CN114443988A - Information display method and device, electronic equipment and storage medium - Google Patents

Information display method and device, electronic equipment and storage medium Download PDF

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CN114443988A
CN114443988A CN202210113127.9A CN202210113127A CN114443988A CN 114443988 A CN114443988 A CN 114443988A CN 202210113127 A CN202210113127 A CN 202210113127A CN 114443988 A CN114443988 A CN 114443988A
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product
information
user
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常萧颖
王欣
苏畅
李佩刚
高建瓴
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Agricultural Bank of China
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9538—Presentation of query results
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/957—Browsing optimisation, e.g. caching or content distillation
    • G06F16/9574—Browsing optimisation, e.g. caching or content distillation of access to content, e.g. by caching

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Abstract

The invention discloses an information display method, an information display device, electronic equipment and a storage medium, wherein the method comprises the following steps: determining recommendation parameters of the product to be displayed according to the client information, the product information and the historical evaluation information; and displaying the product information for the user corresponding to the user information according to the recommendation parameter. According to the embodiment of the invention, the recommendation parameters of the client to other product information are predicted through the historical evaluation information, and the information is displayed according to the recommendation parameters, so that the accuracy of product information display can be improved, the product interest degree of the user is enhanced, and the use experience of the user can be improved.

Description

Information display method and device, electronic equipment and storage medium
Technical Field
The invention relates to the technical field of computer application, in particular to an information display method, an information display device, electronic equipment and a storage medium.
Background
With the development of internet technology, enterprise services are gradually digitized in an intelligent manner, and product display is changed from original text information display to multimedia display. The user can acquire the product occupation of the multimedia form by means of the terminal equipment or the cloud server, however, due to the diversification of product models and types, the static product display mode often causes that the client cannot really acquire the product which the client is interested in, and the product display effect is poor. How to help customers to quickly acquire interesting products from a large number of products gradually becomes the key point of enterprise product display.
The existing product recommendation is mainly based on subjective promotion of sales personnel, whether the product display is the interesting product of a client or not is mainly limited by the capability of the sales personnel, the accuracy of the product display is insufficient, and the use experience of a user is greatly reduced.
Disclosure of Invention
The invention provides an information display method, an information display device, electronic equipment and a storage medium, which are used for realizing the accuracy of product display, improving the satisfaction degree of a user and enhancing the use experience of the user.
According to an aspect of the present invention, there is provided an information presentation method, wherein the method includes:
determining recommendation parameters of the product to be displayed according to the client information, the product information and the historical evaluation information;
and displaying the product information for the user corresponding to the user information according to the recommendation parameter.
According to another aspect of the present invention, there is provided an information presentation apparatus, wherein the apparatus comprises:
the recommendation parameter module is used for determining recommendation parameters of the products to be displayed according to the client information, the product information and the historical evaluation information;
and the product display module is used for displaying the product information for the user corresponding to the user information according to the recommendation parameter.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the information presentation method of any of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the information presentation method according to any one of the embodiments of the present invention when the computer instructions are executed.
According to the technical scheme of the embodiment of the invention, the recommendation parameter of the product to be displayed is determined by using the customer information, the product information and the historical evaluation information, and the product information is determined to be displayed for the user according to the recommendation parameter, so that the accuracy of product display can be realized, the satisfaction degree of the user is improved, and the use experience of the user can be enhanced.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of an information displaying method according to an embodiment of the present invention;
fig. 2 is a flowchart of an information displaying method according to a second embodiment of the present invention;
fig. 3 is a flowchart of an information displaying method according to a third embodiment of the present invention;
FIG. 4 is a schematic structural diagram of an information display apparatus according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device implementing the information presentation method according to the embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solutions of the present invention, 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 only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
Fig. 1 is a flowchart of an information displaying method according to an embodiment of the present invention, where the present embodiment is applicable to a case where product information is displayed for a user, and the method may be executed by an information displaying apparatus, where the information displaying apparatus may be implemented in a form of hardware and/or software, and the apparatus may be configured in a server or a mobile terminal. As shown in fig. 1, the method includes:
and step 110, determining recommendation parameters of the product to be displayed according to the customer information, the product information and the historical evaluation information.
