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  3. Learning Deep Tree-based Retriever For Efficient Recommendation: Theory And Method.
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  3. Learning Deep Tree-based Retriever For Efficient Recommendation: Theory And Method.

Related Experiment Video

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

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Learning Deep Tree-Based Retriever for Efficient Recommendation: Theory and Method.

Ze Liu, Jin Zhang, Defu Lian

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |December 26, 2025

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    Deep Tree-based Retriever (DTR) improves recommendation efficiency by using multi-class classification and a novel loss function. This method enhances accuracy while addressing computational costs in deep learning recommendation systems.

    Related Experiment Videos

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
    12:18

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

    Published on: January 11, 2020

    7.9K

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Deep learning significantly enhances recommendation accuracy but faces efficiency challenges due to large item sets and high computation costs.
    • Existing tree-based deep recommendation models learn tree structures for efficiency but struggle to fully satisfy the max-heap assumption using one-versus-all classification.

    Purpose of the Study:

    • To propose a Deep Tree-based Retriever (DTR) for efficient and accurate recommendations.
    • To enhance the max-heap assumption adherence and recommendation performance in tree-based models.

    Main Methods:

    • DTR frames training as a softmax-based multi-class classification over tree nodes at the same level, promoting competition and mimicking beam search.
    • A rectification method is introduced for the loss function to better align with the max-heap assumption.
  • Sampled softmax and a tree-based sampling method are employed to improve efficiency and reduce bias.

    • Main Results:

    • DTR demonstrates improved recommendation efficiency and accuracy.
    • Theoretical analysis confirms DTR's generalization capability.
    • Experiments on four real-world datasets validate the effectiveness of DTR, its rectification method, and tree-based sampling.

    Conclusions:

    • DTR offers a more efficient and accurate approach to deep recommendation systems.
    • The proposed methods effectively address the limitations of previous tree-based models.
    • DTR shows strong generalization and practical applicability in real-world scenarios.