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Related Concept Videos

Diffusion01:12

Diffusion

Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
Diffusion01:21

Diffusion

Diffusion is a type of passive transport. In passive transport, a substance tends to move from an area of high concentration to an area of low concentration until the concentration is equal across the space. For example, take the diffusion of substances through the air. When someone opens a perfume bottle in a room filled with people, the perfume is at its highest concentration in the bottle and is at its lowest at the edges of the room. The perfume vapor will diffuse, or spread away, from the...
Assessment of Diffusion and Perfusion01:17

Assessment of Diffusion and Perfusion

Understanding and evaluating diffusion and perfusion is critical in assessing a patient's respiratory and circulatory health. These processes play key roles in maintaining the body's internal environment, ensuring that tissues receive adequate oxygen while waste products are efficiently removed.
The Role of Diffusion in Respiration
Diffusion is the process by which molecules move from an area of higher concentration to an area of lower concentration. In the respiratory system, this principle...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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Related Experiment Videos

Travel recommendation via diffusion-guided multiplex graph contrastive learning.

Chaoyue Yu1, Chaobo Zhang2, Long Tan3

  • 1The School of Hotel and Tourism Management, Shunde Polytechnic University, 528333, Guangdong, China. 10728@sdpt.edu.cn.

Scientific Reports
|May 16, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework for travel recommendations, improving accuracy by analyzing diverse user interactions. The diffusion-guided multiplex graph contrastive learning method addresses data sparsity and enhances smart tourism development.

Keywords:
Attribute-chainContrastive learningDiffusion modelsGraph representation learningSmart tourismTravel recommendations

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Data Science

Background:

  • Travel recommendation systems often overlook diverse user interaction attributes and suffer from data sparsity.
  • Existing methods fail to capture the complex dependencies between interaction attributes and final travel choices.

Purpose of the Study:

  • To propose a novel diffusion-guided multiplex graph contrastive learning (DMGCLTR) framework for travel recommendation.
  • To address data sparsity and the impact of various interaction attributes in travel data.
  • To enhance the accuracy and comprehensiveness of travel recommendation systems.

Main Methods:

  • Utilized an enhanced Diffusion Model (DM) to capture global interaction patterns across user-travel product attributes.
  • Developed an attribute-chain representation learner and perceptual encoder for granular analysis of attribute influence.
  • Employed contrastive learning (CL) for joint optimization and enhanced symmetry in the recommendation process.

Main Results:

  • DMGCLTR demonstrated superior performance compared to existing baselines across three real-world datasets.
  • Achieved an average improvement of 5.20% in HR@5 and 7.01% in MRR.
  • The framework effectively captures complex attribute dependencies and mitigates data sparsity.

Conclusions:

  • The proposed DMGCLTR framework offers a significant advancement in travel recommendation technology.
  • This approach provides a novel perspective for innovation and integrated development in the smart tourism industry.
  • Highlights the importance of considering multiplex interaction attributes for improved recommendation accuracy.