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Related Experiment Video

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Multi-view knowledge representation learning for personalized news recommendation.

Chao Chang1, Feiyi Tang2, Peng Yang1,3

  • 1School of Information Engineering, Guangzhou Panyu Polytechnic, Guangzhou, 511483, China.

Scientific Reports
|January 8, 2025
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Summary
This summary is machine-generated.

A new Multi-view Knowledge Representation Learning (MKRL) framework improves personalized news recommendations by integrating candidate news into user interest modeling, enhancing accuracy and relevance for dynamic user preferences.

Keywords:
Convolutional Neural NetworkMulti-head self-attentionMulti-view Representation LearningPersonalized News Recommendation

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Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Information Retrieval

Background:

  • Personalized news recommendation faces challenges due to diverse and dynamic user interests.
  • Existing models struggle to integrate candidate news into user interest modeling, limiting accuracy.

Purpose of the Study:

  • To propose the Multi-view Knowledge Representation Learning (MKRL) framework for enhanced personalized news recommendation.
  • To improve user interest modeling by jointly considering user history and candidate news characteristics.

Main Methods:

  • Developed MKRL framework with a multi-view news encoder and candidate-aware attention mechanisms.
  • Integrated convolutional neural networks and multi-head attention for contextual information capture.
  • Dynamically weighed user interactions and candidate news based on relevance.

Main Results:

  • MKRL framework demonstrated superior performance over state-of-the-art baselines.
  • Achieved enhanced recommendation accuracy and relevance in experiments.
  • Validated effectiveness on three real-world datasets.

Conclusions:

  • The MKRL framework effectively captures dynamic user interests for personalized news.
  • Incorporating candidate news directly into modeling significantly improves recommendation quality.
  • The multi-view approach enables richer and more personalized news recommendations.