CN116307394A - Product user experience scoring method, device, medium and equipment - Google Patents
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Abstract
The embodiment of the application provides a product user experience scoring method, device, medium and equipment, wherein the method comprises the following steps: acquiring a user demand sheet; extracting keywords in the user demand sheet, and classifying and dividing the user demand sheet according to the keyword mapping and the corresponding product management and control department; the scoring dimension module is used for sending the classified and divided user demand list to a corresponding product management and control department so as to enable the product management and control department to judge the user demand list; counting the user demand single cumulative number corresponding to each grading dimension module, and updating the weight value of each grading dimension module in the product grading model according to the user demand single cumulative number to obtain an updated product grading model; and displaying the user score of the target product through the updated product score model. The embodiment of the application can embody the attention degree of a user on each grading dimension module, and is convenient for developers to improve the problems existing in the product in a targeted manner.
Description
Technical Field
The present disclosure relates to the field of electronic communications technologies, and in particular, to a method, an apparatus, a medium, and a device for scoring product user experience.
Background
The product user experience measurement model is a tool capable of measuring the user experience degree of a product (such as APP), and specifically, a specific gravity value is allocated to each scoring dimension module in the product user experience measurement model, and then the scores given by the user to each dimension of the product are input into the product user experience measurement model to be weighted and summed to obtain a comprehensive score for evaluating the performance of the product.
However, the specific gravity value allocated by each scoring dimension module in the traditional product user experience measurement model is fixed, the existing subjective factors are larger, the fact that the attention points of users to the problems of products are always changed is not considered, the attention degree of the problem A about the products is likely to suddenly become larger in a certain period of time, but the attention degree of the users of a certain scoring dimension module cannot be embodied because the traditional model only focuses on the comprehensive scores, and the problems of the products are difficult to improve in a targeted manner.
Disclosure of Invention
The embodiment of the application provides a product user experience scoring method, a device, a medium and equipment, by utilizing the product user experience scoring method provided by the embodiment of the application, text content in the request list is identified by acquiring the request list uploaded by a client, keywords in the text content are extracted, the request list is classified based on the information of the affiliated departments of keyword mapping, the classified request list is distributed to the scoring dimension modules of the affiliated departments to which the request list belongs further, statistical events of the scoring dimension modules of the request list belong are accumulated, the occupation ratio of each scoring dimension module in the scoring model is updated, the user score of a target product is displayed through the updated product scoring model, the attention degree of the specific scoring dimension modules to the user can be embodied, and developers can purposefully improve the problem existing in the product.
In one aspect, a product user experience scoring method is provided, where the product user experience scoring method is obtained based on a product scoring model, and the product scoring model includes a plurality of scoring dimension modules, and the method includes:
acquiring a user demand form aiming at a target product;
extracting keywords in the user demand form, and classifying and dividing the user demand form according to the keyword mapping and the corresponding product management and control department;
the classified and divided user demand sheets are sent to corresponding product management and control departments, so that the product management and control departments can judge the grading dimension modules of the user demand sheets;
counting the user demand single cumulative number corresponding to each grading dimension module in a preset time period, and updating the weight value of each grading dimension module in a product grading model according to the user demand single cumulative number to obtain an updated product grading model;
and displaying the user score of the target product through the updated product score model.
In the product user experience scoring method according to the embodiment of the present application, before the extracting the keywords in the user requirement list, the method further includes:
And inputting the user demand form into a trained text recognition model to perform text recognition operation, so as to obtain text information in the user demand form.
In the product user experience scoring method according to the embodiment of the present application, the extracting keywords in the user requirement list includes:
word segmentation preprocessing is carried out on the text information in the user demand list so as to obtain a plurality of word segmentation results of the text information;
respectively converting words in the word segmentation results into word vectors, and dividing all word vectors of the text information into class clusters with preset quantity according to a clustering algorithm;
adding all word vectors contained in any type of cluster in the text information to be used as the type cluster vector of the any type of cluster;
inputting the cluster vector of any cluster into a preset keyword extraction model to obtain the word vector of the standard keyword of any cluster; the keyword extraction model is generated by taking a class cluster vector of each class cluster of each standard text in the first text corpus as input and taking a word vector of a standard keyword of a corresponding class cluster as output to train the deep neural network model;
Respectively calculating the similarity between each word vector of any cluster and the word vector of the standard keyword of the any cluster, and determining the word corresponding to the word vector with the highest similarity in all word vectors of the any cluster as the keyword of the any cluster;
and extracting the keywords of the text information according to the keywords of each type of cluster of the text information.
In the product user experience scoring method of the embodiment of the present application, the calculating the similarity between each word vector of the any type of cluster and the word vector of the standard keyword of the any type of cluster, and determining the word corresponding to the word vector with the highest similarity in all word vectors of the any type of cluster as the keyword of the any type of cluster includes:
respectively calculating the distance between each word vector of any cluster and the word vector of the standard keyword of the any cluster;
wherein the distance is a cosine distance or a Euclidean distance;
and determining the word corresponding to the word vector with the minimum distance between the word vector of the standard keyword of any cluster in all word vectors of any cluster as the keyword of any cluster.
