CN116521989A - Visual tool recommendation processing method and device - Google Patents
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Abstract
The invention provides a visual tool recommendation processing method and device, relates to the technical field of big data, and can be used in the financial field or other technical fields. The method comprises the following steps: acquiring user category label data, preprocessing the user category label data, and clustering sample data in a sample set according to the preprocessed user category label data; determining user category labels corresponding to each cluster as a clustering result, and determining a target cluster to which a target user of visual recommendation belongs according to the clustering result; and selecting a preset number of cluster users from the target cluster, acquiring statistical frequency information of the visualization tools used by the cluster users, and determining target visualization tools recommended to the target users according to the statistical frequency information. The apparatus performs the above method. The method and the device provided by the embodiment of the invention can comprehensively and accurately determine the visualization tool recommended to the user based on the user category.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a visual tool recommendation processing method and device.
Background
With the gradual development of big data technology, data visualization tools are widely applied in the fields of data exploration, data analysis, mining and the like, and more users generate insights, exchange discovery and make decisions according to the visualization tools.
However, as visualization tools increase, intelligent recommendation visualization tools are necessary. The existing method generally comprises the steps that a data application party selects a visual tool based on own habits and subjective judgment of user preference, and displays data to a user by using the selected visual tool.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides a method and a device for processing the recommendation of a visualization tool, which can at least partially solve the problems in the prior art.
In one aspect, the present invention provides a visualization tool recommendation processing method, including:
acquiring user category label data, preprocessing the user category label data, and clustering sample data in a sample set according to the preprocessed user category label data;
determining user category labels corresponding to each cluster as a clustering result, and determining a target cluster to which a target user of visual recommendation belongs according to the clustering result;
and selecting a preset number of cluster users from the target cluster, acquiring statistical frequency information of the visualization tools used by the cluster users, and determining target visualization tools recommended to the target users according to the statistical frequency information.
Wherein, the preprocessing the user category label data includes:
digitally tagging the user category tag data;
and performing dimension reduction processing on the digitally marked user category label data, and taking the dimension-reduced data as preprocessed user category label data.
The clustering processing of the sample data in the sample set according to the preprocessed user category label data comprises the following steps:
and clustering the sample data in the sample set according to the preprocessed user category label data by adopting a K-means method.
The determining, according to the clustering result, a target cluster group to which the target user of the visual recommendation belongs, includes:
and acquiring target user category label data of the target user, and taking the cluster group which is the same as the target user category label data in the clustering result as the target cluster group.
The determining, according to the clustering result, a target cluster group to which the target user of the visual recommendation belongs, includes:
and selecting a neighbor of the target user from the clustering result by adopting a KNN method, and determining a target cluster group to which the target user belongs according to the neighbor.
Wherein the selecting a preset number of cluster users from the target cluster includes:
and averagely selecting cluster users with total number equal to the preset number from the target cluster.
Wherein the determining, according to the statistical frequency information, a target visualization tool recommended to the target user includes:
and respectively calculating the sum of the number of the statistical frequencies respectively corresponding to each visualization tool, and determining the visualization tool with the largest sum value of the number of the statistical frequencies as the target visualization tool.
In one aspect, the present invention provides a visualization tool recommendation processing apparatus, including:
the clustering unit is used for acquiring user category label data, preprocessing the user category label data and clustering sample data in a sample set according to the preprocessed user category label data;
the determining unit is used for determining user category labels corresponding to each cluster as a clustering result, and determining a target cluster group to which a target user of the visual recommendation belongs according to the clustering result;
and the recommending unit is used for selecting a preset number of cluster users from the target cluster, acquiring the statistical frequency information of the visualization tools used by the cluster users, and determining the target visualization tools recommended to the target users according to the statistical frequency information.
