CN111950907B

CN111950907B - Information generation method, apparatus, electronic device and computer readable medium

Info

Publication number: CN111950907B
Application number: CN202010814280.5A
Authority: CN
Inventors: 韩东亮; 徐诚浪
Original assignee: Zhongcheng Information Services Shenzhen Co ltd
Current assignee: Zhongcheng Information Services Shenzhen Co ltd
Priority date: 2020-08-13
Filing date: 2020-08-13
Publication date: 2024-01-16
Anticipated expiration: 2040-08-13
Also published as: CN111950907A

Abstract

Embodiments of the present disclosure disclose an information generation method, an apparatus, an electronic device, and a computer readable medium. One embodiment of the method comprises the following steps: acquiring a user tag information set and an article tag information set; generating a normalized user tag score value set and a normalized article tag score value set based on the user tag information set and the article tag information set, respectively; generating a user tag weight based on each normalized user tag score value in the normalized user tag score value set and a user tag usage frequency value corresponding to the normalized user tag score value; generating an item tag weight based on each normalized item tag score value in the normalized item tag score value set and an item tag usage frequency value corresponding to the normalized item tag score value; a user item tag table is generated based on the user tag weight set and the item tag weight set. This embodiment helps the system to generate a user item tag table reasonably.

Description

Information generation method, apparatus, electronic device and computer readable medium

Technical Field

Embodiments of the present disclosure relate to the field of computer technology, and in particular, to an information generating method, an apparatus, an electronic device, and a computer readable medium.

Background

With the development of internet technology and the arrival of the age of electronic commerce, various user article labels appear on the market. The computing device may push item information to the user through the user item tag. It is desirable to generate a list of user item tags reasonably so as to facilitate adjustment of the user item tags.

Disclosure of Invention

The disclosure is in part intended to introduce concepts in a simplified form that are further described below in the detailed description. The disclosure is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

Some embodiments of the present disclosure propose an information generation method, apparatus, electronic device, and computer readable medium to solve the technical problems mentioned in the background section above.

In a first aspect, some embodiments of the present disclosure provide an information generating method, the method including: acquiring a user tag information set and an article tag information set associated with the user tag information set, wherein the user tag information comprises a user tag name, a user tag grading value corresponding to the user tag name and a user tag use frequency value corresponding to the user tag grading value, and the article tag information comprises an article tag name, an article tag grading value corresponding to the article tag name and an article tag use frequency value corresponding to the article tag grading value; generating a normalized user tag score value set and a normalized article tag score value set based on the user tag information set and the article tag information set, respectively; generating user tag weights based on each normalized user tag score value in the normalized user tag score value set and a user tag use frequency value corresponding to the normalized user tag score value to obtain a user tag weight set; generating article tag weights based on each normalized article tag score value in the normalized article tag score value set and an article tag use frequency value corresponding to the normalized article tag score value, thereby obtaining an article tag weight set; and generating a user object tag table based on the user tag weight set and the object tag weight set.

In a second aspect, some embodiments of the present disclosure provide an information generating apparatus, the apparatus including: an acquisition unit configured to acquire a set of user tag information including a user tag name, a user tag score value corresponding to the user tag name, and a user tag use frequency value corresponding to the user tag score value, and a set of item tag information associated with the set of user tag information including an item tag name, an item tag score value corresponding to the item tag name, and an item tag use frequency value corresponding to the item tag score value; a first generation unit configured to generate a normalized user tag score value set and a normalized article tag score value set, respectively, based on the user tag information set and the article tag information set; a second generating unit configured to generate a user tag weight based on each normalized user tag score value in the normalized user tag score value set and a user tag use frequency value corresponding to the normalized user tag score value, to obtain a user tag weight set; a third generating unit configured to generate an article tag weight based on each normalized article tag score value in the normalized article tag score value set and an article tag use frequency value corresponding to the normalized article tag score value, to obtain an article tag weight set; and a fourth generation unit configured to generate a user article tag table based on the user tag weight set and the article tag weight set.

In some embodiments, the generating the item tag weight based on each normalized item tag score value in the set of normalized item tag score values and an item tag frequency of use value corresponding to the normalized item tag score value includes:

determining the quantity of item tag information included in the item tag information set;

determining a sum of article tag usage frequency values of each article tag information in the article tag information set;

inputting the normalized item tag score value, the sum of item tag use frequency values of the individual item tag information, the item tag use frequency value corresponding to the item tag score value, and the number of item tag information included in the item tag information set into the following formula to generate an item tag weight:

wherein,item tag weight indicating item tag information including the item tag score value, +.>Representing the normalized item tag score value, +.>Indicating the frequency value of use of the item tag corresponding to the item tag score value,/for>The sum of the item tag usage frequency values representing the individual item tag information, +. >Representing the number of item tag information included in the item tag information set,/item tag information>Representing a parameter having a value of 0.081819.

In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors, cause the one or more processors to implement the method as described in the first aspect.

In a fourth aspect, some embodiments of the present disclosure provide a computer readable medium having a computer program stored thereon, wherein the program when executed by a processor implements the method as described in the first aspect.