In the product user experience scoring method of the embodiment of the present application, the word segmentation preprocessing is performed on the text information to obtain a plurality of word segmentation results of the text information, including:
word segmentation is carried out on the text information according to a preset dictionary so as to obtain a plurality of preliminary word segmentation results;
and removing stop words from the plurality of preliminary word segmentation results according to a preset stop word list so as to obtain a plurality of word segmentation results of the text information.
In the product user experience scoring method disclosed by the embodiment of the application, the weight value of the scoring dimension module is in a proportional incremental relation with the single accumulated number of the user demand.
In the product user experience scoring method of the embodiment of the present application, the displaying, by the updated product scoring model, the user score of the target product includes:
respectively obtaining the score of each scoring dimension of the target product;
and inputting the scores of the scoring dimensionalities into the updated product scoring model, and carrying out weighted summation on the scores of the scoring dimensionalities according to the weight values of the scoring dimensionalities in the updated product scoring model to obtain the product user experience score of the target product.
Correspondingly, the embodiment of the application further provides a product user experience scoring device, the product user experience scoring device is used for a product scoring model, the product scoring model comprises a plurality of scoring dimension modules, and the product user experience scoring device comprises:
the acquisition module is used for acquiring a user demand sheet aiming at a target product;
the extraction module is used for extracting keywords in the user demand sheet and classifying and dividing the user demand sheet according to the keyword mapping and the corresponding product management and control department;
the sending module is used for sending the classified and divided user demand sheets to corresponding product management and control departments so that the product management and control departments can judge the grading dimension module of the user demand sheets;
the statistics module is used for counting the user demand single accumulated number corresponding to each grading dimension module in a preset time period, and updating the weight value of each grading dimension module in the product grading model according to the user demand single accumulated number to obtain an updated product grading model;
and the scoring module is used for displaying the user scores of the target products through the updated product scoring model.
Accordingly, another aspect of the embodiments of the present application further provides a storage medium storing a plurality of instructions adapted to be loaded by a processor to perform the product user experience scoring method as described above.
Accordingly, in another aspect, the embodiment of the present application further provides a terminal device, including a processor and a memory, where the memory stores a plurality of instructions, and the processor loads the instructions to perform the product user experience scoring method as described above.
The embodiment of the application provides a product user experience scoring method, device, medium and equipment, wherein the method comprises the steps of obtaining a user demand sheet aiming at a target product; extracting keywords in the user demand form, and classifying and dividing the user demand form according to the keyword mapping and the corresponding product management and control department; the classified and divided user demand sheets are sent to corresponding product management and control departments, so that the product management and control departments can judge the grading dimension modules of the user demand sheets; counting the user demand single cumulative number corresponding to each grading dimension module in a preset time period, and updating the weight value of each grading dimension module in a product grading model according to the user demand single cumulative number to obtain an updated product grading model; and displaying the user score of the target product through the updated product score model. By using the product user experience scoring method provided by the embodiment of the application, the demand list uploaded by the customer is acquired, the text content in the demand list is identified, then the keywords in the text content are extracted, the demand list is classified based on the information of the affiliated departments of the keyword mapping, the classified demand list is distributed to the scoring dimension modules which the departments respectively affiliated further judge the affiliated of the demand list, statistical events of the scoring dimension modules which the demand list affiliated of are accumulated, the occupation ratio of each scoring dimension module in the scoring model is updated, the user score of the target product is displayed through the updated product scoring model, the attention degree of the specific scoring dimension modules to the user can be reflected, and developers can purposefully improve the problems existing in the product.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained from these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a product user experience scoring method according to an embodiment of the present application.
Fig. 2 is a diagram showing a user scoring effect in the product user experience scoring method provided in the embodiment of the present application.
Fig. 3 is a schematic structural diagram of a product user experience scoring device according to an embodiment of the present application.
Fig. 4 is another schematic structural diagram of a product user experience scoring device according to an embodiment of the present application.
Fig. 5 is a schematic structural diagram of a terminal device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by a person skilled in the art without any inventive effort, are intended to be within the scope of the present application based on the embodiments herein.
It should be noted that the following is a simple description of the background of the present solution:
the technical problem is developed that the problem that the degree of concern of a problem A is likely to suddenly become large in a certain period of time is likely to exist because the traditional model only focuses on the comprehensive score, the degree of concern of a user of a specific grading dimension module cannot be reflected, and the problem existing in the product is difficult to improve in a targeted manner is mainly solved. It can be appreciated that the product user experience metric model is a tool capable of measuring the user experience degree of a product (such as APP), specifically, a specific gravity value is allocated to each scoring dimension module in the product user experience metric model, and then the scores given by the user to each dimension of the product are input into the product user experience metric model to be weighted and summed to obtain a comprehensive score for evaluating the performance of the product.
However, the specific gravity value allocated by each scoring dimension module in the traditional product user experience measurement model is fixed, the existing subjective factors are larger, the fact that the attention points of users to the problems of products are always changed is not considered, the attention degree of the problem A about the products is likely to suddenly become larger in a certain period of time, but the attention degree of the users of a certain scoring dimension module cannot be embodied because the traditional model only focuses on the comprehensive scores, and the problems of the products are difficult to improve in a targeted manner.