In still another aspect, an embodiment of the present invention provides an electronic device, including: a processor, a memory, and a bus, wherein,
the processor and the memory complete communication with each other through the bus;
the memory stores program instructions executable by the processor, the processor invoking the program instructions capable of performing the method of:
acquiring user category label data, preprocessing the user category label data, and clustering sample data in a sample set according to the preprocessed user category label data;
determining user category labels corresponding to each cluster as a clustering result, and determining a target cluster to which a target user of visual recommendation belongs according to the clustering result;
and selecting a preset number of cluster users from the target cluster, acquiring statistical frequency information of the visualization tools used by the cluster users, and determining target visualization tools recommended to the target users according to the statistical frequency information.
Embodiments of the present invention provide a non-transitory computer readable storage medium comprising:
the non-transitory computer readable storage medium stores computer instructions that cause the computer to perform the method of:
acquiring user category label data, preprocessing the user category label data, and clustering sample data in a sample set according to the preprocessed user category label data;
determining user category labels corresponding to each cluster as a clustering result, and determining a target cluster to which a target user of visual recommendation belongs according to the clustering result;
and selecting a preset number of cluster users from the target cluster, acquiring statistical frequency information of the visualization tools used by the cluster users, and determining target visualization tools recommended to the target users according to the statistical frequency information.
According to the visual tool recommendation processing method and device provided by the embodiment of the invention, user category label data is obtained, preprocessing is carried out on the user category label data, and clustering processing is carried out on sample data in a sample set according to the preprocessed user category label data; determining user category labels corresponding to each cluster as a clustering result, and determining a target cluster to which a target user of visual recommendation belongs according to the clustering result; and selecting a preset number of cluster users from the target cluster, acquiring statistical frequency information of the visualization tools used by the cluster users, and determining target visualization tools recommended to the target users according to the statistical frequency information, so that the visualization tools recommended to the users can be comprehensively and accurately determined based on user types.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:
fig. 1 is a flow chart of a visualization tool recommendation processing method according to an embodiment of the invention.
Fig. 2 is a flow chart of a visualization tool recommendation processing method according to another embodiment of the present invention.
Fig. 3 is a schematic structural diagram of a visualization tool recommendation processing device according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of an entity structure of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings. The exemplary embodiments of the present invention and their descriptions herein are for the purpose of explaining the present invention, but are not to be construed as limiting the invention. It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be arbitrarily combined with each other.
Fig. 1 is a flow chart of a visualization tool recommendation processing method according to an embodiment of the present invention, as shown in fig. 1, where the visualization tool recommendation processing method according to the embodiment of the present invention includes:
step S1: and acquiring user category label data, preprocessing the user category label data, and clustering sample data in a sample set according to the preprocessed user category label data.
Step S2: and determining the user category labels corresponding to each cluster as a clustering result, and determining the target cluster to which the target user of the visual recommendation belongs according to the clustering result.
Step S3: and selecting a preset number of cluster users from the target cluster, acquiring statistical frequency information of the visualization tools used by the cluster users, and determining target visualization tools recommended to the target users according to the statistical frequency information.
In the step S1, the device acquires user category label data, preprocesses the user category label data, and performs clustering processing on sample data in the sample set according to the preprocessed user category label data. The apparatus may be a computer device, for example a server, performing the method. It should be noted that, the data acquisition and analysis according to the embodiments of the present invention are authorized by the user. The user categories are obtained according to user attribute division using the BI platform, each user category is identified through user category label data, and the BI platform is briefly described as follows:
the BI platform, namely the BI visual platform, realizes rich data presentation, can provide various analysis decision services for users, the BI visual and man-machine interaction capability of the BI visual gradually changes from decision making of the users to decision making of the machines, the cognitive ability of the people is limited, for technological development, the users are always relatively lagged, the center of gravity of man-machine interaction is that the machine decision can bear more responsibilities of data processing, data presentation and the like, and the people are more to effectively perceive information provided by the machines to make decision judgment.
The invention considers that the cognitive abilities of users at different levels play a decisive role in the perception effect of the visual view, and the cognitive abilities are the abilities of the human brain to process, store and extract information. Visual view recommendation the final object oriented is a person, so how to accurately recommend visual views that conform to the cognitive abilities of the user itself is a problem to be solved by the present invention. Thus, the use of users is classified into expert leaders, data analysts, professionals, non-professionals, and the like.