One of the above embodiments of the present disclosure has the following advantageous effects: firstly, respectively carrying out normalization processing on a user label score value of each user label information in a user label information set and an article label score value of each article label information in an article label information set to generate a normalized user label score value and a normalized article label score value, and obtaining a normalized user label score value set and a normalized article label score value set. The numerical value is normalized, so that the accuracy of a calculation result can be improved, and the calculation of data is facilitated. And then, the executing body can carry out numerical processing on each normalized user tag score value in the normalized user tag score value set and the user tag using frequency value corresponding to the normalized user tag score value to generate user tag weight, so as to obtain a user tag weight set. And carrying out numerical processing on each normalized article label scoring value in the normalized article label scoring value set and the article label using frequency value corresponding to the normalized article label scoring value to generate article label weights, thereby obtaining an article label weight set. Optionally, the executing body may build a user tag weight matrix by sorting the user tag weights in the user tag weight set, and may determine the importance of each user tag in the tag set of the whole user. Then, by ordering the individual item tag weights in the item tag weight set, an item tag weight matrix is established, the importance of each item tag in the tag set for the entire item can be determined. And multiplying the user tag weight matrix and the article tag weight matrix to obtain the user article tag weight matrix. And inputting the user article label weight matrix into the user article label blank table to obtain the user article label table. The influence of the user tag and the article tag on the user can be comprehensively considered through the user article tag table. Thus, the executing body can adjust the user article tag according to the weight of each user article tag in the user article tag table. Thus, the system is facilitated to provide customized services to the user.

Drawings

The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.

FIG. 1 is a schematic illustration of one application scenario of an information generation method according to some embodiments of the present disclosure;

FIG. 2 is a flow chart of some embodiments of an information generation method according to the present disclosure;

FIG. 3 is a flow chart of other embodiments of information generation methods according to the present disclosure;

FIG. 4 is a schematic structural diagram of some embodiments of an information generating apparatus according to the present disclosure;

fig. 5 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.

Description of the embodiments

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.

It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings. Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.

It should be noted that the terms "first," "second," "third," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.

The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

Fig. 1 is a schematic diagram of an application scenario of an information generation method according to some embodiments of the present disclosure.

In the application scenario of fig. 1, first, computing device 101 may generate normalized user tag score value set and normalized item tag score value set 104 from user tag information set 102 and item tag information set 103. Second, computing device 101 may generate user tag weight set 105 and item tag weight set 106 from normalized user tag score value set and normalized item tag score value set 104. The computing device 101 may then generate a user item tag table 107 from the user tag weight set 105 and the item tag weight set 106. Finally, optionally, the computing device 101 may output the user item tag table 107 on the display screen 108.

The computing device 101 may be hardware or software. When the computing device is hardware, the computing device may be implemented as a distributed cluster formed by a plurality of servers or terminal devices, or may be implemented as a single server or a single terminal device. When the computing device is embodied as software, it may be installed in the hardware devices listed above. It may be implemented as a plurality of software or software modules, for example, for providing distributed services, or as a single software or software module. The present invention is not particularly limited herein.

It should be understood that the number of computing devices in fig. 1 is merely illustrative. There may be any number of computing devices, as desired for an implementation.

With continued reference to fig. 2, a flow 200 of some embodiments of information generation methods according to the present disclosure is shown. The method may be performed by the computing device 101 in fig. 1. The information generating method comprises the following steps:

step 201, acquiring a user tag information set and an article tag information set associated with the user tag information set.

In some embodiments, an execution subject (e.g., a computing device shown in fig. 1) for the information generating method may acquire the user tag information set and the item tag information set associated with the user tag information set from the terminal through a wired connection or a wireless connection. The user tag information includes a user tag number, a user tag score value corresponding to the user tag number, and a user tag use frequency value corresponding to the user tag score value. The article tag information includes an article tag name, an article tag score value corresponding to the article tag name, and an article tag use frequency value corresponding to the article tag score value.

As an example, the above-described user tag information set may be "{ purchase cosmetics; 5 minutes; 12 times, { motion; 4, dividing; 10 times }). The article tag information set is "{ di; 4, dividing; 15 times } { exercise wheel; 5 minutes; 11 times }).

Step 202, generating a normalized user tag score value set and a normalized article tag score value set based on the user tag information set and the article tag information set, respectively.

In some embodiments, the executing body may perform normalization processing on each user tag score value in the user tag information set and each item tag score value in the item tag information set to generate a normalized user tag score value and a normalized item tag score value, to obtain a normalized user tag score value set and a normalized item tag score value set.

As an example, each user tag score value in the above-described user tag information set may be "2,3,4,1,5". And carrying out normalization processing on each user tag score value in the user tag information set to obtain a normalized user tag score value set which is 0.13,0.2,0.27,0.07,0.33. Each item tag score value in the item tag information set may be "3,3,4,2,5". And normalizing the item label scoring values in the item label information set to obtain a normalized item label scoring value set which is 0.17,0.17,0.23,0.18,0.29. Here, the normalized value takes two bits after the decimal point.

In some optional implementations of some embodiments, the executing entity may generate the normalized user tag score value set and the normalized item tag score value set by:

the first step, sorting the user tag score values of the user tag information in the user tag information set to obtain a user tag score value sequence. The manner of ordering is not limited herein.