In order to solve the technical problems, the embodiment of the application provides a product user experience scoring method. By using the product user experience scoring method provided by the embodiment of the application, the demand list uploaded by the customer is acquired, the text content in the demand list is identified, then the keywords in the text content are extracted, the demand list is classified based on the information of the affiliated departments of the keyword mapping, the classified demand list is distributed to the scoring dimension modules which the departments respectively affiliated further judge the affiliated of the demand list, statistical events of the scoring dimension modules which the demand list affiliated of are accumulated, the occupation ratio of each scoring dimension module in the scoring model is updated, the user score of the target product is displayed through the updated product scoring model, the attention degree of the specific scoring dimension modules to the user can be reflected, and developers can purposefully improve the problems existing in the product.
Referring to fig. 1, fig. 1 is a flowchart of a product user experience scoring method according to an embodiment of the present application. The product user experience scoring method is applied to terminal equipment. Optionally, the terminal device is a terminal or a server. Optionally, the server is an independent physical server, or a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs (Content Delivery Network, content delivery networks), basic cloud computing services such as big data and artificial intelligence platforms, and the like. Optionally, the terminal is a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, a smart voice interaction device, a smart home appliance, a vehicle-mounted terminal, and the like, but is not limited thereto.
In an embodiment, the method may comprise the steps of:
It should be noted that the product mentioned in this embodiment may be a physical product or a virtual product, for example, an APP. The user demand bill records that the user puts forward the place needing improvement aiming at the problem of the product in the process of using the product, for example, when the user finds that the function of automatically identifying the Chinese information in the picture is lacking in the process of using a certain chat software, the user demand bill can be sent to the developer of the product.
And 102, extracting keywords in the user demand sheet, and classifying and dividing the user demand sheet according to the keyword mapping and the corresponding product management and control department.
In this embodiment, the product management and control department that can process the problem corresponding to the user demand sheet can be quickly determined by extracting the keywords in the user demand sheet, so that the problem can be effectively solved.
It should be noted that, in order to extract the keywords in the user demand sheet, the text information in the user demand sheet needs to be extracted first. Specifically, the user demand bill can be input into a trained text recognition model to perform text recognition operation, so that text information in the user demand bill is obtained.
After obtaining the text information in the user demand bill, keywords in the text information can be extracted by the following ways:
word segmentation preprocessing is carried out on the text information in the user demand list so as to obtain a plurality of word segmentation results of the text information;
respectively converting words in the word segmentation results into word vectors, and dividing all word vectors of the text information into class clusters with preset quantity according to a clustering algorithm;
adding all word vectors contained in any type of cluster in the text information to be used as the type cluster vector of the any type of cluster;
Inputting the cluster vector of any cluster into a preset keyword extraction model to obtain the word vector of the standard keyword of any cluster; the keyword extraction model is generated by taking a class cluster vector of each class cluster of each standard text in the first text corpus as input and taking a word vector of a standard keyword of a corresponding class cluster as output to train the deep neural network model;
respectively calculating the similarity between each word vector of any cluster and the word vector of the standard keyword of the any cluster, and determining the word corresponding to the word vector with the highest similarity in all word vectors of the any cluster as the keyword of the any cluster;
and extracting the keywords of the text information according to the keywords of each type of cluster of the text information.
It should be explained that, respectively calculating the similarity between each word vector of the cluster of any kind and the word vector of the standard keyword of the cluster of any kind, and determining the word corresponding to the word vector with the highest similarity in all word vectors of the cluster of any kind as the keyword of the cluster of any kind, including:
respectively calculating the distance between each word vector of any cluster and the word vector of the standard keyword of the any cluster;
Wherein the distance is a cosine distance or a Euclidean distance;
and determining the word corresponding to the word vector with the minimum distance between the word vector of the standard keyword of any cluster in all word vectors of any cluster as the keyword of any cluster.
Word segmentation preprocessing is carried out on the text information to obtain a plurality of word segmentation results of the text information, and the word segmentation method comprises the following steps:
word segmentation is carried out on the text information according to a preset dictionary so as to obtain a plurality of preliminary word segmentation results;
and removing stop words from the plurality of preliminary word segmentation results according to a preset stop word list so as to obtain a plurality of word segmentation results of the text information.
And step 103, sending the classified and divided user demand sheets to corresponding product management and control departments so that the product management and control departments can judge the grading dimension modules of the user demand sheets.
The grading dimension module comprises attractive appearance, convenient operation, function setting, business process and system operation. The product management and control department judges the scoring dimension module to which the user demand sheet belongs according to the received user demand sheet, so that the attention degree of the user to each scoring dimension module can be conveniently counted and analyzed.
And 104, counting the user demand single cumulative number corresponding to each grading dimension module in a preset time period, and updating the weight value of each grading dimension module in the product grading model according to the user demand single cumulative number to obtain an updated product grading model.
In this embodiment, the weight value of the scoring dimension module is in a proportional incremental relationship with the user demand single cumulative number. The product scoring model is assumed to comprise 5 scoring dimension modules with attractive appearance, convenient operation, function setting, business process and system operation, and the weight values originally set by the product scoring model are respectively 0.1, 0.2, 0.3, 0.25 and 0.15. By counting the user demand single accumulation number corresponding to each scoring dimension module in a preset time period (for example, one month), when the fact that the number of user demands corresponding to the problem A in the preset time period is the largest and the scoring dimension module to which the problem A belongs is set as a function is found, the fact that the scoring dimension module is higher in attention degree of a user and the weight value corresponding to the scoring dimension module needs to be adjusted upwards is indicated, and an updated product scoring model is obtained.
