Results 101 to 110 of about 2,209 (219)

Multimodal Temporal Fusion for Next POI Recommendation

Algorithms
The objective of the next POI recommendation is using the historical check-in sequences of users to learn the preferences and habits of users, providing a list of POIs that users will be inclined to visit next. Then, there are some limitations in existing POI recommendation algorithms.
Fang Liu 0004, Jiangtao Li, Tianrui Li 0010   +2 more
openaire   +1 more source

Islet Autoantibodies in Adult With Pituitary and Ovarian Autoimmunity: Implications for Type 1 Diabetes Screening

Diabetes, Obesity and Metabolism, Volume 28, Issue 7, Page 5827-5833, July 2026.
ABSTRACT Purpose Type 1 diabetes is a chronic autoimmune disease characterised by progressive pancreatic β‐cell destruction and the early appearance of islet autoantibodies (islet‐AAb) during the pre‐symptomatic phases. Given the growing interest in screening and prevention of type 1 diabetes, this study aimed to assess the prevalence of pancreatic ...
Giuseppe Bellastella, Michela di Nuzzo, Miriam Longo, Paolo Cirillo, Antonietta Maio, Nicole Di Martino, Silvia Angelino, Paola Caruso, Lorenzo Scappaticcio, Maria Teresa Vietri, Maria Ida Maiorino, Katherine Esposito   +11 more
wiley   +1 more source

LGLMF: Local Geographical Based Logistic Matrix Factorization Model for POI Recommendation

, 2020
With the rapid growth of Location-Based Social Networks, personalized Points of Interest (POIs) recommendation has become a critical task to help users explore their surroundings.
Afsharchi, M., Crestani, F., Rahmani, H.A., Aliannejadi, M.; id_orcid, Ahmadian, S., Baratchi, M.   +5 more
core   +1 more source

First Generation Proteolysis Targeting Chimeras (PROTACs) for the Treatment of Progeria

Advanced Science, Volume 13, Issue 32, 9 June 2026.
We report the first PROTACs designed to degrade progerin, introducing a novel therapeutic approach for progeria. The best compound, UCM‐18142, significantly reduces progerin levels and improves key disease phenotypes in patient‐derived cells and in the LmnaG609G/G609G mouse model, paving the way for new treatment strategies targeting the root cause of ...
Jon Macicior‐Michelena, Marcelino Telechea, Daniel Fernández, Alba García‐Martín, Ángeles Canales, Silvia Ortega‐Gutiérrez   +5 more
wiley   +1 more source

PERS: A Personalized and Explainable POI Recommender System

CoRR, 2017
The Location-Based Social Networks (LBSN) (e.g., Facebook) have many factors (for instance, ratings, check-in time, etc.) that play a crucial role for the Point-of-Interest (POI) recommendations. Unlike ratings, the reviews can help users to elaborate their opinion and share the extent of consumption experience in terms of the relevant factors of ...
Ramesh Baral, Tao Li 0001
openaire   +2 more sources

Dietary and biomarker‐guided strategies as supportive measures in the fragile X syndrome

Food Biomacromolecules, Volume 3, Issue 2, Page 237-251, June 2026.
Abstract The fragile X syndrome (FXS) is an inherited neurodevelopmental disorder that primarily affects males, often resulting in an IQ below 55, while about two‐thirds of females also experience intellectual disability. Physical features may include an elongated face, prominent ears, finger joint laxity, and enlarged testes in males.
Jailan E. El Halawani, Reem R. AlOlaby
wiley   +1 more source

Recommending POIs Based on the User’s Context and Intentions [PDF]

, 2013
This paper describes a Recommender System that implements a Multiagent System for making personalised context and intention-aware recommendations of Points of Interest (POIs). A two-parted agent architecture was used, with an agent responsible for gathering POIs from a location-based service, and a set of Personal Assistant Agents (PAAs) collecting ...
Hernani Pereira Costa, Bárbara Furtado, Durval Pires, Luís Macedo, Amílcar Cardoso   +4 more
openaire   +1 more source

Exploiting Spatial and Temporal for Point of Interest Recommendation

Complexity, 2018
An increasing number of users have been attracted by location-based social networks (LBSNs) in recent years. Meanwhile, user-generated content in online LBSNs like spatial, temporal, and social information provides an ever-increasing chance to study the ...
Jinpeng Chen, Wen Zhang, Pei Zhang, Pinguang Ying, Kun Niu, Ming Zou   +5 more
doaj   +1 more source

A Paradigm of Temporal‐Weather‐Aware Transition Pattern for POI Recommendation

CAAI Transactions on Intelligence Technology
Point of interest (POI) recommendation analyses user preferences through historical check‐in data. However, existing POI recommendation methods often overlook the influence of weather information and face the challenge of sparse historical data for ...
Junyang Chen, Jingcai Guo, Huan Wang, Zhihui Lai, Qin Zhang, Kaishun Wu, Liang‐Jie Zhang   +6 more
doaj   +1 more source

A HieArarchical LSTM Framework for Capturing Long- and Short-Term Preferences in POI Recommendation

ITEGAM-JETIA
Point-of-Interest (POI) recommendation is crucial for improving user experience in location-based social networks (LBSNs). With the growing number of users checking in at various places personalized recommendations are necessary to provide relevant ...
Sarala Patchala, Vijay Babu Burra, Vullam Naga Gopi Raju, Banda SNV Ramana Murthy, Desamala Prabhakara Rao   +4 more
doaj   +1 more source