As an example, each user tag score value in the above-described user tag information set may be "2,3,4,1,5". Sequencing the scoring values of all the user labels in the user label information set according to the numerical value from large to small, and obtaining a scoring value sequence of the user labels comprises the following steps: {5,4,3,2,1}.

And secondly, carrying out difference processing on every two adjacent user tag score values in the user tag score value sequence to generate a user tag score difference value, and obtaining a user tag score difference value set.

As an example, the above-described user tag score value sequence may be {5,4,3,2,1}. Performing difference processing on every two adjacent user tag score values in the user tag score value sequence to obtain a user tag score difference value set as follows: {1,1,1,1}.

And thirdly, determining the average value of the scoring differences of all the user labels in the scoring difference set of the user labels.

As an example, the set of user tag score differences described above may be: {1,1,1,1}. The average value of the individual user tag score differences in the set of user tag score differences may be determined to be "1".

And step four, selecting the user tag score value which is larger than or equal to a first preset threshold value from the user tag score value sequence as a first user tag score value, and obtaining a first user tag score value group. Here, the setting of the first predetermined threshold value is not limited.

As an example, the above-described user tag score value sequence may be {5,4,3,2,1}. The first predetermined threshold may be "3". Selecting a user tag score value greater than or equal to a first preset threshold value from the user tag score value sequence, and obtaining a first user tag score value group as follows: {5,4,3}.

And fifthly, carrying out summation processing on the average value of each first user tag score value in the first user tag score value group and the user tag score difference value to generate a summation user tag score value, and obtaining a summation user tag score value set.

As an example, the first user tag score value group may be {5,4,3}. The mean value of the user tag score differences is "1". And carrying out summation processing on each first user tag score value in the first user tag score value group and the average value of the user tag score difference values to generate a summation user tag score value, and obtaining a summation user tag score value set {6,5,4}.

And sixthly, selecting the user tag score value smaller than the first preset threshold value from the user tag score value sequence as a second user tag score value, and obtaining a second user tag score value group.

As an example, the above-described user tag score value sequence may be {5,4,3,2,1}. The first predetermined threshold may be "3". Selecting a user tag score value smaller than a first preset threshold value from the user tag score value sequence as a second user tag score value, and obtaining a second user tag score value group as follows: {2,1}.

And seventh, carrying out difference processing on each second user tag score value in the second user tag score value group and the average value of the user tag score differences to generate a difference user tag score value, and obtaining a difference user tag score value set.

As an example, the second user tag score value group may be {2,1}. The mean value of the user tag score differences is "1". And carrying out difference processing on each second user tag score value in the second user tag score value group and the average value of the user tag score difference values to generate a difference user tag score value, and obtaining a difference user tag score value set {1}. Here, the value of the difference user tag score value of zero is cleared by default.

And eighth step, combining the sum user tag grading value set and the difference user tag grading value set to generate a user tag grading value set to be processed.

As an example, the set of summed user tag score values described above may be {6,5,4}. The set of difference user tag score values may be {1}. Combining the sum user tag score value set and the difference user tag score value set to generate a user tag score value set to be processed as follows: {6,5,4,1}.

And ninth, normalizing each to-be-processed user tag score value in the to-be-processed user tag score value set to generate to-be-processed normalized user tag score values as normalized user tag score values, so as to obtain a normalized user tag score value set.

As an example, the set of user tag score values to be processed may be {6,5,4,1}. Normalizing each user label score value in the user label score value set to be processed to generate normalized user label score values to be processed, wherein the normalized user label score value set is obtained by: {0.375,0.3125,0.25,0.0625}.

And tenth, sorting the item label grading values of the item label information in the item label information set to obtain an item label grading value sequence.

As an example, each item tag score value in the above item tag information set may be "2,3,4,1,5". Sorting the item label scoring values in the item label information set according to the numerical value from big to small, wherein the obtained item label scoring value sequence is as follows: {5,4,3,2,1}.

And eleventh step, carrying out difference processing on every two adjacent item label grading values in the item label grading value sequence to generate item label grading difference values, and obtaining an item label grading difference value set.

As an example, the item tag score value sequence may be {5,4,3,2,1}. Performing difference processing on each two adjacent item label scoring values in the item label scoring value sequence to obtain an item label scoring difference value set, wherein the item label scoring difference value set is as follows: {1,1,1,1}.

And twelfth, determining the average value of the item label scoring differences in the item label scoring difference set.

As an example, the item tag score difference set described above may be: {1,1,1,1}. The average value of the individual item tag score differences in the set of item tag score differences may be determined to be "1".

Thirteenth, selecting an item tag score value greater than or equal to a second predetermined threshold value from the item tag score value sequence as a first item tag score value, and obtaining a first item tag score value group. Here, the setting of the second predetermined threshold value is not limited.

As an example, the item tag score value sequence may be {5,4,3,2,1}. The first predetermined threshold may be "3". Selecting an item tag score value greater than or equal to a second preset threshold value from the item tag score value sequence, and obtaining a first item tag score value group as follows: {5,4,3}.

And fourteenth step, summing the average value of each first item tag score value in the first item tag score value group and the item tag score difference value to generate a summed item tag score value, and obtaining a summed item tag score value set.

As an example, the first item tag credit value group may be {5,4,3}. The average value of the item tag score differences is "1". And carrying out summation processing on the average value of each first item label grading value in the first item label grading value group and the item label grading difference value to generate a summation item label grading value, and obtaining a summation item label grading value set {6,5,4}.

