CN113204636A - Knowledge graph-based user dynamic personalized image drawing method - Google Patents
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Abstract
The invention discloses a knowledge graph-based user dynamic personalized image method, which comprises the following steps: collecting user data of a user to be represented in real time, and inputting a named entity recognition model to obtain an entity in the user data and a corresponding entity relation; classifying the entity data based on a knowledge fusion technology to obtain multi-dimensional behavior data of a user; constructing a knowledge graph facing to the user portrait after field screening is carried out on the behavior data; representing the entities in the knowledge graph by word vectors, and calculating Euclidean distances among the words; determining entities with similar semantics in the knowledge graph and the correlation between the words and the entities according to the Euclidean distance, and constructing a user behavior tag table according to the correlation; and updating the user behavior tag table according to the iterative processing of the real-time acquired data, and constructing a dynamic personalized image of the user. By the technical scheme, the dynamic personalized portrait of the user is obtained, and the accuracy and the real-time performance of the portrait of the user are guaranteed.
Description
Technical Field
The invention relates to the technical field of user portraits, in particular to a knowledge graph-based user dynamic personalized portraits method.
Background
At present, with the wide application of big data technology, different fields utilize big data to perform corresponding analysis on users of the big data technology, so as to obtain characteristics and attributes of user groups of the big data technology, and further expand users. In contrast, the image technology for the user population is being developed with great attention.
However, the existing portrait method for users adopts the same algorithm for different people and different users, the adopted corpora and data are also data from multiple channels and multiple fields, and only can simply reflect the portrait of the user, and the personalized portrait for the user people in different fields cannot be systematically, systematically and accurately reflected.
Disclosure of Invention
In addition, semantic similar entities in the data of the current user can be obtained through calculation of European distance in word vector space, so that a feature tag which can represent the current user most can be obtained through vector similarity, and the user data is collected and updated in real time, so that the dynamic personalized portrait of the user is obtained, and the accuracy and the real-time performance of the portrait of the user are ensured.
In order to achieve the above object, the present invention provides a method for dynamically and individually imaging a user based on a knowledge graph, which comprises the following steps: collecting user data of a user to be portrait in real time; inputting the user data into a named entity recognition model to obtain entities in the user data and corresponding entity relationships; performing data classification on the entity based on a knowledge fusion technology to obtain multidimensional behavior data of the user; constructing a knowledge graph facing the user portrait after field screening is carried out on the behavior data; representing the entities in the knowledge graph by word vectors, and calculating Euclidean distances among the words in a word vector space; determining entities with similar semantics in the knowledge graph and the correlation between the words and the entities according to the Euclidean distance, and constructing a user behavior tag table according to the correlation; and updating the user behavior tag table according to the iterative processing of the real-time collected data, and constructing the dynamic personalized image of the user.
In the above technical solution, preferably, the acquiring user data of a user to be depicted in real time specifically includes: and dynamically capturing multi-terminal, multi-system and multi-field data of the user in real time through a data capturing tool.
In the foregoing technical solution, preferably, the inputting the user data into a named entity recognition model to obtain the entity in the user data and the corresponding entity relationship specifically includes: inputting the user data into a named entity recognition model based on deep learning; and the named entity identification model carries out entity identification and relationship extraction on the user data to obtain an entity relationship between an entity in the user data and a corresponding entity.
In the foregoing technical solution, preferably, the obtaining the multidimensional behavior data of the user by performing data classification on the entity based on the knowledge fusion technology specifically includes: and classifying the entities obtained by the named entity recognition model by adopting a knowledge fusion technology based on a cross-modal shared subspace learning theory to obtain a multi-dimensional entity classification as the behavior data of the user.
In the foregoing technical solution, preferably, the representing the entities in the knowledge graph by word vectors, and calculating the euclidean distances between words in a word vector space specifically includes: expressing the entities in the knowledge graph by word vectors by using a depth semantic model to form a word vector space; and calculating Euclidean distances between related words in the word vector space by utilizing semantic similarity and logic correlation between the entities of the knowledge graph.
In the foregoing technical solution, preferably, the determining, according to the euclidean distance, entities with similar semantics in the knowledge graph and a correlation between a word and the entity, and the constructing a user behavior tag table specific body according to the correlation includes: obtaining entities with similar word expression semantics obtained by generalization of demand words in the knowledge graph according to the Euclidean distance; and (5) obtaining a label with similar semantics of the entity and the label by utilizing vector similarity calculation, and constructing a user behavior label table.
In the foregoing technical solution, preferably, the updating the user behavior tag table and constructing the dynamic personalized image of the user according to the iterative processing of the real-time collected data specifically includes: updating the behavior data of the user according to the data acquired in real time; updating the knowledge-graph according to the updated behavior data; updating the user behavior tag table according to the updated knowledge graph; and constructing the dynamic personalized image of the user according to the updated user behavior tag table.