And 105, displaying the user scores of the target products through the updated product score model.
The specific scoring condition of the user on the target product can be shown by the weight value corresponding to each scoring dimension module in the updated product scoring model, as shown in fig. 2. The degree of attention of a user on each grading dimension module can be embodied, and development staff can improve problems existing in products in a targeted manner.
In some embodiments, a scoring report including the user score of the target product may also be generated and sent to the product developer.
Any combination of the above optional solutions may be adopted to form an optional embodiment of the present application, which is not described herein in detail.
In particular, the present application is not limited by the order of execution of the steps described, and certain steps may be performed in other orders or concurrently without conflict.
As can be seen from the above, the product user experience scoring method provided by the embodiment of the present application obtains a user requirement list for a target product; extracting keywords in the user demand form, and classifying and dividing the user demand form according to the keyword mapping and the corresponding product management and control department; the classified and divided user demand sheets are sent to corresponding product management and control departments, so that the product management and control departments can judge the grading dimension modules of the user demand sheets; counting the user demand single cumulative number corresponding to each grading dimension module in a preset time period, and updating the weight value of each grading dimension module in a product grading model according to the user demand single cumulative number to obtain an updated product grading model; and displaying the user score of the target product through the updated product score model. By using the product user experience scoring method provided by the embodiment of the application, the demand list uploaded by the customer is acquired, the text content in the demand list is identified, then the keywords in the text content are extracted, the demand list is classified based on the information of the affiliated departments of the keyword mapping, the classified demand list is distributed to the scoring dimension modules which the departments respectively affiliated further judge the affiliated of the demand list, statistical events of the scoring dimension modules which the demand list affiliated of are accumulated, the occupation ratio of each scoring dimension module in the scoring model is updated, the user score of the target product is displayed through the updated product scoring model, the attention degree of the specific scoring dimension modules to the user can be reflected, and developers can purposefully improve the problems existing in the product.
The embodiment of the application also provides a product user experience scoring device, which can be integrated in the terminal equipment.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a product user experience scoring device according to an embodiment of the present application. The product user experience scoring apparatus 30 may include:
an obtaining module 31, configured to obtain a user requirement list for a target product;
the extracting module 32 is configured to extract keywords in the user requirement list, and classify and divide the user requirement list according to the keyword mapping and the corresponding product management and control department;
the sending module 33 is configured to send the categorized and divided user demand sheet to a product management and control department corresponding to the categorized and divided user demand sheet, so that the product management and control department can determine the scoring dimension module of the user demand sheet;
the statistics module 34 is configured to count a user demand list accumulated number corresponding to each grading dimension module in a preset time period, and update a weight value of each grading dimension module in a product grading model according to the user demand list accumulated number, so as to obtain an updated product grading model;
and the scoring module 35 is configured to display the user score of the target product through the updated product scoring model.
In some embodiments, the device further includes a recognition module, configured to input the user requirement list into a trained text recognition model to perform a text recognition operation, so as to obtain text information in the user requirement list.
In some embodiments, the extracting module 32 is configured to perform word segmentation preprocessing on the text information in the user requirement list to obtain a plurality of word segmentation results of the text information; respectively converting words in the word segmentation results into word vectors, and dividing all word vectors of the text information into class clusters with preset quantity according to a clustering algorithm; adding all word vectors contained in any type of cluster in the text information to be used as the type cluster vector of the any type of cluster; inputting the cluster vector of any cluster into a preset keyword extraction model to obtain the word vector of the standard keyword of any cluster; the keyword extraction model is generated by taking a class cluster vector of each class cluster of each standard text in the first text corpus as input and taking a word vector of a standard keyword of a corresponding class cluster as output to train the deep neural network model; respectively calculating the similarity between each word vector of any cluster and the word vector of the standard keyword of the any cluster, and determining the word corresponding to the word vector with the highest similarity in all word vectors of the any cluster as the keyword of the any cluster; and extracting the keywords of the text information according to the keywords of each type of cluster of the text information.
In some embodiments, the extracting module 32 is configured to calculate a distance between each word vector of the cluster of any type and a word vector of a standard keyword of the cluster of any type; wherein the distance is a cosine distance or a Euclidean distance; and determining the word corresponding to the word vector with the minimum distance between the word vector of the standard keyword of any cluster in all word vectors of any cluster as the keyword of any cluster.
In some embodiments, the extracting module 32 is configured to segment the text information according to a preset dictionary, so as to obtain a plurality of preliminary word segmentation results; and removing stop words from the plurality of preliminary word segmentation results according to a preset stop word list so as to obtain a plurality of word segmentation results of the text information.
In some embodiments, the weight value of the scoring dimension module is in a proportional increasing relationship with the user demand single accumulation number.
In some embodiments, the scoring module 35 is configured to obtain scores of respective scoring dimensions of the target product; and inputting the scores of the scoring dimensionalities into the updated product scoring model, and carrying out weighted summation on the scores of the scoring dimensionalities according to the weight values of the scoring dimensionalities in the updated product scoring model to obtain the product user experience score of the target product.
