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Related Experiment Video

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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A dynamic graph Hawkes process based on linear complexity self-attention for dynamic recommender systems.

Zhiwen Hou1, Xiaojun Lv2, Yuchen Zhou1

  • 1School of Information Network Security, People's Public Security University of China, Beijing, China.

Peerj. Computer Science
|June 22, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new dynamic recommender system, DGHP-LISA, to capture user interest evolution. It effectively models event influence for improved real-time recommendations in e-commerce and social media.

Keywords:
Dynamic graphHawkes processRecommender systemsSelf-attention

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Recommender Systems

Background:

  • Dynamic recommender systems require modeling evolving user interests and item popularity.
  • Existing methods struggle to capture the excitation effects between historical user-item interactions.
  • Real-time recommendations are crucial for platforms like e-commerce and social media.

Purpose of the Study:

  • To propose a novel framework, DGHP-LISA, for dynamic recommender systems.
  • To accurately model the dynamic relationships between users and items.
  • To capture the excitation effects of historical information on interaction evolution.

Main Methods:

  • Developed a Dynamic Graph Hawkes Process based on Linear complexity Self-Attention (DGHP-LISA).
  • Utilized a dynamic graph structure to represent user-item interactions.
  • Employed the Hawkes process to model event excitation effects.
  • Introduced a linear complexity self-attention mechanism for temporal and dynamic correlations.

Main Results:

  • DGHP-LISA demonstrated consistent improvements over state-of-the-art baseline models.
  • The model effectively captures the excitation effects in user-item interactions.
  • Accurate modeling of interaction evolution was achieved through the proposed self-attention mechanism.

Conclusions:

  • DGHP-LISA offers a robust framework for dynamic recommender systems.
  • The proposed method enhances the accuracy of real-time recommendations by modeling dynamic user interests.
  • This approach is particularly beneficial for applications with rapidly changing user preferences.