ArticleBioengineering (Basel, Switzerland)2023
Point-of-Interest Preference Model Using an Attention Mechanism in a Convolutional Neural Network.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- The segmentation of nanoparticles with a novel approach of HRUScientific reports · 2025Article
- A novel approach for segmentation and quantitative analysis of breast calcification in mammograms.Frontiers in oncology · 2024Article
- Real-time temperature anomaly detection in vaccine refrigeration systems using deep learning on a resource-constrained microcontroller.Frontiers in artificial intelligence · 2024Article
- MCE: Medical Cognition Embedded in 3D MRI feature extraction for advancing glioma staging.PloS one · 2024Article
- Optimizing Clinical Diabetes Diagnosis through Generative Adversarial Networks: Evaluation and Validation.Diseases (Basel, Switzerland) · 2023Article
- A Novel Asynchronous Brain Signals-Based Driver-Vehicle Interface for Brain-Controlled Vehicles.Bioengineering (Basel, Switzerland) · 2023Article
- Radiomics-Based Prediction of TERT Promotor Mutations in Intracranial High-Grade Meningiomas.Cancers · 2023Article
- Continuous Motion Estimation of Knee Joint Based on a Parameter Self-Updating Mechanism Model.Bioengineering (Basel, Switzerland) · 2023Article
- Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review.Biology · 2023Review
- Predicting early gastric cancer risk using machine learning: A population-based retrospective study.Digital healthArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, there has been a growing interest in developing next point-of-interest (POI) recommendation systems in both industry and academia. However, current POI recommendation strategies suffer from the lack of sufficient mixing of details of the features related to individual users and their corresponding contexts. To overcome this issue, we propose a deep learning model based on an attention mechanism in this study. The suggested technique employs an attention mechanism that focuses on the pattern's friendship, which is responsible for concentrating on the relevant features related to individual users. To compute context-aware similarities among diverse users, our model employs six features of each user as inputs, including user ID, hour, month, day, minute, and second of visiting time, which explore the influences of both spatial and temporal features for the users. In addition, we incorporate geographical information into our attention mechanism by creating an eccentricity score. Specifically, we map the trajectory of each user to a shape, such as a circle, triangle, or rectangle, each of which has a different eccentricity value. This attention-based mechanism is evaluated on two widely used datasets, and experimental outcomes prove a noteworthy improvement of our model over the state-of-the-art strategies for POI recommendation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.