In specific implementation, each module may be implemented as a separate entity, or may be combined arbitrarily and implemented as the same entity or several entities.
As can be seen from the above, the product user experience scoring device 30 provided in the embodiments of the present application, where the obtaining module 31 is configured to obtain a user requirement list for a target product; the extracting module 32 is configured to extract keywords in the user requirement list, and classify and divide the user requirement list according to the keyword mapping and the corresponding product management and control department; the sending module 33 is configured to send the categorized and divided user demand sheet to a product management and control department corresponding to the categorized and divided user demand sheet, so that the product management and control department can determine the scoring dimension module of the user demand sheet; the statistics module 34 is configured to count a user demand single cumulative number corresponding to each scoring dimension module in a preset time period, and update a weight value of each scoring dimension module in a product scoring model according to the user demand single cumulative number, so as to obtain an updated product scoring model; the scoring module 35 is configured to display the user score of the target product through the updated product scoring model.
Referring to fig. 4, fig. 4 is another schematic structural diagram of a product user experience scoring device provided in an embodiment of the present application, where the product user experience scoring device 30 includes a memory 120, one or more processors 180, and one or more application programs, where the one or more application programs are stored in the memory 120 and configured to be executed by the processors 180; the processor 180 may include a construction module 31, an extraction module 32, a transmission module 33, a statistics module 34, and a scoring module 35. For example, the structures and connection relationships of the above respective components may be as follows:
The processor 180 is a control center of the device, connects various parts of the entire terminal using various interfaces and lines, and performs various functions of the device and processes data by running or executing application programs stored in the memory 120 and calling data stored in the memory 120, thereby performing overall monitoring of the device. Optionally, the processor 180 may include one or more processing cores; preferably, the processor 180 may integrate an application processor and a modem processor, wherein the application processor primarily processes an operating system, user interfaces, application programs, and the like.
In particular, in this embodiment, the processor 180 loads executable codes corresponding to the processes of one or more application programs into the memory 120 according to the following instructions, and the processor 180 executes the application programs stored in the memory 120, so as to implement various functions:
a construction instruction is used for constructing a bill relation network diagram of a bill partner to be identified based on the identity information of a plurality of bill correspondents;
the method comprises the steps of obtaining an instruction, wherein the instruction is used for obtaining a user demand sheet aiming at a target product;
the extraction instruction is used for extracting keywords in the user demand sheet, and classifying and dividing the user demand sheet according to the keyword mapping and the corresponding product management and control department;
the sending instruction is used for sending the classified and divided user demand sheets to corresponding product management and control departments so that the product management and control departments can judge the grading dimension modules of the user demand sheets;
the statistical instruction is used for counting the user demand single cumulative number corresponding to each grading dimension module in a preset time period, updating the weight value of each grading dimension module in the product grading model according to the user demand single cumulative number, and obtaining an updated product grading model;
And the scoring instruction is used for displaying the user score of the target product through the updated product scoring model.
In some embodiments, the program further includes a recognition instruction, configured to input the user requirement list into a trained text recognition model to perform a text recognition operation, so as to obtain text information in the user requirement list.
In some embodiments, the extracting instruction is configured to perform word segmentation preprocessing on text information in the user requirement list to obtain a plurality of word segmentation results of the text information; respectively converting words in the word segmentation results into word vectors, and dividing all word vectors of the text information into class clusters with preset quantity according to a clustering algorithm; adding all word vectors contained in any type of cluster in the text information to be used as the type cluster vector of the any type of cluster; inputting the cluster vector of any cluster into a preset keyword extraction model to obtain the word vector of the standard keyword of any cluster; the keyword extraction model is generated by taking a class cluster vector of each class cluster of each standard text in the first text corpus as input and taking a word vector of a standard keyword of a corresponding class cluster as output to train the deep neural network model; respectively calculating the similarity between each word vector of any cluster and the word vector of the standard keyword of the any cluster, and determining the word corresponding to the word vector with the highest similarity in all word vectors of the any cluster as the keyword of the any cluster; and extracting the keywords of the text information according to the keywords of each type of cluster of the text information.
In some embodiments, the extracting instruction is configured to calculate a distance between each word vector of the cluster of any type and a word vector of a standard keyword of the cluster of any type; wherein the distance is a cosine distance or a Euclidean distance; and determining the word corresponding to the word vector with the minimum distance between the word vector of the standard keyword of any cluster in all word vectors of any cluster as the keyword of any cluster.
In some embodiments, the extracting instruction is configured to segment the text information according to a preset dictionary, so as to obtain a plurality of preliminary word segmentation results; and removing stop words from the plurality of preliminary word segmentation results according to a preset stop word list so as to obtain a plurality of word segmentation results of the text information.
In some embodiments, the weight value of the scoring dimension module is in a proportional increasing relationship with the user demand single accumulation number.
In some embodiments, the scoring instruction is configured to obtain scores of each scoring dimension of the target product respectively; and inputting the scores of the scoring dimensionalities into the updated product scoring model, and carrying out weighted summation on the scores of the scoring dimensionalities according to the weight values of the scoring dimensionalities in the updated product scoring model to obtain the product user experience score of the target product.