In the foregoing technical solution, preferably, the feature tag in the dynamic personalized image is highlighted in a preset manner according to the frequency of occurrence, where the preset manner includes increasing the display font size of the feature tag, highlighting the feature tag in different colors, and displaying the feature tag in the central area of the dynamic personalized image or the density between the feature tag and other tags is less than the density between common tags.
Compared with the prior art, the invention has the beneficial effects that: the method comprises the steps of constructing a knowledge graph on data acquired by user portrait, mining and analyzing entities, entity relations, attributes and the like in the user data, performing visual display by using a visualization technology, organically combining with a user portrait technology, calculating European distance in a word vector space to obtain semantic similar entities in the data of a current user, obtaining a feature tag which can represent the current user most by vector similarity, acquiring and updating the user data in real time, obtaining dynamic personalized portrait of the user, and ensuring accuracy and real-time performance of user portrait.
Drawings
Fig. 1 is a flowchart illustrating a method for dynamically personalizing an image of a user based on a knowledge graph according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
The invention is described in further detail below with reference to the attached drawing figures:
as shown in fig. 1, the method for dynamically personalizing an image of a user based on a knowledge-graph provided by the invention comprises the following steps: collecting user data of a user to be portrait in real time; inputting the user data into a named entity recognition model to obtain entities in the user data and corresponding entity relationships; classifying the entity data based on a knowledge fusion technology to obtain multi-dimensional behavior data of a user; constructing a knowledge graph facing to the user portrait after field screening is carried out on the behavior data; representing entities in the knowledge graph by word vectors, and calculating Euclidean distances among the words in a word vector space; determining entities with similar semantics in the knowledge graph and the correlation between the words and the entities according to the Euclidean distance, and constructing a user behavior tag table according to the correlation; and updating the user behavior tag table according to the iterative processing of the real-time acquired data, and constructing a dynamic personalized image of the user.
In the embodiment, by constructing the knowledge graph of the data acquired by the user portrait, the entities, entity relations, attributes and the like in the user data can be mined and analyzed, visualized technical image display is utilized, and the data can be organically combined with the user portrait technology.
Specifically, specific requirement information is analyzed, and semantic generalization is performed to obtain a word expression close to the semantic of the requirement information. Then, semantic similarity and logic correlation between entities provided by the knowledge graph are utilized, Euclidean distances between related words are calculated in a word vector space, all words obtained through generalization of the demand words are represented, entities with similar semantics are searched in the knowledge graph, and the correlation between the words and the entities can be obtained through representation of the Euclidean distances. After the knowledge entity related to the semantics is obtained, a label table with the related entity being similar to the known user behavior label semantics is obtained by utilizing the similarity calculation of the vector. And according to the size of the similarity value, obtaining the strength of the correlation between the label and the corresponding user, and generating a user behavior label capable of representing the user characteristics.
Furthermore, by collecting a large amount of real user data in real time, including mobile phone APP behavior data of the user, browser search word data, entertainment consumption data of live game and the like, the attribute values of the relative entities are extracted, and description of the entities is enriched. In addition, new entities or new entity attributes are discovered through searching logs, and the coverage rate of the knowledge graph is continuously expanded. Based on the method, the dynamic updating of the user personalized image is realized, and the real-time performance and the accuracy of the user portrait are ensured.
In the above embodiment, preferably, the acquiring, in real time, user data of a user to be portrait specifically includes: the data capture tool is used for dynamically capturing multi-terminal, multi-system and multi-field data of the user in real time, such as mobile phone APP behavior data, browser search word data, entertainment consumption data of live game and the like of the user.
In the foregoing embodiment, preferably, the inputting the user data into the named entity recognition model to obtain the entity and the corresponding entity relationship in the user data specifically includes: inputting user data into a named entity recognition model based on deep learning; the named entity recognition model carries out entity recognition, relationship extraction and attribute extraction technologies on the user data to obtain entity relationships between entities in the user data and corresponding entities.
In the foregoing embodiment, preferably, the data classification of the entity based on the knowledge fusion technology to obtain the multidimensional behavior data of the user specifically includes: and classifying the entities obtained by the named entity recognition model by adopting a knowledge fusion technology based on a cross-modal shared subspace learning theory to obtain multi-dimensional entity classification as behavior data of the user, wherein the multi-dimensional entity classification comprises social attribute, living habits, consumption behaviors and other data.
In the above embodiment, preferably, the entity in the knowledge-graph is represented by a word vector, and calculating the euclidean distance between words in the word vector space specifically includes: expressing entities in the knowledge graph by word vectors by using a depth semantic model to form a word vector space; and calculating Euclidean distances between related words in a word vector space by utilizing semantic similarity and logic correlation between the entities of the knowledge graph.
In the above embodiment, preferably, determining entities with similar semantics in the knowledge graph and correlations between the words and the entities according to the euclidean distance, and constructing the user behavior tag table specifically according to the correlations includes: obtaining entities with similar word expression semantics obtained by generalization of demand words in a knowledge graph according to the Euclidean distance; and (5) obtaining a label with similar semantics of the entity and the label by utilizing vector similarity calculation, and constructing a user behavior label table.