The customer information may be related attribute information of the customer, and may include information such as age of the customer, product use habits, and the like, the product information may include related introduction information of the product, and may include type of the product, description of the product, and the like, the historical evaluation information may be descriptive evaluation information of the used product by different customers, and the historical evaluation information may be represented in a satisfaction degree or star level manner. The product to be displayed may be a product currently being sold and may include a product that has been used by a customer or a product that has not been used by a customer. The recommendation parameter may be indication information for determining whether the product is recommended, and the recommendation parameter may be implemented by presetting the evaluation of the product by the client.
In the embodiment of the present invention, the recommendation parameter of each product to be displayed may be determined based on the customer information, the product information, and the historical evaluation information, and the generation process of the recommendation parameter may include using a machine learning algorithm or an expert system to process.
And 120, displaying product information for the user corresponding to the user information according to the recommended parameters.
In the embodiment of the invention, the corresponding recommended parameters can be obtained according to the user information, and the product information of one or more commodities can be obtained according to the value of the recommended parameters for displaying, so that the user can conveniently obtain interested products.
According to the embodiment of the invention, the recommendation parameters of the product to be displayed are determined by using the customer information, the product information and the historical evaluation information, and the product information is determined to be displayed for the user according to the recommendation parameters, so that the accuracy of product display can be realized, the satisfaction degree of the user is improved, and the use experience of the user can be enhanced.
Example two
Fig. 2 is a flowchart of an information displaying method according to a second embodiment of the present invention, which is embodied on the basis of the second embodiment of the present invention, and referring to fig. 2, the method according to the second embodiment of the present invention includes the following steps:
and 210, generating an original evaluation matrix corresponding to the historical evaluation information, and extracting the user implicit characteristics and the product implicit characteristics included in the original evaluation matrix.
The original evaluation matrix may include a matrix of historical evaluation information, each element in the original evaluation matrix may correspond to a historical evaluation value of a customer on a commodity, and it may be understood that, when the customer does not evaluate the commodity, an element at a corresponding position in the original evaluation matrix may be null. The user implicit characteristic and the product implicit characteristic can be characteristic information implicit in original evaluation information, the characteristic information can be obtained by extracting an original evaluation matrix, and it can be understood that the user implicit characteristic and the product implicit characteristic can be extracted by matrix decomposition or machine learning processing of the original evaluation matrix.
Specifically, the historical evaluation information may be processed to fill the evaluation value of each customer for each product into the corresponding position in the original evaluation matrix, and it is understood that the row coordinates of each element in the original evaluation matrix may represent the customer and the column coordinates may represent the product. After the original evaluation matrix is generated, matrix decomposition or machine learning processing can be performed on the original matrix, so that the user implicit characteristic for the user and the product implicit characteristic for the product can be extracted from the original evaluation matrix.
Step 220, quantifying the customer information and the product information to extract the user dominant features and the product dominant features.
In the embodiment of the invention, the client information and the product information can be quantized, the type parameters in the client information and the product information can be converted into the numerical parameters, and the numerical parameter set corresponding to the client information and the numerical parameter set corresponding to the product information can be respectively used as the user dominant characteristic and the product dominant characteristic.
And step 230, adjusting the user implicit characteristic and the product implicit characteristic according to the user explicit characteristic, the product explicit characteristic and a preset adjusting formula.
Specifically, after the user dominant characteristic and the product dominant characteristic are obtained, the user dominant characteristic and the product dominant characteristic can be adjusted through a preset adjusting formula, so that the user recessive characteristic and the product recessive characteristic can be reflected more accurately, the preset adjusting formula can be determined and preset at a server or an intelligent terminal according to the actual product condition, and the preset adjusting formula can comprise a weighted average value formula or a linear regression formula used by a gradient descent method and the like.
And step 240, constructing a recommendation score matrix based on the adjusted user implicit characteristics and the adjusted product implicit characteristics to serve as recommendation parameters of the product to be displayed.
The recommendation scoring matrix can be a matrix comprising the predictive evaluation values of all customers for all products, and can be constructed by the implicit characteristics of users and the implicit characteristics of products.