The embodiment of the application also provides terminal equipment. The terminal equipment can be a server, a smart phone, a computer, a tablet personal computer and the like.
Referring to fig. 4, fig. 4 shows a schematic structural diagram of a terminal device provided in an embodiment of the present application, where the terminal device may be used to implement the product user experience scoring method provided in the foregoing embodiment. The terminal device 1200 may be a smart phone or a tablet computer.
As shown in fig. 5, the terminal device 1200 may include an RF (Radio Frequency) circuit 110, a memory 120 including one or more (only one is shown in the figure) computer readable storage mediums, an input unit 130, a display unit 140, a sensor 150, an audio circuit 160, a transmission module 170, a processor 180 including one or more (only one is shown in the figure) processing cores, and a power supply 190. It will be appreciated by those skilled in the art that the configuration of the terminal device 1200 shown in fig. 5 does not constitute a limitation of the terminal device 1200, and may include more or fewer components than shown, or may combine certain components, or may have a different arrangement of components. Wherein:
the RF circuit 110 is configured to receive and transmit electromagnetic waves, and to perform mutual conversion between the electromagnetic waves and the electrical signals, so as to communicate with a communication network or other devices. RF circuitry 110 may include various existing circuit elements for performing these functions, such as an antenna, a radio frequency transceiver, a digital signal processor, an encryption/decryption chip, a Subscriber Identity Module (SIM) card, memory, and the like. The RF circuitry 110 may communicate with various networks such as the internet, intranets, wireless networks, or other devices via wireless networks.
The memory 120 may be used to store software programs and modules, such as program instructions/modules corresponding to the product user experience scoring method in the above embodiment, and the processor 180 executes various functional applications and product user experience scoring by running the software programs and modules stored in the memory 120, so that the vibration reminding mode can be automatically selected to update data according to the current scene where the terminal device is located, thereby not only ensuring that the scenes such as a conference are not disturbed, but also ensuring that the user can perceive an incoming call, and improving the intelligence of the terminal device. Memory 120 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 120 may further include memory remotely located relative to processor 180, which may be connected to terminal device 1200 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input unit 130 may be used to receive input numeric or character information and to generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control. In particular, the input unit 130 may comprise a touch sensitive surface 131 and other input devices 132. The touch sensitive surface 131, also referred to as a touch display screen or touch pad, may collect touch operations thereon or thereabout by a user (e.g., operations of the user on the touch sensitive surface 131 or thereabout by any suitable object or accessory such as a finger, stylus, etc.), and actuate the corresponding connection means according to a pre-set program. Alternatively, the touch sensitive surface 131 may comprise two parts, a touch detection device and a touch controller. The touch control detection device detects the touch control direction of a user, detects signals brought by touch control operation and transmits the signals to the touch control controller; the touch controller receives touch information from the touch detection device, converts the touch information into touch coordinates, sends the touch coordinates to the processor 180, and can receive and execute commands sent by the processor 180. In addition, the touch-sensitive surface 131 may be implemented in various types of resistive, capacitive, infrared, surface acoustic wave, and the like. In addition to the touch-sensitive surface 131, the input unit 130 may also comprise other input devices 132. In particular, other input devices 132 may include, but are not limited to, one or more of a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, mouse, joystick, etc.
The display unit 140 may be used to display information input by a user or information provided to the user and various graphical user interfaces of the terminal device 1200, which may be composed of graphics, text, icons, video, and any combination thereof. The display unit 140 may include a display panel 141, and alternatively, the display panel 141 may be configured in the form of an LCD (Liquid Crystal Display ), an OLED (Organic Light-Emitting Diode), or the like. Further, the touch-sensitive surface 131 may cover the display panel 141, and after the touch-sensitive surface 131 detects a touch operation thereon or thereabout, the touch-sensitive surface is transferred to the processor 180 to determine a type of touch event, and then the processor 180 provides a corresponding visual output on the display panel 141 according to the type of touch event. Although in fig. 5 the touch-sensitive surface 131 and the display panel 141 are implemented as two separate components for input and output functions, in some embodiments the touch-sensitive surface 131 may be integrated with the display panel 141 to implement the input and output functions.
The terminal device 1200 may also include at least one sensor 150, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor that may adjust the brightness of the display panel 141 according to the brightness of ambient light, and a proximity sensor that may turn off the display panel 141 and/or the backlight when the terminal device 1200 moves to the ear. As one of the motion sensors, the gravity acceleration sensor can detect the acceleration in all directions (generally three axes), and can detect the gravity and the direction when the mobile phone is stationary, and can be used for applications of recognizing the gesture of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer gesture calibration), vibration recognition related functions (such as pedometer and knocking), and the like; other sensors such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc. that may also be configured with the terminal device 1200 are not described in detail herein.
Terminal device 1200 may facilitate user email, web browsing, streaming media access, etc. via a transmission module 170 (e.g., wi-Fi module) that provides wireless broadband internet access to the user. Although fig. 5 shows the transmission module 170, it is understood that it does not belong to the essential constitution of the terminal device 1200, and may be omitted entirely as needed within the scope of not changing the essence of the invention.
