CN114398557A - Information recommendation method and device based on double portraits, electronic equipment and storage medium - Google Patents
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Abstract
The invention relates to a data analysis technology, and discloses an information recommendation method based on double portraits, which comprises the following steps: the method comprises the steps of constructing information portraits of a first preset number of fields and user portraits of a second preset number of target users, calculating a matching value of each user portrait and each information portrait, obtaining information to be recommended of each user portrait according to the matching value, extracting core features of each user portrait, extracting comparison features of information to be recommended corresponding to each user portrait, calculating a distance between each core feature and each comparison feature corresponding to each core feature, sequencing the information to be recommended according to the distance, and recommending the information to be recommended to each target user corresponding to each core feature according to a sequencing result. The invention also provides an information recommendation device, equipment and a medium based on the double portrait. The invention can improve the accuracy of information recommendation.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to a double-portrait-based information recommendation method and device, electronic equipment and a computer-readable storage medium.
Background
In the face of the current information explosion and the individuation of user requirements, information recommendation is an effective scheme for solving the problem of excessive information in the era of knowledge explosion. For example, the ecological environment management information website provides ecological environment information in different fields, including ecological environment information in the fields of atmospheric treatment, water pollution management, soil environment monitoring, nuclear and radiation monitoring and the like, and how to promote the accuracy of information recommendation and ensure different information users and how to quickly acquire the information concerned by the users is a very key matter in the information operation process, facing different ecological environment managers, researchers, workers and ecological environment concerns.
Most of the traditional information recommendation methods focus on one side of information or one side of a user, perform relevant big data analysis and determine the matching relation between the information and the user. For example, extracting feature tags of information, constructing a product portrait of the information, pushing differentiated information to a target user based on the product portrait, or analyzing historical behavior data of the user, constructing a user portrait of the user, and selecting to push personalized information to the user based on the difference of the user portrait, the method of analyzing only the information data or only the user data ignores the relevance between the information data and the user data, which results in lower accuracy of information recommendation.
Disclosure of Invention
The invention provides a method and a device for recommending information based on double portraits and a computer readable storage medium, and mainly aims to improve the accuracy of recommending information.
In order to achieve the above object, the present invention provides a method for recommending information based on a dual portrait, comprising:
acquiring information data of a first preset number of fields, and generating an information portrait of each field by using the information data of the first preset number of fields;
acquiring basic data of a second preset number of target users and behavior data of each target user, and generating a user portrait of each target user by using the basic data and the behavior data;
calculating a matching value of each user image and each information image, and collecting information data corresponding to the information images with the matching values larger than a preset threshold value as information to be recommended;
extracting key features of each user portrait as core features, and extracting key features of information to be recommended corresponding to each user portrait as comparison features;
calculating the distance between each core feature and each comparison feature corresponding to the core feature, sorting the messages to be recommended corresponding to each comparison feature according to the distance, and recommending the messages to be recommended to the target user corresponding to each core feature according to a sorting result.
Optionally, the generating an information portrait of each domain by using the information data of the first preset number of domains includes:
sequentially performing core semantic extraction on the information data of each field in the information data of the first preset number of fields to obtain the information semantics of each field;
performing word vector conversion on the information semantics to obtain an information semantics vector;
and constructing an information portrait of the corresponding field by using the information semantic vector.
Optionally, the sequentially performing core semantic extraction on the information data of each field in the information data of the first preset number of fields to obtain the information semantics of each field includes:
performing convolution and pooling on the information data of each field to obtain low-dimensional feature semantics of the information data;
mapping the low-dimensional feature semantics to a pre-constructed high-dimensional space to obtain high-dimensional feature semantics;
and screening the high-dimensional characteristic semantics by using a preset activation function to obtain the information semantics of each field.
Optionally, the constructing an information portrait of a corresponding domain by using the information semantic vector includes:
counting the vector length of each vector in the information semantic vectors, and selecting the vector with the longest vector length as a mode vector;
utilizing preset parameters to extend the length of each residual vector in the information semantic vectors to be the same as the vector length of the module vector;
and splicing each vector in the information semantic vectors with the extended vector length as a row vector into a vector matrix, and taking the vector matrix as an information portrait of a corresponding area.