In the embodiment of the invention, the predictive evaluation value of each user for each product can be determined according to the adjusted user implicit characteristic and the product implicit characteristic, the predictive evaluation value can be a product of values of the user implicit characteristic and the product implicit characteristic, or a weighted average value of the user implicit characteristic and the product implicit characteristic, each predictive evaluation value can be stored in a matrix to form a recommendation score matrix, the user implicit characteristic and the product implicit characteristic can be directly processed into the recommendation score matrix by using a trained machine learning model, and the recommendation score matrix can be used for searching recommendation parameters of products to be displayed.
And step 250, sequencing the product information according to the values of the recommended parameters.
Specifically, the recommended parameters may be arranged into a recommended parameter sequence with a value from large to small, and then the corresponding product information may be ordered according to the recommended parameter sequence.
And step 260, extracting at least one product information in the sequencing for visual display.
In the embodiment of the invention, after the product information is sequenced, the plurality of product information are sequentially acquired in the formed sequence, and one or more acquired product information can be visually displayed, so that a user can conveniently know the product.
According to the embodiment of the invention, the original evaluation matrix corresponding to the historical evaluation information is generated, the user implicit characteristic and the product implicit characteristic are extracted from the original evaluation matrix, the user explicit characteristic and the product explicit characteristic corresponding to the client information and the product information are generated, the user explicit characteristic, the product explicit characteristic and a preset adjusting formula are used for adjusting the user implicit characteristic and the product implicit characteristic, the recommendation scoring matrix corresponding to the user implicit characteristic and the product implicit characteristic is generated to serve as the recommendation parameter, the corresponding product information is sequenced according to the recommendation parameter, a plurality of product information are obtained in the sequencing for displaying, the interest degree of the user in the displayed product can be improved, the satisfaction degree of the user can be improved, and the use experience of the user can be enhanced.
EXAMPLE III
Fig. 3 is a flowchart of an information displaying method according to a third embodiment of the present invention, which is embodied on the basis of the third embodiment of the present invention, and referring to fig. 3, the method according to the third embodiment of the present invention specifically includes the following steps:
and step 310, extracting evaluation values of different history clients in the history evaluation information.
The evaluation value may be information of the product evaluated by the user, the evaluation value may be a numerical value or a grade value, and the history customer may be past use or used goods evaluated.
In the embodiment of the present invention, the historical evaluation information may be extracted, the extracting may include extracting evaluation records of different clients from a server log or acquiring evaluation information of different clients from a public web page, and the like, and the acquired evaluation records or evaluation information may generate corresponding evaluation values, for example, different evaluation values may be assigned to different comments, and the corresponding evaluation values may be acquired by matching comments in the evaluation information or the evaluation records.
And step 320, establishing an original evaluation matrix for the evaluation value by taking historical customers and products as dimensions.
Specifically, a history client and a product corresponding to each evaluation value may be extracted, a coordinate position corresponding to the history client and the product may be found in the original evaluation matrix, the evaluation value may be stored in the coordinate position, and for example, the identification numbers of the history client and the product may be set as horizontal and vertical coordinates of the original evaluation matrix, respectively.
And step 330, performing matrix decomposition on the original evaluation matrix to generate a user implicit characteristic matrix corresponding to the user implicit characteristic and a product implicit characteristic matrix corresponding to the product implicit characteristic.
The matrix decomposition may be a form of dividing the matrix into a plurality of matrix products, and may be implemented by triangular decomposition, full rank decomposition, QR decomposition, singular value decomposition, or the like.
In the embodiment of the invention, the original evaluation matrix can be subjected to matrix decomposition to obtain a form of multiplying the user implicit characteristic matrix and the product implicit characteristic matrix, and the user implicit characteristic matrix can be used as the user implicit characteristic and the product implicit characteristic matrix can be used as the product implicit characteristic.
And step 340, extracting the client information of the target client and the product information of the product to be recommended.
The client that the target client needs to display the product information may include a user using a server or a mobile terminal, and the product to be recommended may be a product currently being sold.
In the embodiment of the present invention, the server or the mobile terminal may search for the client information of the target client, where the client information may include registration time, frequent login address, product usage habit, and the like of the target client, and search for the product information of the product to be recommended in the server or the mobile terminal, and it may be understood that the product to be recommended may be a product used by the target client or an unused product.
And step 350, quantizing different dimensions of the customer information and the product information to generate a user dominant feature vector and a product dominant feature vector.
