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    CONNECTING MACHINE LEARNING METHODS THROUGH TRAINABLE TENSOR TRANSFORMERS
    3.
    发明申请
    CONNECTING MACHINE LEARNING METHODS THROUGH TRAINABLE TENSOR TRANSFORMERS 审中-公开

    公开(公告)号：US20200311613A1

    公开(公告)日：2020-10-01

    申请号：US16370156

    申请日：2019-03-29

    Applicant: Microsoft Technology Licensing, LLC

    Inventor： Yiming Ma ,  Jun Jia ,  Yi Wu ,  Xuhong Zhang ,  Leon Gao ,  Baolei Li ,  Bee-Chung Chen ,  Bo Long

    IPC: G06N20/20 ,  G06N5/04

    Abstract: Herein are techniques for configuring, integrating, and operating trainable tensor transformers that each encapsulate an ensemble of trainable machine learning (ML) models. In an embodiment, a computer-implemented trainable tensor transformer uses underlying ML models and additional mechanisms to assemble and convert data tensors as needed to generate output records based on input records and inferencing. The transformer processes each input record as follows. Input tensors of the input record are converted into converted tensors. Each converted tensor represents a respective feature of many features that are capable of being processed by the underlying trainable models. The trainable models are applied to respective subsets of converted tensors to generate an inference for the input record. The inference is converted into a prediction tensor. The prediction tensor and input tensors are stored as output tensors of a respective output record for the input record.

    TWO-TOWER NEURAL NETWORK FOR CONTENT-AUDIENCE RELATIONSHIP PREDICTION
    4.
    发明申请
    TWO-TOWER NEURAL NETWORK FOR CONTENT-AUDIENCE RELATIONSHIP PREDICTION 有权

    公开(公告)号：US20250156641A1

    公开(公告)日：2025-05-15

    申请号：US18388726

    申请日：2023-11-10

    Applicant: Microsoft Technology Licensing, LLC

    Inventor： Xueqian Tang ,  Lijun Peng ,  Jiarui Wang ,  Yi Zhang ,  Yi Wu ,  Arvind Subramaniam

    IPC: G06F40/30

    Abstract: In an example embodiment, a generator model such as a large language model (LLM) is leveraged to generate embeddings for both pieces of content and users. The embeddings map the pieces of content and the users into the same latent n-dimensional space. The embeddings are then fine-tuned using a two-tower deep neural network, with one of the towers representing users and the other tower representing content. The two-tower deep neural network is trained to optimize the embeddings over some shared goal, such as user engagement with content, and uses information such as user interactions with content in that process. A clustering technique, such as K-nearest neighbor (kNN) can then be used to identify a grouping of top user/content pairs based on similarity between users and content, as reflected in the embeddings. For a given piece of content, therefore, the top users from that cluster can then be recommended as an audience for the content.

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