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Context-sensitive search using a deep learning model

A technology of deep learning and context, applied in biological neural network models, special data processing applications, instruments, etc., can solve problems such as poor user experience

Active Publication Date: 2017-02-15
MICROSOFT TECH LICENSING LLC
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  • Summary
  • Abstract
  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

In those scenarios, the search engine may provide a poor user experience to the user because the results will contain many documents that are not relevant to the user's actual search purpose

Method used

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  • Context-sensitive search using a deep learning model
  • Context-sensitive search using a deep learning model
  • Context-sensitive search using a deep learning model

Examples

Experimental program
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Embodiment Construction

[0021] The public organization is as follows. Part A describes illustrative functions for performing searches based on contextual information associated with the submitted query by applying a deep learning model. Part B sets out illustrative methods that explain the operation of the functions in Part A. Part C describes illustrative computational functions that can be used to implement any aspect of the features described in Part A and Part B.

[0022] As preliminary content, some of the drawings in the drawings describe concepts in the context of one or more structural components that are differently referred to as functions, modules, features, elements, etc. It can be implemented in any way by any physical or tangible mechanism (for example, software running on a computer device, hardware (for example, a logic function implemented by a chip), etc., and / or any combination thereof). Various components shown. In one case, the illustrated division of various components in the dr...

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PUM

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Abstract

A search engine is described herein for providing search results based on a context in which a query has been submitted, as expressed by context information. The search engine operates by ranking a plurality of documents based on a consideration of the query, and based, in part, on a context concept vector and a plurality of document concept vectors, both generated using a deep learning model (such as a deep neural network). The context concept vector is formed by a projection of the context information into a semantic space using the deep learning model. Each document concept vector is formed by a projection of document information, associated with a particular document, into the same semantic space using the deep learning model. The ranking operates by favoring documents that are relevant to the context within the semantic space, and disfavoring documents that are not relevant to the context.

Description

Background technique [0001] Search engines can use ranking algorithms to rank the relevance of documents regarding the query entered by the user. In many cases, ranking algorithms provide results that strongly match the user's previous search-related behavior. For example, consider an illustrative scenario where the user enters a search term: "Michael Jordan". The sorting algorithm can provide sorting results that emphasize those documents that contain the name of the famous basketball player Michael Jordan. This result reflects the fact that the vast majority of users who entered the search term "Michael Jordan" are interested in accessing documents about basketball player Michael Jordan. However, some users who entered the name "Michael Jordan" may be interested in exploring other topics of interest, such as works done by an architect named Michael Jordan. In those scenarios, the search engine may provide users with a poor user experience, because the results will contain m...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06N3/04
CPCG06F16/9535G06N3/045G06F16/248G06F16/24578
Inventor 过晨雷高剑峰王野翊L·邓何晓冬
Owner MICROSOFT TECH LICENSING LLC
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