In the embodiment of the invention, the client information and the product information can be quantized by using different quantization standards in different dimensions, and the type information or the character information in the client information and the product information can be converted into numerical information, so that the client information is converted into the user dominant eigenvector and the product information is converted into the product dominant eigenvector.
Step 360, substituting the user dominant feature vector, the product dominant feature vector, the user recessive feature vector and the product recessive feature vector into a first preset adjustment formula, wherein the first preset adjustment formula is as follows: r isij＝(ui·vj)+α·pi+β·qjWherein, u isiMatrix representing implicit characteristics of a user, vjRepresenting a product implicit characteristic matrix, piRepresenting the dominant eigenvector, q, of the userjRepresenting the product dominant feature vector.
In the embodiment of the invention, the user dominant eigenvector, the product dominant eigenvector, the user dominant eigenvector and the product dominant eigenvector can be substituted into rij＝(ui·vj)+α·pi+β·qj。
Step 370, performing linear regression on the first preset adjustment formula to determine values of α and β, where the loss function used in the linear regression is (r)ij-(ui·vj)+α·pi+β·qj)2。
The Linear Regression (LR) may be a statistical analysis method that determines a quantitative relationship in which two variables are dependent on each other by using mathematical statistics and Regression analysis, and may perform linear Regression processing on the first preset adjustment formula to obtain a value of the coefficient α sum when the loss function is minimum.
In the embodiment of the invention, r can be obtained by pairingij＝(ui·vj)+α·pi+β·qjLR linear regression was performed such that the loss function was (r)ij-(ui·vj)+α·pi+β·qj)2The value of (2) is minimum, the process of LR linear regression can be repeated for many times, the minimum value of the loss function can be obtained by adopting a gradient descent method, and the values of the coefficients alpha and beta under the condition of the minimum value.
380, determining a grading matrix containing the user implicit feature matrix and the product implicit feature matrix according to alpha and beta through a second preset adjusting formula, wherein the second preset formula isThe adjustment formula includes: r isij’＝rij-(ui·vj)+α·pi+β·qjWherein u isiMatrix representing implicit characteristics of a user, vjRepresenting a product implicit characteristic matrix, piRepresenting the dominant eigenvector, q, of the userjRepresenting a user implicit feature matrix.
In the embodiment of the present invention, α and β may be substituted into the second preset adjustment formula rij’＝rij-(ui·vj)+α·pi+β·qjThereby obtaining a scoring matrix, namely ui·vjThe product of (a) and (b).
And 390, performing matrix decomposition on the scoring matrix, and then using the scoring matrix as a new user implicit feature matrix and a new product implicit feature matrix again, and repeating the process until the user implicit feature matrix and the product implicit feature matrix meet preset stop conditions.
Specifically, matrix decomposition can be continuously carried out on the scoring matrix, the matrix obtained through decomposition can be respectively used as a new user recessive feature matrix and a new product recessive feature matrix, the user recessive feature matrix and the product recessive feature matrix can be substituted into the step 360, the steps 360-390 are repeatedly executed until the adjustment process meets the preset stop condition, and the accuracy of the user recessive feature matrix and the accuracy of the product recessive feature matrix can be improved. The preset stop condition may include evaluation criteria for the user implicit characteristic matrix and the product implicit characteristic matrix or a maximum running time.
And 3100, taking the product of the user implicit characteristic matrix and the product implicit characteristic matrix as a recommendation scoring matrix.
In the embodiment of the invention, the product of the user implicit characteristic matrix and the product implicit characteristic matrix can be calculated, and the product can be used as a recommendation score matrix.
Step 3110, extracting recommendation parameters of the user information corresponding to each product to be displayed in the recommendation score matrix.
Specifically, a certain row of elements or a certain column of elements may be corresponding to the recommendation score matrix according to the user information of the target customer, and the recommendation parameters of each currently-sold product to be displayed may be found in the row of elements or the column of elements.
And 3120, displaying product information for the user corresponding to the user information according to the recommended parameters.