The user's visual skill expertise is classified into a visual small white user, a medium visual user and an expert visual user.
From the viewpoint of visualization of professional cognitive ability, the system is divided into doctor and above, master, family, specialty and below.
From the perspective of visualization habits, it is classified into creating static graphics, creating dynamic associative graphs, creating reports, and creating 3D animations. The user category label data is shown in table 1:
TABLE 1
The sample set includes sample data as shown in table 2:
TABLE 2
Where u1, u2, etc. represent each sample data in the sample set, each sample data comprising a user attribute, the user attribute being identified by the above-mentioned user category label data.
The preprocessing the user category label data comprises the following steps:
digitally tagging the user category tag data; the nominal conversion is realized, namely, user category labels, such as expert leaders, data analysts, business personnel and non-professionals, are respectively represented by numbers, which can be represented as 1,2,3 and 4, wherein the numbers have no meaning of size.
Similarly, doctor and above, filling, family and specialty and below are also denoted by numerals, respectively, and may be denoted by 1,2,3,4, wherein the numerals have no meaning of magnitude.
And performing dimension reduction processing on the digitally marked user category label data, and taking the dimension-reduced data as preprocessed user category label data. In the subsequent clustering process, the label data of each user category should not have a strong correlation, otherwise, the label data of each user category occupies a high weight in the similar distance calculation, so that the clustering result is adversely affected.
The method can use the existing tool to realize the dimension reduction processing of the digitally marked user category label data, and the digitally marked user category label data is firstly marked before the dimension reduction processing, so that the user category label data can be adapted to the existing tool.
Referring to the above illustration, the preprocessed user class label data is shown in table 3:
TABLE 3 Table 3
The column in which "visualization habit" is located is removed in table 3 compared to table 2 because of the stronger correlation with other user categories.
It should be noted that, in the implementation of the method according to the embodiment of the present invention, before digitally marking the user class label data, the inventors found that a large number of data in the sample set exhibit a normal distribution characteristic, and the data form of the distribution is clustered by using the K-means method.
The clustering processing of the sample data in the sample set according to the preprocessed user category label data comprises the following steps:
and clustering the sample data in the sample set according to the preprocessed user category label data by adopting a K-means method. The K-means method is described as follows:
the K-means method is a clustering algorithm.
Clustering is to divide a sample set into different classes or clusters according to a specific criterion (such as a distance criterion), so that the similarity of data objects in the same cluster is as large as possible, and the difference of data objects not in the same cluster is also as large as possible. The data of the same class after clustering are gathered together as much as possible, and different data are separated as much as possible, so that the method is an unsupervised learning method. The K-means algorithm uses K as a parameter to divide the object into K clusters, so that the clusters have higher similarity and the clusters have lower similarity.
As shown in fig. 2, n represents the number of iterations of the K-means algorithm.
K elements are randomly taken from the sample set D as the center of k clusters.
The dissimilarity (distance) of all the remaining elements to the center of k clusters is calculated, and these elements are classified into clusters having the lowest dissimilarity (smallest distance), respectively. The distance measure may take the form of euclidean distance, i.e. the linear distance of two points in space.
And (3) re-calculating the centers of the k clusters according to the clustering result, wherein the calculation method is to take the arithmetic average of the respective dimensions of all elements in the clusters.
All elements in D are re-clustered according to the center of the new cluster.
The step of calculating the arithmetic mean of the respective dimensions of all elements in the cluster is repeated until the clustering result is no longer significantly changed (not shown in fig. 2, which may be achieved by comparing the error value with an error threshold), or the clustering is ended by directly setting the number of iterations.
And outputting a final clustering result.
