun 0001, Tiantian He 0001, Yew-Soon Ong   +4 more
openaire   +2 more sources

Hierarchical Graph Learning with Cross-Layer Information Propagation for Next Point of Interest Recommendation

Applied Sciences
With the vast quantity of GPS data that have been collected from location-based social networks, Point-of-Interest (POI) recommendation aims to predict users’ next locations by learning from their historical check-in trajectories.
Qiuhan Han, Atsushi Yoshikawa, Masayuki Yamamura   +2 more
doaj   +1 more source

RALLM-POI: Retrieval-Augmented LLM for Zero-Shot Next POI Recommendation with Geographical Reranking

Next point-of-interest (POI) recommendation predicts a user's next destination from historical movements. Traditional models require intensive training, while LLMs offer flexible and generalizable zero-shot solutions but often generate generic or geographically irrelevant results due to missing trajectory and spatial context.
Kunrong Li, Kwan Hui Lim 0001
openaire   +2 more sources

Geography-Aware Large Language Models for Next POI Recommendation

CoRR
The next Point-of-Interest (POI) recommendation task aims to predict users' next destinations based on their historical movement data and plays a key role in location-based services and personalized applications. Accurate next POI recommendation depends on effectively modeling geographic information and POI transition relations, which are crucial for ...
Zhao Liu, Wei Liu 0061, Huajie Zhu, Jianxing Yu, Jian Yin 0001, Wang-Chien Lee, Shun Wang   +6 more
openaire   +2 more sources

Efficient Model-Agnostic Continual Learning for Next POI Recommendation

CoRR
Next point-of-interest (POI) recommendation improves personalized location-based services by predicting users' next destinations based on their historical check-ins. However, most existing methods rely on static datasets and fixed models, limiting their ability to adapt to changes in user behavior over time.
Chenhao Wang 0007, Shanshan Feng 0001, Lisi Chen 0001, Fan Li 0015, Shuo Shang   +4 more
openaire   +2 more sources

MBSCL-Net: Multi-Branch Spectral Network and Contrastive Learning for Next-Point-of-Interest Recommendation. [PDF]

Sensors (Basel)
Wang S, Zhang J, Zeng T.
europepmc   +1 more source

Editorial: Machine learning and applied neuroscience, volume II. [PDF]

Front Neurorobot
Dos Santos WP, Conti V, Gambino O, Naik GR.   +3 more
europepmc   +1 more source

From genes to generations: genetic evaluation and counseling for infertility and pregnancy loss. [PDF]

Front Genet
Skrypnyk C, AlHafnawi HH, AlHarmi R, Amin E, Albuarki H.   +4 more
europepmc   +1 more source

Understanding the interplay between urban segregation and accessibility to services with network analysis. [PDF]

PLoS One
Lai M, Sapienza A, Vilella S, Canonico M, Cena F, Ruffo G.   +5 more
europepmc   +1 more source

Efficient and Site-Specific Incorporation of 3-Nitro-Tyrosine Into Recombinant Proteins in <i>Escherichia coli</i>. [PDF]

Bio Protoc
McGee SB, Stanisheuski S, Mehl RA, Cooley RB.   +3 more
europepmc   +1 more source