The embodiment of the invention extracts evaluation values of different historical clients from historical evaluation information, stores the evaluation values as an original evaluation matrix, carries out matrix decomposition on the original evaluation matrix to obtain a user implicit characteristic matrix and a product implicit characteristic matrix, quantizes the extracted client information and product information, forms a user linear characteristic vector and a product linear characteristic vector from quantization results, and iteratively realizes the process of substituting the user explicit characteristic vector, the product explicit characteristic vector, the user implicit characteristic vector and the product implicit characteristic vector into a first preset adjusting formula, carrying out linear regression on the first preset adjusting formula to determine coefficients alpha and beta, substituting the alpha and beta into a second preset adjusting formula to determine a new user implicit characteristic matrix and a new product implicit characteristic matrix, thereby determining the adjusted user implicit characteristic matrix and the product implicit characteristic matrix, the product of the user implicit characteristic matrix and the product implicit characteristic matrix is used as a recommendation scoring matrix, recommendation parameters of products to be displayed corresponding to the user information are extracted from the recommendation scoring matrix, the product information is displayed for the user according to the recommendation parameters, accurate display of the product information is achieved, the interest degree of the user in the products can be improved, and the user experience can be enhanced.
In an exemplary embodiment, taking the information presentation of financial products as an example, past satisfaction scores of old customers are first collected, which may range from 1 to 5, where 1 represents dissatisfaction, 5 represents very slow, and higher scores represent greater satisfaction. The collected satisfaction scores may be formed into a raw score matrix R, such as:
Figure BDA0003495390420000101
when targeted information presentation is performed for a client, the information presentation process may include the following steps:
the method comprises the following steps: solving the implicit characteristic vector u of the old customer by using MF matrix decomposition on the original scoring matrixiAnd implicit characteristic vector v of financial products on salej；
Step two: quantifying old customer explicit characteristics and on-sale financial product explicit characteristics into a vector piSum vector qjTaking the performance of the old customer as an example to show the quantization process, since the gender can be divided into two types, namely male and female, two feature vectors can be used to identify the gender of the old customer, for example, the male is quantized to [1,0 ]]Quantification of women as [0,1 ]]The remaining explicit features can be represented quantitatively to construct a client explicit feature vector piAnd product explicit feature vector qj。
Step three: utilizing the implicit feature vector u of the old client obtained in the step oneiAnd implicit characteristic vector v of financial products on salejAnd the client explicit characteristic vector p obtained in the second stepiAnd product explicit feature vector qjAccording to the formula rij＝(ui·vj)+α·pi+β·qjWherein u isiMatrix representing implicit characteristics of a user, vjRepresenting a product implicit characteristic matrix, piRepresenting the dominant eigenvector, q, of the userjRepresenting the user implicit feature matrix such that the residual δ is (r)ij-(ui·vj)+α·pi+β·qj)2And finally, determining coefficients alpha and beta by using an LR linear regression method.
Step four: using the coefficients α and β determined in step three, in accordance with rij’＝rij-(ui·vj)+α·pi+β·qjDetermining a scoring matrix R;
step five: using the step four rijForming a new scoring matrix R, and then performing matrix decomposition on the R to determine a new implicit characteristic matrix U, V;
step six: circularly performing the fourth step and the fifth step until the scoring matrix R is converged;
step seven: after convergence, multiplying the finally obtained U and V to obtain a final recommendation scoring matrix
Figure BDA0003495390420000111
A recommendation scoring matrix may be determined
Figure BDA0003495390420000112
Root mean square error to assess the accuracy of the scoring;
step eight: recommendation scoring matrix
Figure BDA0003495390420000113
The satisfaction degree of the financial product which is not purchased or known by the customer is determined, and the detailed information of the financial product can be displayed to the customer based on the satisfaction degree so as to assist the user in purchasing the financial product.
Example four
Fig. 4 is a schematic structural diagram of an information display apparatus according to a fourth embodiment of the present invention. As shown in fig. 4, the apparatus includes: a recommendation parameter module 401 and a product display module 402.
And the recommendation parameter module 401 is configured to determine recommendation parameters of the product to be displayed according to the customer information, the product information, and the historical evaluation information.
A product display module 402, configured to display the product information for the user corresponding to the user information according to the recommendation parameter.