Optionally, the extracting key features of each user portrait as core features includes:
extracting features in each user portrait by utilizing a pre-constructed semantic analysis model;
carrying out vector mapping on the features in each user portrait to obtain a feature vector set;
randomly selecting a preset number of feature vectors from the feature vector set as clustering centers;
sequentially calculating the distance from each feature vector in the feature vector set to the clustering center, and dividing each feature vector into categories corresponding to the clustering center with the minimum distance to obtain a plurality of category clusters;
and recalculating the clustering center of each category cluster, returning to the step of sequentially calculating the distance from each feature vector in the feature vector set to the clustering center until the clustering centers of the plurality of category clusters are converged, and taking the category corresponding to the converged category cluster as the key feature of the user portrait.
Optionally, the extracting key features of information to be recommended corresponding to each user portrait as comparison features includes:
performing word segmentation processing on each piece of information to be recommended one by one to obtain information word segmentation corresponding to each piece of information to be recommended;
collecting all the information participles into an information word bank;
selecting each piece of information to be recommended one by one as information to be analyzed, and selecting one information word from information words corresponding to the information to be analyzed as a target word;
counting a first occurrence frequency of the target word in the information word corresponding to the information to be analyzed and a second occurrence frequency of the target word in the information word stock, and calculating a ratio of the second occurrence frequency to the first occurrence frequency;
and selecting the information participles with the ratio larger than a preset ratio threshold value as key features of the information to be analyzed.
Optionally, the calculating a distance between each core feature and each aligned feature corresponding to the core feature includes:
taking each core feature as a clustering center of a corresponding target user, and taking the clustering center as a target label;
sequentially selecting any one comparison characteristic as a comparison label, and performing word segmentation processing on the target label and the comparison label to obtain a target list and a comparison list;
constructing a coding dictionary according to the target list and the comparison list;
vector coding is carried out on the target list and the comparison list by utilizing the coding dictionary to obtain a target vector and a comparison vector;
and calculating the target similarity of the target vector and the comparison vector by using a preset cosine similarity calculation formula, and obtaining the distance between each core feature and each comparison feature corresponding to the core feature according to the target similarity.
In order to solve the above problem, the present invention further provides a dual portrait-based information recommendation apparatus, comprising:
the information portrait generating module is used for acquiring information data of a first preset number of fields and generating information portrait of each field by using the information data of the first preset number of fields;
the user portrait generation module is used for acquiring basic data of a second preset number of target users and behavior data of each target user and generating a user portrait of each target user by using the basic data and the behavior data;
the information to be recommended generating module is used for calculating a matching value of each user portrait and each information portrait and collecting information data corresponding to the information portraits of which the matching values are greater than a preset threshold value as information to be recommended;
the recommendation information sorting module is used for extracting the key features of each user portrait as core features and extracting the key features of the information to be recommended corresponding to each user portrait as comparison features; calculating the distance between each core feature and each comparison feature corresponding to the core feature, sorting the messages to be recommended corresponding to each comparison feature according to the distance, and recommending the messages to be recommended to the target user corresponding to each core feature according to a sorting result.
In order to solve the above problem, the present invention also provides an electronic device, including:
a memory storing at least one computer program; and
and the processor executes the program stored in the memory to realize the information recommendation method based on the double portrait.
In order to solve the above problem, the present invention further provides a computer-readable storage medium, in which at least one computer program is stored, and the at least one computer program is executed by a processor in an electronic device to implement the dual portrait-based information recommendation method described above.
The method comprises the steps of constructing an information portrait and a user portrait, calculating a matching value of each user portrait and each information portrait, obtaining information to be recommended of each user portrait, further extracting core features of each user portrait, extracting comparison features of the information to be recommended corresponding to each user portrait, calculating a distance between each core feature and each comparison feature corresponding to each core feature, sequencing the information to be recommended according to the distance, recommending the user according to a sequencing result, and improving accuracy of information recommendation.
