公开(公告)号：US20180060728A1

公开(公告)日：2018-03-01

申请号：US15253669

申请日：2016-08-31

Applicant: Microsoft Technology Licensing, LLC

Inventor： Ying Shan ,  Jianchang Mao ,  Dong Yu ,  Holakou Rahmanian ,  Yi Zhang

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/084 ,  G06N5/003 ,  G06N20/00

Abstract: A deep embedding forest-based (DEF) model for improving on-line serving time for classification learning methods and other tasks such as, for example, predicting user selection of search results provided in response to a query or for image, speech or text recognition. Initially, a deep neural network (DNN) model is trained to determine parameters of an embedding layer, a stacking layer, deep layers and a scoring layer thereby reducing high dimensional features. After training the DNN model, the parameters of the deep layers and the scoring layer of the DNN model and discarded and the parameters of the embedding layer and the stacking layer are extracted. The extracted parameters from the DNN model then initialize parameters of an embedding layer and a stacking layer of the DEF model such that only a forest layer of the DEF model is then required to be trained. Output from the DEF model is stored in computer memory.

公开(公告)号：US09589277B2

公开(公告)日：2017-03-07

申请号：US14145422

申请日：2013-12-31

Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC

Inventor： Bruce Zhang ,  Jianchang Mao ,  Yuan Shen

IPC: G06Q30/02 ,  G06N99/00

CPC classification number: G06Q30/0256 ,  G06N99/005

Abstract: Methods, computer systems, and computer storage media are provided for evaluating information retrieval (IR) such as search query results (including advertisements) by a machine learning scorer. In an embodiment, a set of features is derived from a query and a machine learning algorithm is applied to construct a linear model of (query, ads) for scoring by maximizing a relevance metric. In an embodiment, the machine learned scorer is adapted for use with WAND algorithm based ad selection.

Abstract translation: 提供方法，计算机系统和计算机存储介质，用于通过机器学习记分器评估诸如搜索查询结果（包括广告）的信息检索（IR）。 在一个实施例中，从查询导出一组特征，并且应用机器学习算法来通过最大化相关性度量来构建用于评分的（查询，广告）的线性模型。 在一个实施例中，机器学习得分器适用于基于WAND算法的广告选择。

公开(公告)号：US11023473B2

公开(公告)日：2021-06-01

申请号：US16017817

申请日：2018-06-25

Applicant: Microsoft Technology Licensing, LLC

Inventor： Ying Shan ,  Jian Jiao ,  Jie Zhu ,  Jianchang Mao

IPC: G06F16/2457 ,  G06F7/16 ,  G06N3/04 ,  G06N3/063 ,  G06N3/08 ,  G06F16/2455

Abstract: A computational search method for retrieving computer information related to a query includes transforming a plurality of candidate answers to candidate answer recurrent binary embedding (RBE) embeddings using a trained RBE model. A query is transformed to a query RBE embedding using the trained RBE model. The query RBE embedding is compared to each candidate answer RBE embedding of a plurality of candidate answer RBE embeddings using a similarity function. The candidate answers are sorted based on the comparisons made using the similarity function, and returning a plurality of the top candidate answers.