And fifteenth, selecting an item label grading value smaller than a second preset threshold value from the item label grading value sequence as a second item label grading value, and obtaining a second item label grading value group.

As an example, the item tag score value sequence may be {5,4,3,2,1}. The first predetermined threshold may be "3". Selecting an item tag score value smaller than a second preset threshold value from the item tag score value sequence as a second item tag score value, and obtaining a second item tag score value group as follows: {2,1}.

Sixteenth, performing difference processing on the average value of each second article tag score value in the second article tag score value group and the article tag score difference value to generate a difference article tag score value, and obtaining a difference article tag score value set.

As an example, the second item tag score value group may be {2,1}. The average value of the item tag score differences is "1". And carrying out difference processing on each second article label grading value in the second article label grading value group and the average value of the article label grading difference values to generate a difference article label grading value, wherein the obtained difference article label grading value set is {1}.

Seventeenth, combining the sum item label grading value set and the difference item label grading value set to generate an item label grading value set to be processed.

As an example, the set of summed item tag score values described above may be {6,5,4}. The set of differential item tag score values may be {1}. Combining the sum item tag score value set and the difference item tag score value set to generate an item tag score value set to be processed as follows: {6,5,4,1}.

Eighteenth, normalizing each item label score value in the item label score value set to be processed to generate a normalized item label score value to be processed as a normalized item label score value, thereby obtaining a normalized item label score value set.

As an example, the set of item tag score values to be processed may be {6,5,4,1}. Normalizing each item label score value in the item label score value set to be processed to generate a normalized item label score value, wherein the obtained normalized item label score value set is as follows: {0.375,0.3125,0.25,0.0625}.

Step 203, generating a user tag weight based on each normalized user tag score value in the normalized user tag score value set and a user tag use frequency value corresponding to the normalized user tag score value, thereby obtaining a user tag weight set.

In some embodiments, the executing entity may determine the number of user tag information included in the set of user tag information. Then, the number of user tag information included in the set of user tag information, each normalized user tag score value in the set of normalized user tag score values, and a user tag usage frequency value corresponding to the normalized user tag score value may be input to the following equation to generate a user tag weight:

wherein,indicate->User tag weight of individual user tag information. />Indicate->Normalized user tag score value for individual user tag information. />Indicate->The user tag of the individual user tag information uses the frequency value. />Representing the number of user tag information included in the set of user tag information. />Indicate->Normalized user tag score value for individual user tag information. />Indicate->The user tag of the individual user tag information uses the frequency value. Here, the range of the decimal point post-value of the user tag weight is not limited.

As an example, each normalized user tag score value in the set of normalized user tag score values described above may be {0.375,0.3125}. The number of user tag information included in the user tag information set is "2". The user tag usage frequency value corresponding to each normalized user tag score value in the normalized user tag score value set may be "6,5". The numerical values are respectively input into a formula to generate user tag weights:

。

The resulting set of user tag weights is 0.59,0.41.

Step 204, generating an article tag weight based on each normalized article tag score value in the normalized article tag score value set and an article tag use frequency value corresponding to the normalized article tag score value, and obtaining an article tag weight set.

In some embodiments, the executing entity may determine the number of item tag information included in the item tag information set. Inputting the number of item tag information included in the item tag information set, each normalized item tag score value in the normalized item tag score value set, and an item tag usage frequency value corresponding to the normalized item tag score value to the following to generate an item tag weight:

。

wherein,indicate->Item tag weight of individual item tag information. />Indicate->Normalized item tag score value for individual item tag information. />Indicate->The article tag use frequency value of the article tag information. />The number of item tag information items included in the item tag information item set is represented. />Indicate->Normalized item tag score value for individual item tag information. / >Indicate->The article tag use frequency value of the article tag information. Here, the range of the post-decimal value of the article tag weight is not limited.

As an example, each normalized item tag score value in the set of normalized item tag score values described above may be {0.375,0.3125}. The number of item tag information included in the item tag information set is "2". The article tag usage frequency value corresponding to each normalized article tag score value in the normalized article tag score value set may be "4,6". Inputting the above values into formulas respectively to generate article tag weights:

。

the resulting item tag weight set is {0.44,0.56}.

Step 205, generating a user article tag table based on the user tag weight set and the article tag weight set.

In some embodiments, the executing entity may generate the user tag table by:

the first step, each user tag weight in the user tag weight set and the article tag weight in the article tag weight set corresponding to the user tag weight are combined to generate a binary group, and a binary group set is obtained.

As an example, the set of user tag weights described above may be {0.59,0.41}. The article tag weight set is {0.44,0.56}. Combining each user tag weight in the user tag weight set with the article tag weight in the article tag weight set corresponding to the user tag weight to generate a binary group, and obtaining a binary group set as follows: { (0.59,0.44); (0.41,0.56) }.

And secondly, establishing a user article label blank table, and inputting each binary group in the binary group set into the user article label blank table to generate a user article label table.