According to the embodiment of the invention, the recommendation parameter of the product to be displayed is determined by the recommendation parameter module by using the client information, the product information and the historical evaluation information, and the product display module determines to display the product information for the user according to the recommendation parameter, so that the accuracy of product display can be realized, the satisfaction degree of the user is improved, and the use experience of the user can be enhanced.
Further, on the basis of the above embodiment of the present invention, the recommended parameter module 401 includes:
and the implicit characteristic unit is used for generating an original evaluation matrix corresponding to the historical evaluation information and extracting the user implicit characteristics and the product implicit characteristics included in the original evaluation matrix.
And the explicit characteristic unit is used for quantizing the client information and the product information to extract user explicit characteristics and product explicit characteristics.
And the characteristic adjusting unit is used for adjusting the user implicit characteristic and the product implicit characteristic according to the user explicit characteristic, the product explicit characteristic and a preset adjusting formula.
And the parameter determining unit is used for constructing a recommendation scoring matrix based on the adjusted user implicit characteristic and the product implicit characteristic as recommendation parameters of the product to be displayed.
Further, on the basis of the above embodiment of the present invention, the implicit characteristic unit is specifically configured to: extracting evaluation values of different history clients in the history evaluation information; establishing an original evaluation matrix for the evaluation value by taking historical customers and products as dimensions; and performing matrix decomposition on the original evaluation matrix to generate a user implicit characteristic matrix corresponding to the user implicit characteristic and a product implicit characteristic matrix corresponding to the product implicit characteristic.
Further, on the basis of the above embodiment of the present invention, the explicit feature unit is specifically configured to: extracting the customer information of a target customer and the product information of a product to be recommended; different dimensions of the customer information and the product information are quantified to generate a user dominant feature vector and a product dominant feature vector.
Further, on the basis of the above embodiment of the present invention, the characteristic adjusting unit is specifically configured to: substituting the user dominant eigenvector, the product dominant eigenvector, the user dominant eigenvector and the product dominant eigenvector into a first preset adjustment formula, wherein the first preset adjustment formula is as follows: r isij＝(ui·vj)+α·pi+β·qjWherein u isiMatrix representing implicit characteristics of a user, vjRepresenting a product implicit characteristic matrix, piRepresenting the dominant eigenvector, q, of the userjRepresenting a user implicit characteristic matrix; performing linear regression on the first preset adjustment formula to determine values of alpha and beta, wherein the linear regression uses a loss function of (r)ij-(ui·vj)+α·pi+β·qj)2(ii) a Determining a scoring matrix containing the user implicit feature matrix and the product implicit feature matrix by using a second preset adjusting formula according to the alpha and the beta, wherein the second preset adjusting formula comprises: r isij’＝rij-(ui·vj)+α·pi+β·qjWherein u isiMatrix representing implicit characteristics of a user, vjRepresenting a product implicit characteristic matrix, piRepresenting the dominant eigenvector, q, of the userjRepresenting a user implicit characteristic matrix; and performing matrix decomposition on the scoring matrix, and then taking the scoring matrix as the new user implicit characteristic matrix and the new product implicit characteristic matrix again, and repeating the processes until the user implicit characteristic matrix and the product implicit characteristic matrix meet preset stop conditions.
Further, on the basis of the above embodiment of the present invention, the parameter determining unit is specifically configured to: taking the product of the user implicit characteristic matrix and the product implicit characteristic matrix as the recommendation scoring matrix; and extracting the recommendation parameters of the products to be displayed corresponding to the user information in the recommendation scoring matrix.
Further, on the basis of the above-mentioned embodiment of the invention, the product display module 402 includes:
and the sorting unit sorts according to the values of the recommended parameters from large to small.
And the display unit is used for extracting at least one product information in the sorting to carry out visual display.
The information display device provided by the embodiment of the invention can execute the information display method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
EXAMPLE five
Fig. 5 is a schematic structural diagram of an electronic device implementing the information presentation method according to the embodiment of the present invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM)12, a Random Access Memory (RAM)13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM)12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
Processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The processor 11 performs the various methods and processes described above, such as the information presentation method.
In some embodiments, the information presentation method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the information presentation method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the information presentation method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above, reordering, adding or deleting steps, may be used. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. An information presentation method, the method comprising:
determining recommendation parameters of the product to be displayed according to the client information, the product information and the historical evaluation information;
and displaying the product information for the user corresponding to the user information according to the recommendation parameters.