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FIG. 1 is a flowchart illustrating a method for dual portrait-based information recommendation according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a detailed implementation of one step of the dual-portrait based information recommendation method shown in FIG. 1;
FIG. 3 is a flowchart illustrating another step of the method for dual-portrait based information recommendation shown in FIG. 1;
FIG. 4 is a functional block diagram of a dual-portrait based information recommendation apparatus according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device implementing the dual-portrait-based information recommendation method according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the application provides an information recommendation method based on double portrait. The execution subject of the double-portrait-based information recommendation method includes, but is not limited to, at least one of a server, a terminal, and other electronic devices that can be configured to execute the method provided by the embodiments of the present application. In other words, the dual portrait-based information recommendation method may be executed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The server side can be an independent server, and can also be a cloud server providing basic cloud computing services such as cloud service, a cloud database, cloud computing, cloud functions, cloud storage, Network service, cloud communication, middleware service, domain name service, security service, Content Delivery Network (CDN), big data and an artificial intelligence platform.
Fig. 1 is a schematic flow chart of a method for recommending information based on a dual portrait according to an embodiment of the present invention. In this embodiment, the information recommendation method based on dual portrait includes:
s1, acquiring information data of a first preset number of fields, and generating an information portrait of each field by using the information data of the first preset number of fields;
in the embodiment of the invention, the information recommendation method based on double portraits is described by taking ecological environment management information data as an example. The ecological environment management information data package relates to a plurality of fields, such as different fields of atmosphere, water, solid waste, sound, soil, nuclear and radiation, ecological resources and the like. It can be understood that the different fields of the eco-management information data have different characteristics, different information sources, different information classifications, and different attention degrees with respect to different areas.
In the embodiment of the present invention, the first preset number may be determined according to a range related to actual ecological environment management information data.
In the embodiment of the present invention, the information data may be retrieved from a pre-constructed storage area for storing the information data through a preset external interface or through a computer script having a data fetching function, such as a java script or python. In detail, the storage area includes, but is not limited to: database, block chain node, network cache.
In the embodiment of the invention, the core data in each field information data can be extracted by performing data analysis on the multi-field information data, and the information portrait of the corresponding information data is constructed according to the extracted core data.
In detail, referring to fig. 2, the S1 includes:
s11, core semantic extraction is sequentially carried out on the information data of each field in the information data of the first preset number of fields, and the information semantics of each field are obtained;
s12, performing word vector conversion on the information semantics to obtain information semantics vectors;
and S13, constructing an information portrait of the corresponding field by using the information semantic vector.
In the embodiment of the invention, a pre-constructed semantic analysis model is used for extracting the core semantics of the information data to obtain the information semantics. Wherein the core semantics include but are not limited to information sources, information domain dependencies, key information terms, information popularity, and the like.
In detail, the semantic analysis Model includes, but is not limited to, a Natural Language Processing (NLP) Model, a Hidden Markov Model (HMM) Model.
In detail, the performing core semantic extraction on the information data of each field in the information data of the first preset number of fields in sequence to obtain the information semantic of each field includes: performing convolution and pooling on the information data of each field to obtain low-dimensional feature semantics of the information data; mapping the low-dimensional feature semantics to a pre-constructed high-dimensional space to obtain high-dimensional feature semantics; and screening the high-dimensional characteristic semantics by using a preset activation function to obtain the information semantics of each field.
In detail, the information data can be subjected to convolution and pooling processing through a semantic analysis model so as to reduce the data dimension of the information data, further reduce the occupation of computing resources when the information data is analyzed, and improve the efficiency of core semantic extraction.
Specifically, the low-dimensional feature semantics can be mapped to the pre-constructed high-dimensional space by using a preset mapping Function, wherein the mapping Function comprises a Gaussian Radial Basis Function, a Gaussian Function and the like in the MATLAB library.