As an example, a set of tuples may be { (0.59,0.44); (0.41,0.56) }. Establishing a user article tag blank table, inputting each two-tuple in the two-tuple set into the user article tag blank table, and generating a user article tag table:

。

in some optional implementations of some embodiments, the executing entity may further generate the user item tag table by:

first, constructing a user tag weight matrix according to the user tag weight set. Here, the method of constructing the matrix is not limited.

As an example, the set of user tag weights described above may be {0.59,0.41}. Sequencing all user tag weights in the user tag weight set according to the numerical value from large to small to obtain a user tag weight sequence {0.59;0.41}. Constructing a user tag weight matrix according to the sequence of the user tag weights in the user tag weight sequence to obtain:。

and secondly, constructing an article tag weight matrix according to the article tag weight set.

As an example, the item tag weight set may be {0.56,0.44}. Sequencing all the article tag weights in the article tag weight set according to the numerical value from big to small to obtain an article tag weight sequence {0.56;0.44}.

And thirdly, multiplying the user tag weight matrix with the article tag weight matrix to generate the user article tag weight matrix.

As an example, the user tag weight matrix may be:. The article tag weight matrix may be: />. Multiplying the two matrixes to obtain a user article label weight matrix:

。

fourth, a user article label blank table is established, each user article label weight in the user article label weight matrix is input into the user article label blank table, and a user article label table is generated.

As an example, a user article tag blank table is established, and each user article tag weight in the user article tag weight matrix is input into the user article tag blank table to generate a user article tag table. Here, the manner of creating the empty list of the user article tag is not limited.

。

Optionally, the display device of the control communication connection displays the user article tag table, so that the operation device can adjust the user article tag based on the user article tag table.

As an example, a display device controlling the communication connection displays the above-described user item tag table. The operation device can adjust the label name of the user article according to the weight of each user article label in the label list of the user article. For example, the image representing the user item tag name corresponding to the user item tag weight is resized or repositioned.

In some optional implementations of some embodiments, the executing entity may establish the user tag weight matrix by ordering individual user tag weights in the user tag weight set. Thus, the importance of each user tag in the overall user's tag set can be determined. Then, by sorting the individual item tag weights in the item tag weight set, an item tag weight matrix is established, which can show the importance of each item tag in the tag set of the entire item. Multiplying the user tag weight matrix and the article tag weight matrix to obtain the user article tag weight matrix. And inputting the user article label weight matrix into the user article label blank table to obtain the user article label table. The influence of the two aspects of the user tag and the article tag on the user can be comprehensively considered through the user article tag table. Finally, the executing body can adjust the user article tag according to the weight of each user article tag in the user article tag table.

One of the above embodiments of the present disclosure has the following advantageous effects: firstly, respectively carrying out normalization processing on a user label score value of each user label information in a user label information set and an article label score value of each article label information in an article label information set to generate a normalized user label score value and a normalized article label score value, and obtaining a normalized user label score value set and a normalized article label score value set. The normalization processing is carried out, so that the accuracy of a calculation result can be improved, and the data can be calculated conveniently. And then, the executing body can carry out numerical processing on each normalized user tag score value in the normalized user tag score value set and the user tag using frequency value corresponding to the normalized user tag score value to generate user tag weight, so as to obtain a user tag weight set. And carrying out numerical processing on each normalized article label scoring value in the normalized article label scoring value set and the article label using frequency value corresponding to the normalized article label scoring value to generate article label weights, thereby obtaining an article label weight set. Optionally, the executing body may build a user tag weight matrix by sorting the user tag weights in the user tag weight set, and may determine the importance of each user tag in the tag set of the whole user. Then, by ordering the individual item tag weights in the item tag weight set, an item tag weight matrix is established, the importance of each item tag in the tag set for the entire item can be determined. And multiplying the user tag weight matrix and the article tag weight matrix to obtain the user article tag weight matrix. And inputting the user article label weight matrix into the user article label blank table to obtain the user article label table. The influence of the user tag and the article tag on the user can be comprehensively considered through the user article tag table. Finally, the executing body can adjust the user article tag according to the weight of each user article tag in the user article tag table. Thus, the system is facilitated to provide customized services to the user.

With further reference to fig. 3, a flow 300 of further embodiments of the information generation method according to the present disclosure is shown. The method may be performed by the computing device 101 of fig. 1. The information generating method comprises the following steps:

step 301, acquiring a user tag information set and an article tag information set associated with the user tag information set.

Step 302, generating a normalized user tag score value set and a normalized article tag score value set based on the user tag information set and the article tag information set, respectively.

In some embodiments, the specific implementation manner and the technical effects of steps 301 to 302 may refer to steps 201 to 202 in those embodiments corresponding to fig. 2, which are not described herein.

Step 303, determining the number of user tag information included in the user tag information set.

In some embodiments, the executing entity may directly determine the number of user tag information included in the user tag information set.

As an example, the above-described user tag information set may be "{ purchase cosmetics; 5 minutes; 12 times, { motion; 4, dividing; 10 times }). It is determined that the number of user tag information included in the user tag information set is "2".

Step 304, determining a sum of user tag usage frequency values of the user tag information in the user tag information set.

In some embodiments, the executing body may perform addition processing on the user tag usage frequency value of each user tag information in the user tag information set to obtain a sum of the user tag usage frequency values.

