Model generation method and device as well as recommendation method and device

A technology of model generation and model fusion, applied in the search field, can solve problems such as failure to arouse user interest, low click-through rate of recommended entities, and inability to meet user search and recommendation needs, and achieve the effect of improving click-through rate

Active Publication Date: 2016-02-17
BAIDU ONLINE NETWORK TECH (BEIJIBG) CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, in the prior art, when searching and recommending based on knowledge graphs, the recommended entities are often well-known and cannot arouse user interest.
Therefore, this search recommendation method cannot meet the user's search and recommendation needs, resulting in a relatively low click-through rate for recommended entities.

Method used

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  • Model generation method and device as well as recommendation method and device
  • Model generation method and device as well as recommendation method and device
  • Model generation method and device as well as recommendation method and device

Examples

Experimental program
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Embodiment 1

[0059] The embodiment of the present invention provides a model generation method, please refer to figure 2 , which is a schematic flow chart of the model generation method provided by the embodiment of the present invention. As shown in the figure, the method includes the following steps:

[0060] S201. Obtain at least one of a document content feature vector of each entity in the knowledge graph, a logical relationship feature vector between entities, a user behavior relationship feature vector of each entity, and a feature vector of each entity.

[0061] S202. Perform machine learning according to at least one of the document content feature vector, the logical association relationship feature vector, the user behavior relationship feature vector, and the feature vector to generate a deep fusion model.

[0062] It should be noted that various entities and related information of each entity are defined in the knowledge graph; the entities refer to things in real life, such ...

Embodiment 2

[0076] An embodiment of the present invention provides a recommendation method. The deep fusion model used in the recommendation method provided in this embodiment is the deep fusion model generated by the model generation method provided in the first embodiment above. Please refer to Figure 4 , which is a schematic flowchart of a recommended method provided by an embodiment of the present invention. As shown in the figure, the method includes the following steps:

[0077] S401. Obtain a candidate entity corresponding to the specified entity.

[0078] S402. At least one of the document content feature vector of the specified entity, the logical association relationship feature vector between the specified entity and the candidate entity, the user behavior relationship feature vector of the specified entity, and the feature vector of the specified entity, and all At least one of the document content feature vector of the candidate entity and the feature vector of the candidat...

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Abstract

The embodiment of the invention provides a model generation method, a model generation device, a recommendation method and a recommendation device. The model generation method in the embodiment of the invention comprises the following steps: acquiring a document content feature vector of each entity in a mapping knowledge domain, logical relationship feature vectors among the entities, a user behavior relationship feature vector of each entity and at least one of feature vectors of each entity, then performing machine learning according to the document content feature vectors, the logical relationship feature vectors, the user behavior relationship feature vectors and the at least one of the feature vectors, and generating a deep fusion model. Therefore, according to the technical scheme provided by the embodiment of the invention, the deep fusion model can be generated by integrating various relationships among the entities and can be used for acquiring the surprise degree among the entities, so that the entities can be recommended to users based on the surprise degree, the search recommendation requirements of the users are met, and the click rate of the recommended entities is improved.

Description

【Technical field】 [0001] The present invention relates to the field of search technology, in particular to a model generation method and device, and a recommendation method and device. 【Background technique】 [0002] At present, when the search recommendation is performed, it is based on satisfying the user's main search demand, and stimulating the potential demand of the user by providing the user with other potentially interesting content related to the query word. For example, please refer to figure 1 , which is the first example of search recommendation based on knowledge graphs in the prior art. As shown in the figure, when a user queries "Princeton University", it can be recommended in the non-search result area of ​​the search result page figure 1 Notable alumni of Princeton University shown, this is a recommended entity that is very relevant to the query term "Princeton University". [0003] However, in the prior art, when searching and recommending based on knowle...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCG06F16/9535G06F16/00
Inventor 黄际洲孙明明丁世强王海峰
Owner BAIDU ONLINE NETWORK TECH (BEIJIBG) CO LTD
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