For example, if the low-dimensional feature semantics are points in a two-dimensional plane, a mapping function may be used to calculate two-dimensional coordinates of the points in the two-dimensional plane to convert the two-dimensional coordinates into three-dimensional coordinates, and the calculated three-dimensional coordinates are used to map the points to a pre-constructed three-dimensional space, so as to obtain high-dimensional feature semantics of the low-dimensional feature semantics.
And mapping the low-dimensional feature semantics to a pre-constructed high-dimensional space, so that the classifiability of the low-dimensional feature can be improved, and the accuracy of screening the features from the obtained high-dimensional feature semantics to obtain the information semantics is further improved.
In the embodiment of the invention, a preset activation function can be used for calculating the output value of each feature semantic in the high-dimensional feature semantics, and the feature semantics of which the output value is greater than a preset output threshold value are selected as product semantics, wherein the activation function includes but is not limited to a sigmoid activation function, a tanh activation function and a relu activation function.
For example, the high-dimensional feature semantics include feature semantics a, feature semantics B, and feature semantics C, and the feature semantics a, the feature semantics B, and the feature semantics C are calculated by using an activation function, respectively, to obtain an output value of the feature semantics a of 80, an output value of the feature semantics B of 30, and an output value of the feature semantics C of 70, and when an output threshold value is 50, the feature semantics a and the feature semantics C are output as the information semantics of the information data.
In the embodiment of the invention, word vector conversion can be carried out on the information semantics through a preset vector conversion model to obtain an information semantic vector, wherein the vector conversion model comprises but is not limited to a word2vec model and a Bert model.
In detail, the constructing an information portrait of a corresponding domain by using the information semantic vector includes: counting the vector length of each vector in the information semantic vectors, and selecting the vector with the longest vector length as a mode vector; utilizing preset parameters to extend the length of each residual vector in the information semantic vectors to be the same as the vector length of the module vector; and splicing each vector in the information semantic vectors with the extended vector length as a row vector into a vector matrix, and taking the vector matrix as an information portrait of a corresponding area.
For example, there is vector a in the information semantic vector: (1,3), vector B: (2,4,6), vector C: (5,7,8 and 9), counting to obtain that the vector length of the vector A is 2, the vector length of the vector B is 3, and the vector length of the vector C is 4, and selecting the vector C as a mode vector; when the preset parameter is x, the vector A and the vector B can be subjected to vector extension by using the preset parameter to obtain an extended vector A: (1,3, x, x), and the extended vector B: (2,4,6, x).
In detail, each vector in the information semantic vector after vector extension can be spliced as a row vector into the following vector matrix:
in the embodiment of the invention, the vector matrix obtained by splicing is used as the information portrait of the corresponding area.
S2, acquiring basic data of a second preset number of target users and behavior data of each target user, and generating a user portrait of each target user by using the basic data and the behavior data;
in the embodiment of the present invention, the target user may be an ecological environment quality manager, a monitor, and a researcher in different business fields, and it can be understood that managers, monitors, and researchers in different business fields, different geographic areas, different management posts, or different monitoring tasks hope to quickly obtain effective information data most directly related to their business fields from numerous and complicated ecological environment quality management information data.
In the embodiment of the present invention, the second preset number may be determined according to the classification number of the actual target user.
In the embodiment of the present invention, the basic data of the target user includes basic attribute data of the user, for example, basic data such as a geographic area where the target user is located, a service field in charge, a department where the target user is located, a post where the target user is located, and the like.
In the embodiment of the present invention, the behavior data of the target user refers to a behavior habit of the user browsing the information data, and includes, but is not limited to, information names, information categories, browsing times, browsing duration, browsing time and other data corresponding to browsed products.
In the embodiment of the present invention, the basic data and the behavior data of the target user may be retrieved from a pre-constructed storage area for storing the information data through a preset external interface or through a computer script with a data fetching function, such as a java script or python.
Further, the step of generating the user image of each target user by using the basic data and the behavior data is consistent with the step of generating the information image of each field by using the information data of the first preset number of fields in step S1, and is not repeated herein.
Preferably, in order to avoid revealing privacy of the user during the process of generating the user representation, before generating the user representation of each target user by using the basic data and the behavior data, the method further includes: desensitizing the target user's base data and behavioral data.