As an example, the above-described user tag information set may be "{ purchase cosmetics; 5 minutes; 12 times, { motion; 4, dividing; 10 times }). The frequency value of each user tag in the user tag information set is 12;10". The sum of the usage frequency values of the respective user tags is "22".

And step 305, inputting the normalized user tag score value, the sum of the user tag use frequency values, the user tag use frequency value corresponding to the normalized user tag score value, and the number of user tag information included in the user tag information set into a formula to generate a user tag weight.

In some embodiments, the execution body may input the normalized user tag score value, a sum of the user tag usage frequency values of the respective user tag information, the user tag usage frequency value corresponding to the normalized user tag score value, and the number of the user tag information included in the user tag information set to the following formula to generate the user tag weight:

。

Wherein,and user tag weights representing user tag information including the user tag score values. />Representing the normalized user tag score value. />And a user tag use frequency value corresponding to the user tag score value. />The sum of the user tag usage frequency values representing the respective user tag information. />Representing the users included in the user tag information setsThe amount of tag information.

As an example, the normalized user tag score described aboveMay be "0.59". User tag use frequency value corresponding to the above user tag score value +.>May be "12". Sum of user tag usage frequency values of the respective user tag informationIs "22". The number of user tag information included in the user tag information set>Is "2". Inputting the above values into a formula to generate user tag weights:

。

step 306, determining the quantity of the article tag information included in the article tag information set.

In some embodiments, the executing entity may directly count the number of item tag information included in the item tag information set.

As an example, the item tag information set described above may be "{ di; 4, dividing; 15 times } { exercise wheel; 5 minutes; 11 times }). Counting the number of item tag information included in the item tag information set as "2".

Step 307, determining a sum of article tag usage frequency values of the respective article tag information in the article tag information set.

In some embodiments, the execution body may add the article tag usage frequency values of the respective article tag information in the article tag information set to obtain a sum of the respective article tag usage frequency values.

As an example, the item tag information set described above may be "{ di; 4, dividing; 15 times } { exercise wheel; 5 minutes; 11 times }). The article tag use frequency value of each article tag information in the article tag information set is "15;11". The sum of the article tag use frequency values of the respective article tag information is "26".

Step 308, inputting the normalized item tag score value, the sum of the item tag use frequency values, the item tag use frequency value corresponding to the normalized item tag score value, and the number of item tag information included in the item tag information set into a formula to generate an item tag weight.

In some embodiments, the execution body may input the normalized item tag score value, a sum of item tag usage frequency values of the respective item tag information, the item tag usage frequency value corresponding to the normalized item tag score value, and the number of item tag information included in the item tag information set into the following formula to generate the item tag weight:

，

Wherein,item tag weight indicating item tag information including the item tag score value, +.>Representing the normalized article tag score value, +.>Indicating the frequency value of use of the article tag corresponding to the article tag score value, and +.>The sum of the article tag use frequency values representing the above-mentioned individual article tag information, +.>Representing the number of item tag information included in the item tag information set,/item tag information>Representing a parameter having a value of 0.081819. Here, a->The value range of (1, 0) is changed according to the quantity of the article label information included in the article label information set. The greater the number +.>The larger the value of +.>The initial value of (2) is not limited in the value range.

As an example, the normalized item tag score values described aboveMay be "0.56". Item tag use frequency value corresponding to the item tag score value>May be "15". Sum of item tag usage frequency values of respective item tag information +.>Is "26". The number of item tag information included in the item tag information set>Is "2". Inputting the above values into a formula to generate item tag weights:

。

step 309, generating a user article tag table based on the user tag weight set and the article tag weight set.

In some embodiments, the specific implementation manner of step 309 and the technical effects thereof may refer to step 205 in those embodiments corresponding to fig. 2, which are not described herein.

One of the above embodiments of the present disclosure has the following advantageous effects: first, the execution body may determine a sum of the number of pieces of user tag information included in the user tag information sets and the frequency value of use of each user tag in the user tag information sets. Thus, the weight of the tag is calculated in consideration of the mutual influence between the individual user tag names in the user tag information set. And then inputting the normalized user tag score value, the sum of the user tag use frequency values, the user tag use frequency value and the number of the user tag use frequency values into a formula to generate user tag weights. The user tag weight generated through the formula improves the accuracy of the user tag weight and is beneficial to adjusting the user tag name. And similarly, the article label weight generated by the formula improves the accuracy of the article label weight and is beneficial to adjusting the article label name.

With further reference to fig. 4, as an implementation of the method described above for each of the above figures, the present disclosure provides some embodiments of an information generating apparatus, which correspond to those described above for fig. 2, and which are particularly applicable in various electronic devices.

As shown in fig. 4, the information generating apparatus 400 of some embodiments includes: an acquisition unit 401, a first generation unit 402, a second generation unit 403, a third generation unit 404, and a fourth generation unit 405. Wherein the obtaining unit 401 is configured to obtain a set of user tag information and a set of item tag information associated with the set of user tag information, where the user tag information includes a user tag name, a user tag score value corresponding to the user tag name, and a user tag use frequency value corresponding to the user tag score value, and the item tag information includes an item tag name, an item tag score value corresponding to the item tag name, and an item tag use frequency value corresponding to the item tag score value. The first generating unit 402 is configured to generate a normalized user tag score value set and a normalized article tag score value set, respectively, based on the user tag information set and the article tag information set. The second generating unit 403 is configured to generate a user tag weight based on each normalized user tag score value in the normalized user tag score value set and a user tag usage frequency value corresponding to the normalized user tag score value set, so as to obtain a user tag weight set. The third generating unit 404 is configured to generate an item tag weight based on each normalized item tag score value in the normalized item tag score value set and an item tag usage frequency value corresponding to the normalized item tag score value set, and obtain an item tag weight set. The fourth generating unit 405 is configured to generate a user item tag table based on the user tag weight set and the item tag weight set.