In the step S2, the device determines the user category labels corresponding to each cluster as a clustering result, and determines the target cluster group to which the target user of the visual recommendation belongs according to the clustering result. For the case where the target user category label for the target user is known: the determining, according to the clustering result, a target cluster group to which the target user of the visual recommendation belongs, includes:
and acquiring target user category label data of the target user, and taking the cluster group which is the same as the target user category label data in the clustering result as the target cluster group. For example, the target user is expert leader, visual white users and doctor and above, and the visual habit is not needed to be considered because of the dimension reduction of the data.
For the case where the target user class label for the target user is unknown: the determining, according to the clustering result, a target cluster group to which the target user of the visual recommendation belongs, includes:
and selecting a neighbor of the target user from the clustering result by adopting a KNN method, and determining a target cluster group to which the target user belongs according to the neighbor.
The KNN method is a classification algorithm, and is described as follows:
the KNN nearest neighbor classification algorithm is used for judging the category of an unknown sample, taking samples of all known categories as references, calculating the distance between the unknown sample and all known samples, selecting K known samples closest to the unknown sample, classifying the unknown sample and the category belonging to the K nearest neighbor samples into one category according to a majority-obeying voting rule, and is a supervised learning algorithm.
According to the KNN algorithm, the relation between the sample set and the related visualization method is considered, and the decisive role of the cognitive ability of the person in the process of visual view auxiliary decision is considered, so that the recommendation result is more accurate.
As shown in fig. 2, after the final clustering result is output based on the K-means algorithm, the target cluster group to which the target user belongs is determined by the KNN algorithm, for example, the target cluster group is a cluster group corresponding to expert leaders, visual white users, doctor and above.
In the step S3, the device selects a preset number of cluster users from the target cluster, obtains statistical frequency information of the visualization tools used by the cluster users, and determines a target visualization tool recommended to the target user according to the statistical frequency information. The preset number can be a natural number and can be set autonomously according to actual conditions. The minimum selectable value of the preset number is 1, at this time, one cluster group can be randomly selected from the expert leader, the visual white users, the doctor and the cluster groups above, and then one cluster group user is randomly selected from the cluster groups, so that the statistical frequency information of the visualization tool used by the cluster group user is obtained.
The embodiment of the invention is a large data based visualization tool recommendation processing method, and it can be understood that the larger the preset data quantity is, the larger a data sample which can be used as data analysis is, so that the more accurate the final recommended target visualization tool is.
If the preset number is large, the cluster groups are randomly selected, so that the cluster group users of some cluster groups are too many, and the cluster group users of some cluster groups are too few, thereby affecting the accuracy of the final recommended target visualization tool result.
For example, if the preset number is 9000, if 6000 users belong to the expert leader, only 3000 users of the visualized small white users and doctor and above may appear.
The selecting a preset number of cluster users from the target cluster includes:
and averagely selecting cluster users with total number equal to the preset number from the target cluster. Referring to the above example, if the preset number is 9000, 3000 cluster users are selected from the expert leader, the visual white-light users, and the doctor and above cluster groups, respectively.
The visualization tool may be specifically a bar chart, a line chart, an instrument panel, etc., and is not specifically limited, as shown in table 4:
TABLE 4 Table 4
The determining, according to the statistical frequency information, a target visualization tool recommended to the target user, including:
and respectively calculating the sum of the number of the statistical frequencies respectively corresponding to each visualization tool, and determining the visualization tool with the largest sum value of the number of the statistical frequencies as the target visualization tool. Referring to table 4, if the users corresponding to the bar graph include a total of 1500 users u1, u4, u5, etc., and the sum of the number of statistical frequencies of the users using the bar graph is 20000 times, more than the sum of the number of statistical frequencies respectively corresponding to the line graph and the dashboard, the bar graph is determined as the target visualization tool recommended to the target user.
According to the visual tool recommendation processing method provided by the embodiment of the invention, user category label data is obtained, preprocessing is carried out on the user category label data, and clustering processing is carried out on sample data in a sample set according to the preprocessed user category label data; determining user category labels corresponding to each cluster as a clustering result, and determining a target cluster to which a target user of visual recommendation belongs according to the clustering result; and selecting a preset number of cluster users from the target cluster, acquiring statistical frequency information of the visualization tools used by the cluster users, and determining target visualization tools recommended to the target users according to the statistical frequency information, so that the visualization tools recommended to the users can be comprehensively and accurately determined based on user types.
