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SIIR: Symmetrical Information Interaction Modeling for News Recommendation
IEEE Transactions on Neural Networks and Learning Systems
|August 14, 2023
Summary
This study introduces symmetrical information interaction modeling (SIIR) for improved news recommendation. SIIR enhances user interest and candidate news matching, boosting click-through rate prediction accuracy.
Area of Science:
- Computer Science
- Artificial Intelligence
- Information Retrieval
Background:
- News recommendation systems rely on accurate matching between user interests and candidate news.
- Existing models often suffer from asymmetrical information density between user and candidate news representations, impacting prediction accuracy.
- This asymmetry leads to suboptimal click-through rate prediction in news recommendation.
Purpose of the Study:
- To propose a novel symmetrical information interaction modeling (SIIR) for news recommendation.
- To address the information asymmetry problem in user and candidate news representations.
- To improve the accuracy of click-through rate prediction in news recommendation systems.
Main Methods:
- Designed a light interactive attention network for user (LIAU) modeling to extract relevant user interests and reduce noise.
- Developed a heterogeneous graph neural network (HGNN) to enhance candidate news representation using inter-news relationships.
- Implemented SIIR to create a symmetrical information interaction framework for news recommendation.
Main Results:
- SIIR effectively extracts user interests related to candidate news while minimizing noise interference.
- HGNN enhances candidate news representation by leveraging underlying news relationships, mitigating the cold-start problem.
- Experiments on MIND and Adressa datasets show SIIR significantly outperforms state-of-the-art single-model methods.
Conclusions:
- SIIR provides a symmetrical approach to information interaction, resolving the asymmetry issue in news recommendation.
- The proposed LIAU and HGNN components effectively model user interests and candidate news representations, respectively.
- SIIR demonstrates superior performance in news recommendation tasks, offering a promising direction for future research.
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