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Deep Neural Networks for Image-Based Dietary Assessment
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Deep Structured State Learning for Next-Period Recommendation.

Wen Wen, Fangyu Liang

    IEEE Transactions on Neural Networks and Learning Systems
    |May 31, 2022
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    This study introduces a deep structured state learning (DSSL) framework to capture users' dynamic preferences in time-sensitive recommendation systems. DSSL effectively models evolving user states and their temporal dependencies for improved accuracy.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Sequential models in recommender systems often overlook time-sensitive user behavior.
    • Understanding users' dynamic preferences is crucial for effective time-sensitive recommendations.

    Purpose of the Study:

    • To propose a novel framework for learning temporal user states and their complex dependencies.
    • To address the challenges of latent states, high dimensionality, and personalized temporal dynamics in user behavior.

    Main Methods:

    • Developed a deep structured state learning (DSSL) framework.
    • Designed to represent temporal states and model complex, personalized state dependencies.
    • Applied the framework to time-sensitive recommendation tasks.

    Main Results:

    • The DSSL framework achieved competitive performance across four real-world recommendation datasets.
    • Demonstrated the ability to learn representations of temporal states and their evolving patterns.
    • Identified key insights into designing effective state dependency networks.

    Conclusions:

    • The proposed DSSL framework offers a robust approach to time-sensitive recommendation by modeling user state dynamics.
    • This work provides a new perspective on incorporating temporal signals into sequential recommendation models.
    • Future research can leverage the identified rules for state dependency network design.