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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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Related Experiment Videos

Motif and supernode-enhanced gated graph neural networks for session-based recommendation.

Ronghua Lin1, Chang Liu2, Hao Zhong1

  • 1School of Computer Science, South China Normal University, Guangzhou, 510631, China; Pazhou Lab, Guangzhou, 510330, China.

Neural Networks : the Official Journal of the International Neural Network Society
|March 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new recommender system that uses graph neural networks (GNNs) to better predict user behavior in anonymous sessions. The enhanced system improves recommendations by analyzing item relationships and user patterns.

Keywords:
Graph neural networkMotif-enhancedSession-based recommendationSupernode-enhanced

Related Experiment Videos

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Session-based recommendation systems predict user interactions in short, anonymous sessions.
  • Existing Graph Neural Network (GNN) methods overlook micro-structures and user behavior patterns in session graphs.
  • User behavior data is often sparse and dynamic, posing challenges for recommendation accuracy.

Purpose of the Study:

  • To propose a novel Motif and Supernode-Enhanced Session-based Recommender System (MSERS).
  • To address limitations in existing GNN-based methods by incorporating micro-structures.
  • To improve the representation of item dependencies and user behavior in session-based recommendations.

Main Methods:

  • Constructing a global session graph incorporating motifs as supernodes.
  • Identifying and encoding micro-structures (motifs) to enrich graph topology.
  • Employing supernode-enhanced Gated Graph Neural Networks (GGNN) for improved session representations.

Main Results:

  • MSERS effectively captures both long-term and latent item dependencies.
  • The proposed method significantly enhances session representations compared to baseline approaches.
  • Experiments on real-world datasets validate the superiority of MSERS.

Conclusions:

  • Incorporating micro-structures and motifs as supernodes enhances GNN-based session recommenders.
  • MSERS provides a robust framework for understanding and leveraging complex user behavior patterns.
  • The findings offer valuable insights into improving the accuracy and effectiveness of session-based recommendation systems.