Further, the preprocessing the user category label data includes:
digitally tagging the user category tag data; reference may be made to the above embodiments, and no further description is given.
And performing dimension reduction processing on the digitally marked user category label data, and taking the dimension-reduced data as preprocessed user category label data. Reference may be made to the above embodiments, and no further description is given.
According to the visualization tool recommendation processing method provided by the embodiment of the invention, the accuracy of the visualization tool recommendation is further improved by removing the user category with strong correlation.
Further, the clustering processing of the sample data in the sample set according to the preprocessed user category label data includes:
and clustering the sample data in the sample set according to the preprocessed user category label data by adopting a K-means method. Reference may be made to the above embodiments, and no further description is given.
The visualization tool recommendation processing method provided by the embodiment of the invention can further improve the clustering processing efficiency through the K-means method.
Further, the determining, according to the clustering result, a target cluster group to which the target user of the visual recommendation belongs, includes:
and acquiring target user category label data of the target user, and taking the cluster group which is the same as the target user category label data in the clustering result as the target cluster group. Reference may be made to the above embodiments, and no further description is given.
The visualization tool recommendation processing method provided by the embodiment of the invention can directly and rapidly acquire the target cluster.
Further, the determining, according to the clustering result, a target cluster group to which the target user of the visual recommendation belongs, includes:
and selecting a neighbor of the target user from the clustering result by adopting a KNN method, and determining a target cluster group to which the target user belongs according to the neighbor. Reference may be made to the above embodiments, and no further description is given.
The visualization tool recommendation processing method provided by the embodiment of the invention can further obtain the target cluster group efficiently.
Further, the selecting a preset number of cluster users from the target cluster includes:
and averagely selecting cluster users with total number equal to the preset number from the target cluster. Reference may be made to the above embodiments, and no further description is given.
According to the visualization tool recommendation processing method provided by the embodiment of the invention, the accuracy of the visualization tool recommendation is further improved by selecting cluster users on average.
Further, the determining, according to the statistical frequency information, a target visualization tool recommended to the target user includes:
and respectively calculating the sum of the number of the statistical frequencies respectively corresponding to each visualization tool, and determining the visualization tool with the largest sum value of the number of the statistical frequencies as the target visualization tool. Reference may be made to the above embodiments, and no further description is given.
The visualization tool recommendation processing method provided by the embodiment of the invention further improves the accuracy of visualization tool recommendation in an intuitive quantification mode.
It should be noted that, the method for processing visualization tool recommendation provided by the embodiment of the invention can be used in the financial field, and can also be used in any technical field except the financial field.
Fig. 3 is a schematic structural diagram of a visualization tool recommendation processing device according to an embodiment of the present invention, as shown in fig. 3, where the visualization tool recommendation processing device according to an embodiment of the present invention includes a clustering unit 301, a determining unit 302, and a recommendation unit 303, where:
the clustering unit 301 is configured to obtain user class label data, pre-process the user class label data, and perform clustering processing on sample data in a sample set according to the pre-processed user class label data; the determining unit 302 is configured to determine a user category label corresponding to each cluster as a clustering result, and determine a target cluster group to which a target user of the visual recommendation belongs according to the clustering result; the recommending unit 303 is configured to select a preset number of cluster users from the target cluster groups, obtain statistical frequency information of visualization tools used by the cluster users, and determine a target visualization tool recommended to the target user according to the statistical frequency information.
Specifically, a clustering unit 301 in the device is configured to obtain user class label data, pre-process the user class label data, and perform clustering processing on sample data in a sample set according to the pre-processed user class label data; the determining unit 302 is configured to determine a user category label corresponding to each cluster as a clustering result, and determine a target cluster group to which a target user of the visual recommendation belongs according to the clustering result; the recommending unit 303 is configured to select a preset number of cluster users from the target cluster groups, obtain statistical frequency information of visualization tools used by the cluster users, and determine a target visualization tool recommended to the target user according to the statistical frequency information.
