Jove
Visualize
Contact Us

Related Concept Videos

Ogive Graph01:07

Ogive Graph

5.8K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
5.8K
The Representativeness Heuristic02:13

The Representativeness Heuristic

15.9K
The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
15.9K
Hierarchy of Motor Control01:18

Hierarchy of Motor Control

3.0K
The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.
3.0K
Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

6.4K
Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...
6.4K
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

5.5K
It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
5.5K
Time-Series Graph00:54

Time-Series Graph

4.5K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Detection and recognition of metal surface corrosion based on CBG-YOLOv5s.

PloS one·2024
Same author

A Dual Adaptive Interaction Click-Through Rate Prediction Based on Attention Logarithmic Interaction Network.

Entropy (Basel, Switzerland)·2022
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Aug 16, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

489

HDGFI: Hierarchical Dual-Level Graph Feature Interaction Model for Personalized Recommendation.

Xinxin Ma1, Zhendong Cui1

  • 1School of Computer and Control Engineering, Yantai University, Yantai 264005, China.

Entropy (Basel, Switzerland)
|December 23, 2022
PubMed
Summary

Recommendation systems face challenges with feature interactions generating noise and lacking interpretability. The Hierarchical Dual-level Graph Feature Interaction (HDGFI) model improves accuracy by filtering noise and enhancing feature representation.

Keywords:
dual-level graph feature interactionsfeature interactiongraph structurepersonalized recommendation

More Related Videos

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

7.4K
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.7K

Related Experiment Videos

Last Updated: Aug 16, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

489
Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

7.4K
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.7K

Area of Science:

  • Computer Science
  • Artificial Intelligence

Background:

  • Information overload necessitates effective recommendation systems.
  • Feature interaction is crucial but faces challenges with noise and interpretability.

Purpose of the Study:

  • To propose a novel model for feature interaction in recommendation systems.
  • To address noise generation and lack of interpretability in feature interactions.

Main Methods:

  • Introduced the Hierarchical Dual-level Graph Feature Interaction (HDGFI) model.
  • Utilized graph structures where features are nodes and interactions are edges.
  • Implemented a hierarchical edge selection module for beneficial interaction selection.
  • Modeled interaction importance from dual perspectives for finer granularity.

Main Results:

  • The HDGFI model effectively filters interaction noise.
  • The model enhances the interpretability of feature interactions.
  • Experimental results demonstrate higher accuracy compared to existing models.

Conclusions:

  • The HDGFI model offers a significant advancement in recommendation system feature interaction.
  • The proposed method improves accuracy and interpretability, addressing key limitations.