S3, calculating a matching value of each user portrait and each information portrait, and collecting information data corresponding to the information portraits with the matching values larger than a preset threshold value as information to be recommended;
in the embodiment of the invention, a preset matching value algorithm can be used for calculating the matching value between the user portrait and each information portrait, and then the information data corresponding to the information portrait with the matching value larger than a preset threshold value is selected from the information portraits as the information to be recommended.
In detail, the calculating a matching value of each of the user images and each of the information images includes:
calculating a match value for each of said user representation and each of said information representations using the following match value algorithm formula:
wherein P is the match value, x is the user representation, yiAlpha is a predetermined coefficient for the ith information image.
For example, there are an information image A, an information image B and an information image C, the matching value between the user image and the information image A is 80, the matching value between the user image and the information image B is 70, the matching value between the user image and the information image C is 20, and when the preset threshold value is 60, the product corresponding to the information image A and the information image B is selected as the information to be recommended.
S4, extracting key features of each user portrait as core features, and extracting key features of information to be recommended corresponding to each user portrait as comparison features;
in the embodiment of the invention, the key features of each user portrait and the key features of the information to be recommended corresponding to each user portrait can be extracted through a machine learning model based on a neural network or a preset keyword extraction algorithm.
In detail, the extracting key features of each user portrait as core features includes: extracting features in each user portrait by utilizing a pre-constructed semantic analysis model; carrying out vector mapping on the features in each user portrait to obtain a feature vector set;
randomly selecting a preset number of feature vectors from the feature vector set as clustering centers;
sequentially calculating the distance from each feature vector in the feature vector set to the clustering center, and dividing each feature vector into categories corresponding to the clustering center with the minimum distance to obtain a plurality of category clusters;
and recalculating the clustering center of each category cluster, returning to the step of sequentially calculating the distance from each feature vector in the feature vector set to the clustering center until the clustering centers of the plurality of category clusters are converged, and taking the category corresponding to the converged category cluster as the key feature of the user portrait.
In the embodiment of the present invention, the distance from each feature vector in the feature vector set to the clustering center may be an euclidean distance, a manhattan distance, a chebyshev distance, or the like.
In detail, the recalculating the cluster center for each category cluster includes:
calculating the clustering center of each category cluster by using the following clustering formula:
wherein E isiDenotes the ith cluster center, CiRepresenting the ith category cluster, and x is a feature vector in the category cluster.
Further, the step of extracting the key feature of the information to be recommended corresponding to each user portrait as the comparison feature may adopt a step identical to the step of extracting the key feature of each user portrait as the core feature, or may adopt a clustering process using a mean shift clustering method, a density-based clustering method (DBSCAN), a maximum Expectation (EM) clustering method using a Gaussian Mixture Model (GMM), or the like.
In another embodiment of the present invention, the extracting key features of information to be recommended corresponding to each user portrait as comparison features includes: performing word segmentation processing on each piece of information to be recommended one by one to obtain information word segmentation corresponding to each piece of information to be recommended; collecting all the information participles into an information word bank; selecting each piece of information to be recommended one by one as information to be analyzed, and selecting one information word from information words corresponding to the information to be analyzed as a target word; counting a first occurrence frequency of the target word in the information word corresponding to the information to be analyzed and a second occurrence frequency of the target word in the information word stock, and calculating a ratio of the second occurrence frequency to the first occurrence frequency; and selecting the information participles with the ratio larger than a preset ratio threshold value as key features of the information to be analyzed.
In the embodiment of the invention, the information to be recommended can be subjected to word segmentation processing by utilizing a preset standard dictionary, and the standard dictionary comprises a plurality of standard words.
It is understood that, when the frequency of any one of the target segmented words in the information thesaurus is higher, and the frequency of the target segmented word in all the information to be analyzed is lower, the importance of the target segmented word to the information to be recommended is considered to be lower.