The embodiment of the invention provides a visualization tool recommendation processing device, which is used for acquiring user category label data, preprocessing the user category label data and clustering sample data in a sample set according to the preprocessed user category label data; determining user category labels corresponding to each cluster as a clustering result, and determining a target cluster to which a target user of visual recommendation belongs according to the clustering result; and selecting a preset number of cluster users from the target cluster, acquiring statistical frequency information of the visualization tools used by the cluster users, and determining target visualization tools recommended to the target users according to the statistical frequency information, so that the visualization tools recommended to the users can be comprehensively and accurately determined based on user types.
Further, the clustering unit 301 is specifically configured to:
digitally tagging the user category tag data;
and performing dimension reduction processing on the digitally marked user category label data, and taking the dimension-reduced data as preprocessed user category label data.
The visualization tool recommendation processing device provided by the embodiment of the invention further improves the accuracy of visualization tool recommendation by removing the user category with strong correlation.
Further, the clustering unit 301 is specifically configured to:
and clustering the sample data in the sample set according to the preprocessed user category label data by adopting a K-means method.
The visualization tool recommendation processing device provided by the embodiment of the invention can further improve the clustering processing efficiency through the K-means method.
Further, the determining unit 302 is specifically configured to:
and acquiring target user category label data of the target user, and taking the cluster group which is the same as the target user category label data in the clustering result as the target cluster group.
The visualization tool recommendation processing device provided by the embodiment of the invention can directly and rapidly acquire the target cluster.
Further, the determining unit 302 is specifically configured to:
and selecting a neighbor of the target user from the clustering result by adopting a KNN method, and determining a target cluster group to which the target user belongs according to the neighbor.
The visualization tool recommendation processing device provided by the embodiment of the invention can further obtain the target cluster group efficiently.
Further, the recommending unit 303 is specifically configured to:
and averagely selecting cluster users with total number equal to the preset number from the target cluster.
The visualization tool recommendation processing device provided by the embodiment of the invention further improves the accuracy of visualization tool recommendation by selecting cluster users on average.
Further, the recommending unit 303 is specifically further configured to:
and respectively calculating the sum of the number of the statistical frequencies respectively corresponding to each visualization tool, and determining the visualization tool with the largest sum value of the number of the statistical frequencies as the target visualization tool.
The visualization tool recommendation processing device provided by the embodiment of the invention further improves the accuracy of visualization tool recommendation in an intuitive quantification mode.
The embodiment of the present invention provides a visualization tool recommendation processing device, which can be specifically used to execute the processing flow of each method embodiment, and the functions of the visualization tool recommendation processing device are not described herein, and reference may be made to the detailed description of the method embodiments.
Fig. 4 is a schematic diagram of an entity structure of an electronic device according to an embodiment of the present invention, as shown in fig. 4, where the electronic device includes: a processor (processor) 401, a memory (memory) 402, and a bus 403;
wherein, the processor 401 and the memory 402 complete the communication with each other through the bus 403;
the processor 401 is configured to call the program instructions in the memory 402 to perform the methods provided in the above method embodiments, for example, including:
acquiring user category label data, preprocessing the user category label data, and clustering sample data in a sample set according to the preprocessed user category label data;
determining user category labels corresponding to each cluster as a clustering result, and determining a target cluster to which a target user of visual recommendation belongs according to the clustering result;
and selecting a preset number of cluster users from the target cluster, acquiring statistical frequency information of the visualization tools used by the cluster users, and determining target visualization tools recommended to the target users according to the statistical frequency information.