Specifically, a deep semantic model is used for representing word vector, semantic similarity and logic correlation between entities provided by a knowledge graph are used, Euclidean distances between related words are calculated in a word vector space, all words obtained through generalization of demand words are represented, entities with similar semantics are searched in the knowledge graph, and the correlation between the words and the entities can be obtained through representation of the Euclidean distances. After the knowledge entity related to the semantics is obtained, a label table with the related entity similar to the known user behavior label semantics is obtained by utilizing the similarity calculation of the vector. And according to the magnitude of the similarity value, obtaining the strength of the correlation of the user corresponding to the label through combined calculation, and generating a user behavior label capable of representing the characteristics of the user.
In the above embodiment, preferably, the updating the user behavior tag table and the constructing the dynamic personalized image of the user according to the iterative processing of the real-time collected data specifically includes: updating the behavior data of the user according to the data acquired in real time; updating the knowledge graph according to the updated behavior data; updating a user behavior tag table according to the updated knowledge graph; and constructing the dynamic personalized image of the user according to the updated user behavior tag table.
In the above embodiment, preferably, the feature tag in the dynamic personalized image is highlighted according to the frequency of occurrence in a preset manner, where the preset manner includes increasing the font size of the feature tag, highlighting the feature tag in different colors, and displaying the feature tag in the central area of the dynamic personalized image or the density between the feature tag and other tags is less than the density between common tags.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A method for dynamically and individually imaging a user based on a knowledge graph is characterized by comprising the following steps:
collecting user data of a user to be portrait in real time;
inputting the user data into a named entity recognition model to obtain entities in the user data and corresponding entity relationships;
performing data classification on the entity based on a knowledge fusion technology to obtain multidimensional behavior data of the user;
constructing a knowledge graph facing the user portrait after field screening is carried out on the behavior data;
representing the entities in the knowledge graph by word vectors, and calculating Euclidean distances among the words in a word vector space;
determining entities with similar semantics in the knowledge graph and the correlation between the words and the entities according to the Euclidean distance, and constructing a user behavior tag table according to the correlation;
and updating the user behavior tag table according to the iterative processing of the real-time collected data, and constructing the dynamic personalized image of the user.
2. The method for dynamically and individually portraying a user based on a knowledge graph according to claim 1, wherein the step of collecting user data of the user to be portrayed in real time specifically comprises:
and dynamically capturing multi-terminal, multi-system and multi-field data of the user in real time through a data capturing tool.
3. The method of claim 1, wherein the entering of the user data into a named entity recognition model to obtain the entities and corresponding entity relationships in the user data specifically comprises:
inputting the user data into a named entity recognition model based on deep learning;
and the named entity identification model carries out entity identification and relationship extraction on the user data to obtain an entity relationship between an entity in the user data and a corresponding entity.
4. The method for dynamically and individually imaging a knowledge-graph-based user according to claim 1, wherein the data classification of the entity based on the knowledge fusion technology to obtain the multidimensional behavior data of the user specifically comprises:
and classifying the entities obtained by the named entity recognition model by adopting a knowledge fusion technology based on a cross-modal shared subspace learning theory to obtain a multi-dimensional entity classification as the behavior data of the user.
5. The method of claim 1, wherein the knowledge-graph-based user dynamic personalized imaging is represented by word vectors, and the calculating of Euclidean distances between words in word vector space specifically comprises:
expressing the entities in the knowledge graph by word vectors by using a depth semantic model to form a word vector space;
and calculating Euclidean distances between related words in the word vector space by utilizing semantic similarity and logic correlation between the entities of the knowledge graph.
6. The method of claim 1, wherein the determining semantic similar entities in the knowledge graph and the correlation between words and the entities according to the Euclidean distance comprises:
obtaining entities with similar word expression semantics obtained by generalization of demand words in the knowledge graph according to the Euclidean distance;
and (5) obtaining a label with similar semantics of the entity and the label by utilizing vector similarity calculation, and constructing a user behavior label table.
7. The method of claim 1, wherein the updating the user behavior tag table and the constructing the dynamic personalized image of the user according to the iterative processing of the real-time collected data specifically comprises:
updating the behavior data of the user according to the data acquired in real time;
updating the knowledge-graph according to the updated behavior data;
updating the user behavior tag table according to the updated knowledge graph;
and constructing the dynamic personalized image of the user according to the updated user behavior tag table.
8. The method for dynamically customizing an image according to claim 1, wherein the feature tag in the dynamically personalized image is highlighted according to the frequency of occurrence in a preset manner, wherein the preset manner comprises increasing the font size of the feature tag, highlighting the feature tag in different colors, displaying the feature tag in the central area of the dynamically personalized image, or enabling the density between the feature tag and other tags to be smaller than that between common tags.
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