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Recommendation system in social networks with topical attention and probabilistic matrix factorization
Weiwei Zhang1, Fangai Liu1, Daomeng Xu1
1School of Information Science and Engineering, Shandong Normal University, Jinan, China.
Plos One
|November 1, 2019
Summary
This study introduces a new social network recommendation system (STAPMF) that enhances accuracy by considering trust intensity and user comments. It significantly improves upon existing methods for personalized recommendations.
Area of Science:
- Computer Science
- Artificial Intelligence
- Data Science
Background:
- Collaborative filtering (CF) recommendation systems suffer from data sparsity, limiting prediction accuracy.
- Existing social network-based methods often overlook nuanced trust levels and user comment data.
Purpose of the Study:
- To propose a novel recommendation system, Social Network with Topical Attention and Probabilistic Matrix Factorization (STAPMF).
- To enhance recommendation quality by integrating social trust and user review topical information.
Main Methods:
- Developed a hybrid algorithm combining probabilistic matrix factorization and attention-based recurrent neural networks.
- Extracted item, user personal, and user social hidden feature vectors from trust and review data.
Main Results:
- Demonstrated significant improvements in recommendation performance on real-world datasets.
- Outperformed prevailing state-of-the-art social network-based recommendation algorithms.
Conclusions:
- The proposed STAPMF model effectively addresses data sparsity in CF.
- Integrating nuanced social trust and review content boosts recommendation accuracy.
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