The present embodiment discloses a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, are capable of performing the methods provided by the above-described method embodiments, for example comprising:
acquiring user category label data, preprocessing the user category label data, and clustering sample data in a sample set according to the preprocessed user category label data;
determining user category labels corresponding to each cluster as a clustering result, and determining a target cluster to which a target user of visual recommendation belongs according to the clustering result;
and selecting a preset number of cluster users from the target cluster, acquiring statistical frequency information of the visualization tools used by the cluster users, and determining target visualization tools recommended to the target users according to the statistical frequency information.
The present embodiment provides a computer-readable storage medium storing a computer program that causes the computer to execute the methods provided by the above-described method embodiments, for example, including:
acquiring user category label data, preprocessing the user category label data, and clustering sample data in a sample set according to the preprocessed user category label data;
determining user category labels corresponding to each cluster as a clustering result, and determining a target cluster to which a target user of visual recommendation belongs according to the clustering result;
and selecting a preset number of cluster users from the target cluster, acquiring statistical frequency information of the visualization tools used by the cluster users, and determining target visualization tools recommended to the target users according to the statistical frequency information.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In the description of the present specification, reference to the terms "one embodiment," "one particular embodiment," "some embodiments," "for example," "an example," "a particular example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. A visualization tool recommendation processing method, comprising:
acquiring user category label data, preprocessing the user category label data, and clustering sample data in a sample set according to the preprocessed user category label data;
determining user category labels corresponding to each cluster as a clustering result, and determining a target cluster to which a target user of visual recommendation belongs according to the clustering result;
and selecting a preset number of cluster users from the target cluster, acquiring statistical frequency information of the visualization tools used by the cluster users, and determining target visualization tools recommended to the target users according to the statistical frequency information.
2. The visualization tool recommendation processing method according to claim 1, wherein the preprocessing the user category label data includes:
digitally tagging the user category tag data;
and performing dimension reduction processing on the digitally marked user category label data, and taking the dimension-reduced data as preprocessed user category label data.
3. The visualization tool recommendation processing method according to claim 1, wherein the clustering processing of the sample data in the sample set according to the preprocessed user category label data includes:
and clustering the sample data in the sample set according to the preprocessed user category label data by adopting a K-means method.
4. A visualization tool recommendation processing method according to any one of claims 1 to 3, wherein the determining, according to the clustering result, a target cluster group to which a target user of a visualization recommendation belongs includes:
and acquiring target user category label data of the target user, and taking the cluster group which is the same as the target user category label data in the clustering result as the target cluster group.
5. A visualization tool recommendation processing method according to any one of claims 1 to 3, wherein the determining, according to the clustering result, a target cluster group to which a target user of a visualization recommendation belongs includes:
and selecting a neighbor of the target user from the clustering result by adopting a KNN method, and determining a target cluster group to which the target user belongs according to the neighbor.
6. A visualization tool recommendation processing method according to any one of claims 1 to 3, wherein the selecting a preset number of cluster users from the target cluster group includes:
and averagely selecting cluster users with total number equal to the preset number from the target cluster.
7. The visualization tool recommendation processing method according to claim 6, wherein the determining a target visualization tool recommended to the target user based on the statistical frequency information comprises:
and respectively calculating the sum of the number of the statistical frequencies respectively corresponding to each visualization tool, and determining the visualization tool with the largest sum value of the number of the statistical frequencies as the target visualization tool.
8. A visualization tool recommendation processing device, comprising:
the clustering unit is used for acquiring user category label data, preprocessing the user category label data and clustering sample data in a sample set according to the preprocessed user category label data;
the determining unit is used for determining user category labels corresponding to each cluster as a clustering result, and determining a target cluster group to which a target user of the visual recommendation belongs according to the clustering result;
and the recommending unit is used for selecting a preset number of cluster users from the target cluster, acquiring the statistical frequency information of the visualization tools used by the cluster users, and determining the target visualization tools recommended to the target users according to the statistical frequency information.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of any one of claims 1 to 7 when the computer program is executed by the processor.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any one of claims 1 to 7.
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| CN202310413908.4A CN116521989A (en) | 2023-04-18 | 2023-04-18 | Visual tool recommendation processing method and device |
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