The processor 180 is a control center of the terminal device 1200, connects various parts of the entire mobile phone using various interfaces and lines, and performs various functions of the terminal device 1200 and processes data by running or executing software programs and/or modules stored in the memory 120, and calling data stored in the memory 120, thereby performing overall monitoring of the mobile phone. Optionally, the processor 180 may include one or more processing cores; in some embodiments, the processor 180 may integrate an application processor that primarily processes operating systems, user interfaces, applications, etc., with a modem processor that primarily processes wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 180.
The terminal device 1200 also includes a power supply 190 that provides power to the various components, and in some embodiments, may be logically coupled to the processor 180 via a power management system to perform functions such as managing discharge, and managing power consumption via the power management system. The power supply 190 may also include one or more of any of a direct current or alternating current power supply, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like.
Although not shown, the terminal device 1200 may further include a camera (such as a front camera, a rear camera), a bluetooth module, etc., which will not be described herein. In particular, in the present embodiment, the display unit 140 of the terminal device 1200 is a touch screen display, the terminal device 1200 further includes a memory 120, and one or more programs, wherein the one or more programs are stored in the memory 120 and configured to be executed by the one or more processors 180, the one or more programs include instructions for:
a construction instruction is used for constructing a bill relation network diagram of a bill partner to be identified based on the identity information of a plurality of bill correspondents;
The method comprises the steps of obtaining an instruction, wherein the instruction is used for obtaining a user demand sheet aiming at a target product;
the extraction instruction is used for extracting keywords in the user demand sheet, and classifying and dividing the user demand sheet according to the keyword mapping and the corresponding product management and control department;
the sending instruction is used for sending the classified and divided user demand sheets to corresponding product management and control departments so that the product management and control departments can judge the grading dimension modules of the user demand sheets;
the statistical instruction is used for counting the user demand single cumulative number corresponding to each grading dimension module in a preset time period, updating the weight value of each grading dimension module in the product grading model according to the user demand single cumulative number, and obtaining an updated product grading model;
and the scoring instruction is used for displaying the user score of the target product through the updated product scoring model.
In some embodiments, the program further includes a recognition instruction, configured to input the user requirement list into a trained text recognition model to perform a text recognition operation, so as to obtain text information in the user requirement list.
In some embodiments, the extracting instruction is configured to perform word segmentation preprocessing on text information in the user requirement list to obtain a plurality of word segmentation results of the text information; respectively converting words in the word segmentation results into word vectors, and dividing all word vectors of the text information into class clusters with preset quantity according to a clustering algorithm; adding all word vectors contained in any type of cluster in the text information to be used as the type cluster vector of the any type of cluster; inputting the cluster vector of any cluster into a preset keyword extraction model to obtain the word vector of the standard keyword of any cluster; the keyword extraction model is generated by taking a class cluster vector of each class cluster of each standard text in the first text corpus as input and taking a word vector of a standard keyword of a corresponding class cluster as output to train the deep neural network model; respectively calculating the similarity between each word vector of any cluster and the word vector of the standard keyword of the any cluster, and determining the word corresponding to the word vector with the highest similarity in all word vectors of the any cluster as the keyword of the any cluster; and extracting the keywords of the text information according to the keywords of each type of cluster of the text information.
In some embodiments, the extracting instruction is configured to calculate a distance between each word vector of the cluster of any type and a word vector of a standard keyword of the cluster of any type; wherein the distance is a cosine distance or a Euclidean distance; and determining the word corresponding to the word vector with the minimum distance between the word vector of the standard keyword of any cluster in all word vectors of any cluster as the keyword of any cluster.
In some embodiments, the extracting instruction is configured to segment the text information according to a preset dictionary, so as to obtain a plurality of preliminary word segmentation results; and removing stop words from the plurality of preliminary word segmentation results according to a preset stop word list so as to obtain a plurality of word segmentation results of the text information.
In some embodiments, the weight value of the scoring dimension module is in a proportional increasing relationship with the user demand single accumulation number.
In some embodiments, the scoring instruction is configured to obtain scores of each scoring dimension of the target product respectively; and inputting the scores of the scoring dimensionalities into the updated product scoring model, and carrying out weighted summation on the scores of the scoring dimensionalities according to the weight values of the scoring dimensionalities in the updated product scoring model to obtain the product user experience score of the target product.
The embodiment of the application also provides a storage medium, in which a computer program is stored, and when the computer program runs on a computer, the computer executes the product user experience scoring method according to any one of the embodiments.
It should be noted that, for the product user experience scoring method described in the present application, it will be understood by those skilled in the art that all or part of the process of implementing the product user experience scoring method described in the embodiments of the present application may be implemented by controlling related hardware by a computer program, where the computer program may be stored in a computer readable storage medium, such as a memory of a terminal device, and executed by at least one processor in the terminal device, and the execution process may include the process of implementing the embodiment of the product user experience scoring method. The storage medium may be a magnetic disk, an optical disk, a Read Only Memory (ROM), a random access Memory (RAM, random Access Memory), or the like.
For the product user experience scoring device of the embodiment of the application, each functional module of the product user experience scoring device can be integrated in one processing chip, each module can exist alone physically, and two or more modules can be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated module, if implemented as a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium such as read-only memory, magnetic or optical disk, etc.