In some optional implementations of some embodiments, the fourth generation unit 405 of the information generation apparatus 400 is further configured to: constructing a user tag weight matrix according to the user tag weight set; constructing an article tag weight matrix according to the article tag weight set; multiplying the user tag weight matrix with the article tag weight matrix to generate a user article tag weight matrix; and establishing a user article tag blank table, inputting each user article tag weight in the user article tag weight matrix into the user article tag blank table, and generating a user article tag table.

It will be appreciated that the elements described in the apparatus 400 correspond to the various steps in the method described with reference to fig. 2. Thus, the operations, features and resulting benefits described above with respect to the method are equally applicable to the apparatus 400 and the units contained therein, and are not described in detail herein.

Referring now to FIG. 5, a schematic diagram of an electronic device (e.g., computing device 101 of FIG. 1) 500 suitable for use in implementing some embodiments of the disclosure is shown. The server illustrated in fig. 5 is merely an example, and should not be construed as limiting the functionality and scope of use of embodiments of the present disclosure in any way.

As shown in fig. 5, the electronic device 500 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 501, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.

In general, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 507 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 508 including, for example, magnetic tape, hard disk, etc.; and communication means 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 shows an electronic device 500 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead. Each block shown in fig. 5 may represent one device or a plurality of devices as needed.

In particular, according to some embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via the communications device 509, or from the storage device 508, or from the ROM 502. The above-described functions defined in the methods of some embodiments of the present disclosure are performed when the computer program is executed by the processing device 501.

It should be noted that, in some embodiments of the present disclosure, the computer readable medium may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, the computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.

In some implementations, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.

The computer readable medium may be embodied in the apparatus; or may exist alone without being incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring a user tag information set and an article tag information set associated with the user tag information set, wherein the user tag information comprises a user tag name, a user tag grading value corresponding to the user tag name and a user tag use frequency value corresponding to the user tag grading value, and the article tag information comprises an article tag name, an article tag grading value corresponding to the article tag name and an article tag use frequency value corresponding to the article tag grading value; generating a normalized user tag score value set and a normalized article tag score value set based on the user tag information set and the article tag information set, respectively; generating user tag weights based on each normalized user tag score value in the normalized user tag score value set and a user tag use frequency value corresponding to the normalized user tag score value to obtain a user tag weight set; generating article tag weights based on each normalized article tag score value in the normalized article tag score value set and an article tag use frequency value corresponding to the normalized article tag score value, thereby obtaining an article tag weight set; and generating a user object tag table based on the user tag weight set and the object tag weight set.

Computer program code for carrying out operations for some embodiments of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in some embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The described units may also be provided in a processor, for example, described as: a processor includes an acquisition unit, a first generation unit, a second generation unit, a third generation unit, and a fourth generation unit. The names of these units do not limit the unit itself in some cases, and for example, the fourth generation unit may also be described as "a unit that generates a user item tag table based on the above-described user tag weight set and the above-described item tag weight set".

The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.

The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above technical features, but encompasses other technical features formed by any combination of the above technical features or their equivalents without departing from the spirit of the invention. Such as the above-described features, are mutually substituted with (but not limited to) the features having similar functions disclosed in the embodiments of the present disclosure.

Claims

1. An information generation method, comprising:

acquiring a user tag information set and an article tag information set associated with the user tag information set, wherein the user tag information comprises a user tag name, a user tag grading value corresponding to the user tag name and a user tag use frequency value corresponding to the user tag grading value, and the article tag information comprises an article tag name, an article tag grading value corresponding to the article tag name and an article tag use frequency value corresponding to the article tag grading value;

generating a normalized user tag score value set and a normalized article tag score value set based on the user tag information set and the article tag information set, respectively;

generating user tag weights based on each normalized user tag score value in the normalized user tag score value set and a user tag use frequency value corresponding to the normalized user tag score value to obtain a user tag weight set;

generating article tag weights based on each normalized article tag score value in the normalized article tag score value set and an article tag use frequency value corresponding to the normalized article tag score value, and obtaining an article tag weight set;

Generating a user article tag table based on the user tag weight set and the article tag weight set;

the display device in control communication connection displays the user article tag list so that the operation device can adjust the user article tag based on the user article tag list, wherein the operation device adjusts the name of the user article tag according to the weight of each user article tag in the user article tag list, and the adjustment comprises: the method comprises the steps of adjusting the size or the position of an image representing a user article label corresponding to the user article label weight;

wherein the generating a normalized user tag score value set and a normalized article tag score value set based on the user tag information set and the article tag information set, respectively, includes:

sorting the user tag score values of the user tag information in the user tag information set to obtain a user tag score value sequence;

performing difference processing on every two adjacent user tag score values in the user tag score value sequence to generate a user tag score difference value, and obtaining a user tag score difference value set;

determining the average value of the scoring differences of all the user labels in the scoring difference set of the user labels;