2. The method of claim 1, wherein determining recommended parameters for the product to be displayed based on the customer information, the product information, and the historical rating information comprises:
generating an original evaluation matrix corresponding to the historical evaluation information, and extracting the user implicit characteristics and the product implicit characteristics included in the original evaluation matrix;
quantifying the customer information and the product information to extract user dominant features and product dominant features;
adjusting the user implicit characteristic and the product implicit characteristic according to the user explicit characteristic, the product explicit characteristic and a preset adjusting formula;
and constructing a recommendation scoring matrix based on the adjusted user implicit characteristics and the product implicit characteristics as recommendation parameters of the product to be displayed.
3. The method according to claim 2, wherein the generating an original evaluation matrix corresponding to the historical evaluation information and extracting the user implicit features and the product implicit features included in the original evaluation matrix comprises:
extracting evaluation values of different history clients in the history evaluation information;
establishing an original evaluation matrix for the evaluation value by taking historical customers and products as dimensions;
and performing matrix decomposition on the original evaluation matrix to generate a user implicit characteristic matrix corresponding to the user implicit characteristic and a product implicit characteristic matrix corresponding to the product implicit characteristic.
4. The method of claim 2, wherein the quantifying the customer information and the product information to extract user dominant features and product dominant features comprises:
extracting the customer information of a target customer and the product information of a product to be recommended;
different dimensions of the customer information and the product information are quantified to generate a user dominant feature vector and a product dominant feature vector.
5. The method of claim 3 or 4, wherein the adjusting the user implicit characteristic and the product implicit characteristic according to the user explicit characteristic, the product explicit characteristic and a preset adjustment formula comprises:
substituting the user dominant feature vector, the product dominant feature vector, the user recessive feature vector and the product recessive feature vector into a first preset adjustment formula, wherein the first preset adjustment formula is as follows: r isij＝(ui·vj)+α·pi+β·qjWherein u isiMatrix representing implicit characteristics of a user, vjRepresenting a product implicit characteristic matrix, piRepresenting the dominant eigenvector, q, of the userjRepresenting a product dominant feature vector;
performing linear regression on the first preset adjustment formula to determine values of alpha and beta, wherein the linear regression uses a loss function of (r)ij-(ui·vj)+α·pi+β·qj)2；
Determining a scoring matrix containing the user implicit feature matrix and the product implicit feature matrix by using a second preset adjusting formula according to the alpha and the beta, wherein the second preset adjusting formula comprises: r isij’＝rij-(ui·vj)+α·pi+β·qj；
And after matrix decomposition is carried out on the scoring matrix, the scoring matrix is used as the new user implicit characteristic matrix and the product implicit characteristic matrix again, and the process is repeated until the user implicit characteristic matrix and the product implicit characteristic matrix meet preset stop conditions.
6. The method according to claim 5, wherein the constructing a recommendation score matrix based on the adjusted user implicit characteristic and the product implicit characteristic as a recommendation parameter of the product to be displayed comprises:
taking the product of the user implicit characteristic matrix and the product implicit characteristic matrix as the recommendation scoring matrix;
and extracting the recommendation parameters of the products to be displayed corresponding to the user information in the recommendation scoring matrix.
7. The method of claim 1, wherein the displaying the product information for the user corresponding to the user information according to the recommended parameter comprises:
sorting the product information according to the value of the recommended parameter;
and extracting at least one product information in the sorting for visual display.
8. An information presentation device, the device comprising:
the recommendation parameter module is used for determining recommendation parameters of the products to be displayed according to the client information, the product information and the historical evaluation information;
and the product display module is used for displaying the product information for the user corresponding to the user information according to the recommendation parameter.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the information presentation method of any one of claims 1-7.
10. A computer-readable storage medium storing computer instructions for causing a processor to implement the information presentation method of any one of claims 1-7 when executed.
CN202210113127.9A 2022-01-29 2022-01-29 Information display method and device, electronic equipment and storage medium Pending CN114443988A (en)

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Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210113127.9A CN114443988A (en) 2022-01-29 2022-01-29 Information display method and device, electronic equipment and storage medium

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CN114443988A true CN114443988A (en) 2022-05-06

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