On the contrary, when the frequency of any one of the target participles appearing in the information thesaurus is lower and the frequency of the target participles appearing in all the information to be analyzed is higher, the importance of the target participle to the information to be recommended can be considered to be higher.
In the embodiment of the present invention, the preset ratio threshold may be determined according to an actual situation.
S5, calculating the distance between each core feature and each comparison feature corresponding to the core feature, sorting the messages to be recommended corresponding to each comparison feature according to the distance, and recommending the messages to be recommended to the target user corresponding to each core feature according to the sorting result.
In the embodiment of the invention, the distance between each core feature and each comparison feature corresponding to the core feature can be calculated and calculated through a clustering algorithm or a distance algorithm.
In detail, referring to fig. 3, the calculating a distance between each of the core features and each of the aligned features corresponding to the core features includes:
s51, taking each core feature as a clustering center of a corresponding target user, and taking the clustering center as a target label;
s52, sequentially selecting any one comparison feature as a comparison label, and performing word segmentation processing on the target label and the comparison label to obtain a target list and a comparison list;
s53, constructing an encoding dictionary according to the target list and the comparison list;
s54, carrying out vector coding on the target list and the comparison list by using the coding dictionary to obtain a target vector and a comparison vector;
s55, calculating the target similarity of the target vector and the comparison vector by using a preset cosine similarity calculation formula, and obtaining the distance between each core feature and each comparison feature corresponding to the core feature according to the target similarity.
In the embodiment of the invention, the core characteristic and the comparison characteristic are compared or calculated, and the information with high matching degree with the user image is obtained from the information to be recommended, so that the accuracy of information recommendation is improved.
The method comprises the steps of constructing an information portrait and a user portrait, calculating a matching value of each user portrait and each information portrait, obtaining information to be recommended of each user portrait, further extracting core features of each user portrait, extracting comparison features of the information to be recommended corresponding to each user portrait, calculating a distance between each core feature and each comparison feature corresponding to each core feature, sequencing the information to be recommended according to the distance, recommending the user according to a sequencing result, and improving accuracy of information recommendation.
FIG. 4 is a functional block diagram of a dual-portrait based information recommendation apparatus according to an embodiment of the present invention.
The double portrait-based information recommendation apparatus 100 according to the present invention may be installed in an electronic device. According to the realized function, the double-portrait-based information recommendation device 100 can comprise an information portrait generation module 101, a user portrait generation module 102, an information to be recommended generation module 103 and a recommendation information sorting module 104. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the information portrait generating module 101 is configured to obtain information data of a first preset number of fields, and generate an information portrait of each field by using the information data of the first preset number of fields;
the user portrait generation module 102 is configured to obtain basic data of a second preset number of target users and behavior data of each target user, and generate a user portrait of each target user by using the basic data and the behavior data;
the information to be recommended generation module 103 is configured to calculate a matching value between each user portrait and each information portrait, and collect information data corresponding to the information portrait of which the matching value is greater than a preset threshold as information to be recommended;
the recommendation information sorting module 104 is configured to extract a key feature of each user portrait as a core feature, and extract a key feature of information to be recommended corresponding to each user portrait as a comparison feature; calculating the distance between each core feature and each comparison feature corresponding to the core feature, sorting the messages to be recommended corresponding to each comparison feature according to the distance, and recommending the messages to be recommended to the target user corresponding to each core feature according to a sorting result.
In detail, in the embodiment of the present invention, each module of the dual-image-based information recommendation apparatus 100 adopts the same technical means as the dual-image-based information recommendation method described in fig. 1 to 3, and can produce the same technical effect, which is not described herein again.
Fig. 5 is a schematic structural diagram of an electronic device for implementing a dual-portrait-based information recommendation method according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11 and a bus, and may further comprise a computer program, such as an information recommendation program, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, which includes flash memory, removable hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only to store application software installed in the electronic device 1 and various types of data, such as codes of information recommendation programs, etc., but also to temporarily store data that has been output or is to be output.
The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device 1 by running or executing programs or modules (such as information recommendation programs) stored in the memory 11 and calling data stored in the memory 11.