The method, the device, the medium and the equipment for scoring the product user experience provided by the embodiment of the application are described in detail. The principles and embodiments of the present application are described herein with specific examples, the above examples being provided only to assist in understanding the methods of the present application and their core ideas; meanwhile, those skilled in the art will have variations in the specific embodiments and application scope in light of the ideas of the present application, and the present description should not be construed as limiting the present application in view of the above.
Claims (10)
1. A product user experience scoring method, the product user experience scoring method being derived based on a product scoring model, the product scoring model comprising a plurality of scoring dimension modules, the method comprising:
acquiring a user demand form aiming at a target product;
extracting keywords in the user demand form, and classifying and dividing the user demand form according to the keyword mapping and the corresponding product management and control department;
the classified and divided user demand sheets are sent to corresponding product management and control departments, so that the product management and control departments can judge the grading dimension modules of the user demand sheets;
Counting the user demand single cumulative number corresponding to each grading dimension module in a preset time period, and updating the weight value of each grading dimension module in a product grading model according to the user demand single cumulative number to obtain an updated product grading model;
and displaying the user score of the target product through the updated product score model.
2. The product user experience scoring method of claim 1, wherein prior to the extracting keywords in the user requirement sheet, the method further comprises:
and inputting the user demand form into a trained text recognition model to perform text recognition operation, so as to obtain text information in the user demand form.
3. The product user experience scoring method of claim 2, wherein the extracting keywords in the user demand sheet comprises:
word segmentation preprocessing is carried out on the text information in the user demand list so as to obtain a plurality of word segmentation results of the text information;
respectively converting words in the word segmentation results into word vectors, and dividing all word vectors of the text information into class clusters with preset quantity according to a clustering algorithm;
Adding all word vectors contained in any type of cluster in the text information to be used as the type cluster vector of the any type of cluster;
inputting the cluster vector of any cluster into a preset keyword extraction model to obtain the word vector of the standard keyword of any cluster; the keyword extraction model is generated by taking a class cluster vector of each class cluster of each standard text in the first text corpus as input and taking a word vector of a standard keyword of a corresponding class cluster as output to train the deep neural network model;
respectively calculating the similarity between each word vector of any cluster and the word vector of the standard keyword of the any cluster, and determining the word corresponding to the word vector with the highest similarity in all word vectors of the any cluster as the keyword of the any cluster;
and extracting the keywords of the text information according to the keywords of each type of cluster of the text information.
4. The product user experience scoring method according to claim 3, wherein the calculating the similarity between each word vector of the cluster of any kind and the word vector of the standard keyword of the cluster of any kind, and determining the word corresponding to the word vector with the highest similarity among all word vectors of the cluster of any kind as the keyword of the cluster of any kind, respectively, includes:
Respectively calculating the distance between each word vector of any cluster and the word vector of the standard keyword of the any cluster;
wherein the distance is a cosine distance or a Euclidean distance;
and determining the word corresponding to the word vector with the minimum distance between the word vector of the standard keyword of any cluster in all word vectors of any cluster as the keyword of any cluster.
5. The method for scoring a product user experience of claim 4, wherein the word segmentation pre-processing the text message to obtain a plurality of word segmentation results for the text message comprises:
word segmentation is carried out on the text information according to a preset dictionary so as to obtain a plurality of preliminary word segmentation results;
and removing stop words from the plurality of preliminary word segmentation results according to a preset stop word list so as to obtain a plurality of word segmentation results of the text information.
6. The product user experience scoring method of claim 1, wherein the weight value of the scoring dimension module is in a proportional increasing relationship with the user demand single cumulative number.
7. The product user experience scoring method of claim 1, wherein the method further comprises:
A scoring report is generated that includes a user score for the target product.
8. Product user experience scoring device, product user experience scoring device is used for the product scoring model, the product scoring model includes a plurality of dimension module that scores, its characterized in that, product user experience scoring device includes:
the acquisition module is used for acquiring a user demand sheet aiming at a target product;
the extraction module is used for extracting keywords in the user demand sheet and classifying and dividing the user demand sheet according to the keyword mapping and the corresponding product management and control department;
the sending module is used for sending the classified and divided user demand sheets to corresponding product management and control departments so that the product management and control departments can judge the grading dimension module of the user demand sheets;
the statistics module is used for counting the user demand single accumulated number corresponding to each grading dimension module in a preset time period, and updating the weight value of each grading dimension module in the product grading model according to the user demand single accumulated number to obtain an updated product grading model;
and the scoring module is used for displaying the user scores of the target products through the updated product scoring model.
9. A computer readable storage medium storing a plurality of instructions adapted to be loaded by a processor to perform the product user experience scoring method of any one of claims 1 to 7.
10. A terminal device comprising a processor and a memory, the memory storing a plurality of instructions, the processor loading the instructions to perform the product user experience scoring method of any one of claims 1 to 7.
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| CN116957633A (en) * | 2023-09-19 | 2023-10-27 | 武汉创知致合科技有限公司 | Product design user experience evaluation method based on intelligent home scene |
| CN116957633B (en) * | 2023-09-19 | 2023-12-01 | 武汉创知致合科技有限公司 | Product design user experience evaluation method based on intelligent home scene |
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