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Multiagent-System-Based Attention Mechanism for Predicting Product Popularity: Handling Positive-Negative Diffusion

Mincan Li, Zidong Wang, Kenli Li

    IEEE Transactions on Neural Networks and Learning Systems
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    Summary
    This summary is machine-generated.

    This study introduces a new model for predicting product popularity on social networks (SN) using positive-negative diffusion (PND). The developed multi-agent system attention mechanism (MASAM) accurately captures user features for improved diffusion prediction.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Social Network Analysis

    Background:

    • Product popularity prediction is crucial for marketing and business strategy.
    • Social networks significantly influence product diffusion dynamics.
    • Existing models often struggle to capture complex user interactions and diffusion patterns.

    Purpose of the Study:

    • To propose a novel model for predicting product popularity on social networks considering positive-negative diffusion (PND).
    • To develop an efficient feature extraction method for user representation in diffusion prediction.
    • To establish a multi-agent system (MAS) model that simulates and predicts product diffusion.

    Main Methods:

    • A positive-negative diffusion (PND) model was developed to simulate product spread.
    • A multi-agent-system-based attention mechanism (MASAM) was devised for optimal user feature vector extraction.
    • A distributed learning algorithm was used to train the MASAM's shared weight matrix.
    • An MAS model for product diffusion was established using MASAM feature representations.
    • Agent interaction rules were suggested to accelerate simulation.

    Main Results:

    • The proposed PND model and MASAM effectively simulate product diffusion on social networks.
    • The MASAM significantly improves the precision of user feature extraction for prediction.
    • The MAS model demonstrates high effectiveness and efficiency in product popularity prediction.
    • Experimental results show superior performance compared to baseline methods.
    • A case study validates the algorithm's applicability and extendibility.

    Conclusions:

    • The developed PND and MAS models, powered by MASAM, offer a robust solution for product popularity prediction in social networks.
    • The approach provides accurate and efficient predictions by effectively modeling user behavior and diffusion dynamics.
    • The findings have practical implications for marketing strategies and understanding information spread.