The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
Fig. 5 only shows an electronic device with components, and it will be understood by a person skilled in the art that the structure shown in fig. 5 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or a combination of certain components, or a different arrangement of components.
For example, although not shown, the electronic device 1 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so as to implement functions of charge management, discharge management, power consumption management, and the like through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 1 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
Further, the electronic device 1 may further include a network interface, and optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a bluetooth interface, etc.), which are generally used for establishing a communication connection between the electronic device 1 and other electronic devices.
Optionally, the electronic device 1 may further comprise a user interface, which may be a Display (Display), an input unit (such as a Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the electronic device 1 and for displaying a visualized user interface, among other things.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The information recommendation program stored in the memory 11 of the electronic device 1 is a combination of a plurality of instructions, which when executed in the processor 10, can realize:
acquiring information data of a first preset number of fields, and generating an information portrait of each field by using the information data of the first preset number of fields;
acquiring basic data of a second preset number of target users and behavior data of each target user, and generating a user portrait of each target user by using the basic data and the behavior data;
calculating a matching value of each user image and each information image, and collecting information data corresponding to the information images with the matching values larger than a preset threshold value as information to be recommended;
extracting key features of each user portrait as core features, and extracting key features of information to be recommended corresponding to each user portrait as comparison features;
calculating the distance between each core feature and each comparison feature corresponding to the core feature, sorting the messages to be recommended corresponding to each comparison feature according to the distance, and recommending the messages to be recommended to the target user corresponding to each core feature according to a sorting result.
Specifically, the specific implementation method of the processor 10 for the instruction may refer to the description of the relevant steps in the embodiment corresponding to fig. 1, which is not described herein again.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
The present invention also provides a computer-readable storage medium, storing a computer program which, when executed by a processor of an electronic device, may implement:
acquiring information data of a first preset number of fields, and generating an information portrait of each field by using the information data of the first preset number of fields;
acquiring basic data of a second preset number of target users and behavior data of each target user, and generating a user portrait of each target user by using the basic data and the behavior data;
calculating a matching value of each user image and each information image, and collecting information data corresponding to the information images with the matching values larger than a preset threshold value as information to be recommended;
extracting key features of each user portrait as core features, and extracting key features of information to be recommended corresponding to each user portrait as comparison features;
calculating the distance between each core feature and each comparison feature corresponding to the core feature, sorting the messages to be recommended corresponding to each comparison feature according to the distance, and recommending the messages to be recommended to the target user corresponding to each core feature according to a sorting result.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A dual portrait based information recommendation method, the method comprising:
acquiring information data of a first preset number of fields, and generating an information portrait of each field by using the information data of the first preset number of fields;
acquiring basic data of a second preset number of target users and behavior data of each target user, and generating a user portrait of each target user by using the basic data and the behavior data;
calculating a matching value of each user image and each information image, and collecting information data corresponding to the information images with the matching values larger than a preset threshold value as information to be recommended;
extracting key features of each user portrait as core features, and extracting key features of information to be recommended corresponding to each user portrait as comparison features;
calculating the distance between each core feature and each comparison feature corresponding to the core feature, sorting the messages to be recommended corresponding to each comparison feature according to the distance, and recommending the messages to be recommended to the target user corresponding to each core feature according to a sorting result.
2. The method for dual portrait-based information recommendation of claim 1, wherein said generating an information portrait for each domain using information data for said first predetermined number of domains comprises:
sequentially performing core semantic extraction on the information data of each field in the information data of the first preset number of fields to obtain the information semantics of each field;
performing word vector conversion on the information semantics to obtain an information semantics vector;
and constructing an information portrait of the corresponding field by using the information semantic vector.
3. The method for information recommendation based on dual images as claimed in claim 2, wherein said sequentially performing core semantic extraction on the information data of each of the first predetermined number of fields to obtain the information semantic of each of the fields comprises:
performing convolution and pooling on the information data of each field to obtain low-dimensional feature semantics of the information data;
mapping the low-dimensional feature semantics to a pre-constructed high-dimensional space to obtain high-dimensional feature semantics;
and screening the high-dimensional characteristic semantics by using a preset activation function to obtain the information semantics of each field.