Selecting a user tag score value which is larger than or equal to a first preset threshold value from the user tag score value sequence as a first user tag score value, and obtaining a first user tag score value group;

summing each first user tag score value in the first user tag score value group and the average value of the user tag score difference values to generate a summed user tag score value, and obtaining a summed user tag score value set;

selecting a user tag score value smaller than a first preset threshold value from the user tag score value sequence as a second user tag score value to obtain a second user tag score value group;

performing difference processing on each second user tag score value in the second user tag score value group and the average value of the user tag score differences to generate a difference user tag score value, and obtaining a difference user tag score value set;

combining the sum user tag score value set and the difference user tag score value set to generate a user tag score value set to be processed;

normalizing each to-be-processed user tag score value in the to-be-processed user tag score value set to generate a to-be-processed normalized user tag score value as a normalized user tag score value, and obtaining a normalized user tag score value set;

Sorting the item label scoring values of each item label information in the item label information set to obtain an item label scoring value sequence;

performing difference processing on every two adjacent item label grading values in the item label grading value sequence to generate item label grading difference values, and obtaining an item label grading difference value set;

determining the average value of the scoring differences of each item label in the scoring difference set of item labels;

selecting an item tag score value which is larger than or equal to a second preset threshold value from the item tag score value sequence as a first item tag score value, and obtaining a first item tag score value group;

summing each first item tag score value in the first item tag score value group and the average value of the item tag score difference values to generate a summed item tag score value, and obtaining a summed item tag score value set;

selecting an item tag score value smaller than a second preset threshold value from the item tag score value sequence as a second item tag score value to obtain a second item tag score value group;

performing difference processing on each second article tag score value in the second article tag score value group and the average value of the article tag score differences to generate a difference article tag score value, and obtaining a difference article tag score value set;

Combining the sum item tag grading value set and the difference item tag grading value set to generate an item tag grading value set to be processed;

normalizing each item label score value in the item label score value set to be processed to generate a normalized item label score value to be processed as a normalized item label score value, thereby obtaining a normalized item label score value set.

2. The method of claim 1, wherein the generating a user item tag table based on the set of user tag weights and the set of item tag weights comprises:

constructing a user tag weight matrix according to the user tag weight set;

constructing an article tag weight matrix according to the article tag weight set;

multiplying the user tag weight matrix with the article tag weight matrix to generate a user article tag weight matrix;

and establishing a user article tag blank table, inputting each user article tag weight in the user article tag weight matrix into the user article tag blank table, and generating a user article tag table.

3. The method of claim 1, wherein the generating a user tag weight based on each normalized user tag score value in the set of normalized user tag score values and a user tag frequency of use value corresponding to the normalized user tag score value comprises:

Determining the number of user tag information included in the user tag information set;

determining a sum of user tag use frequency values of all user tag information in the user tag information set;

inputting the normalized user tag score value, the sum of the user tag use frequency values of the respective user tag information, the user tag use frequency value corresponding to the normalized user tag score value, and the number of user tag information included in the user tag information set into the following formula to generate a user tag weight:

wherein->User tag weight representing user tag information including the user tag score value, +.>Representing the normalized user tag score value, +.>Representing a user tag frequency of use value corresponding to said user tag score value,/for>The sum of the user tag usage frequency values representing the respective user tag information, +.>Representing the number of user tag information included in the set of user tag information.

4. An information generating apparatus comprising:

an acquisition unit configured to acquire a set of user tag information and a set of item tag information associated with the set of user tag information, wherein the user tag information includes a user tag name, a user tag score value corresponding to the user tag name, and a user tag frequency of use value corresponding to the user tag score value, the item tag information includes an item tag name, an item tag score value corresponding to the item tag name, and an item tag frequency of use value corresponding to the item tag score value;

A first generation unit configured to generate a normalized user tag score value set and a normalized article tag score value set, respectively, based on the user tag information set and the article tag information set; the first generation unit is further configured to:

Normalizing each item label score value in the item label score value set to be processed to generate a normalized item label score value to be processed as a normalized item label score value, thereby obtaining a normalized item label score value set;

the second generating unit is configured to generate user tag weights based on each normalized user tag score value in the normalized user tag score value set and a user tag use frequency value corresponding to the normalized user tag score value to obtain a user tag weight set;

a third generating unit configured to generate an article tag weight based on each normalized article tag score value in the normalized article tag score value set and an article tag use frequency value corresponding to the normalized article tag score value, to obtain an article tag weight set;

a fourth generation unit configured to generate a user item tag table based on the user tag weight set and the item tag weight set;

the control unit is configured to control the display device in communication connection to display the user article tag list, so that the operation device can adjust the user article tag based on the user article tag list, wherein the adjustment of the user article tag name by the operation device according to the size of each user article tag weight in the user article tag list comprises the following steps: and adjusting the size or the position of the image representing the user article label corresponding to the user article label weight.

5. An electronic device, comprising:

one or more processors;

a storage device having one or more programs stored thereon;

when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-3.

6. A computer readable medium having stored thereon a computer program, wherein the program when executed by a processor implements the method of any of claims 1-3.