4. The dual-portrait-based information recommendation method of claim 2, wherein the constructing the information portrait of the corresponding domain using the information semantic vector comprises:
counting the vector length of each vector in the information semantic vectors, and selecting the vector with the longest vector length as a mode vector;
utilizing preset parameters to extend the length of each residual vector in the information semantic vectors to be the same as the vector length of the module vector;
and splicing each vector in the information semantic vectors with the extended vector length as a row vector into a vector matrix, and taking the vector matrix as an information portrait of a corresponding area.
5. The dual portrait-based information recommendation method of claim 1, wherein said extracting key features of each of said user portraits as core features comprises:
extracting features in each user portrait by utilizing a pre-constructed semantic analysis model;
carrying out vector mapping on the features in each user portrait to obtain a feature vector set;
randomly selecting a preset number of feature vectors from the feature vector set as clustering centers;
sequentially calculating the distance from each feature vector in the feature vector set to the clustering center, and dividing each feature vector into categories corresponding to the clustering center with the minimum distance to obtain a plurality of category clusters;
and recalculating the clustering center of each category cluster, returning to the step of sequentially calculating the distance from each feature vector in the feature vector set to the clustering center until the clustering centers of the plurality of category clusters are converged, and taking the category corresponding to the converged category cluster as the key feature of the user portrait.
6. The method for recommending information based on two pictures as claimed in claim 1, wherein said extracting key features of information to be recommended corresponding to each of said user pictures as comparison features comprises:
performing word segmentation processing on each piece of information to be recommended one by one to obtain information word segmentation corresponding to each piece of information to be recommended;
collecting all the information participles into an information word bank;
selecting each piece of information to be recommended one by one as information to be analyzed, and selecting one information word from information words corresponding to the information to be analyzed as a target word;
counting a first occurrence frequency of the target word in the information word corresponding to the information to be analyzed and a second occurrence frequency of the target word in the information word stock, and calculating a ratio of the second occurrence frequency to the first occurrence frequency;
and selecting the information participles with the ratio larger than a preset ratio threshold value as key features of the information to be analyzed.
7. The method of claim 1, wherein the calculating the distance between each core feature and each aligned feature corresponding to the core feature comprises:
taking each core feature as a clustering center of a corresponding target user, and taking the clustering center as a target label;
sequentially selecting any one comparison characteristic as a comparison label, and performing word segmentation processing on the target label and the comparison label to obtain a target list and a comparison list;
constructing a coding dictionary according to the target list and the comparison list;
vector coding is carried out on the target list and the comparison list by utilizing the coding dictionary to obtain a target vector and a comparison vector;
and calculating the target similarity of the target vector and the comparison vector by using a preset cosine similarity calculation formula, and obtaining the distance between each core feature and each comparison feature corresponding to the core feature according to the target similarity.
8. A dual portrait based information recommendation apparatus, the apparatus comprising:
the information portrait generating module is used for acquiring information data of a first preset number of fields and generating information portrait of each field by using the information data of the first preset number of fields;
the user portrait generation module is used for acquiring basic data of a second preset number of target users and behavior data of each target user and generating a user portrait of each target user by using the basic data and the behavior data;
the information to be recommended generating module is used for calculating a matching value of each user portrait and each information portrait and collecting information data corresponding to the information portraits of which the matching values are greater than a preset threshold value as information to be recommended;
the recommendation information sorting module is used for extracting the key features of each user portrait as core features and extracting the key features of the information to be recommended corresponding to each user portrait as comparison features; calculating the distance between each core feature and each comparison feature corresponding to the core feature, sorting the messages to be recommended corresponding to each comparison feature according to the distance, and recommending the messages to be recommended to the target user corresponding to each core feature according to a sorting result.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor, the instructions being executable by the at least one processor to enable the at least one processor to perform a dual portrait based information recommendation method as recited in any one of claims 1-7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the dual portrait-based information recommendation method of any of claims 1-7.
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