CN111950907A - Information generation method and device, electronic equipment and computer readable medium - Google Patents
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Abstract
The embodiment of the disclosure discloses an information generation method, an information generation device, electronic equipment and a computer readable medium. One embodiment of the method comprises: acquiring a user label information set and an article label information set; respectively generating a normalized user label scoring value set and a normalized article label scoring value set based on the user label information set and the article label information set; generating user tag weight based on each normalized user tag scoring value in the normalized user tag scoring value set and the user tag use frequency value corresponding to the normalized user tag scoring value; generating an article label weight based on each normalized article label scoring value in the normalized article label scoring value set and an article label use frequency value corresponding to the normalized article label scoring value; and generating a user item label table based on the user label weight set and the item label weight set. This embodiment facilitates the system in generating a user item tag table rationally.
Description
Technical Field
Embodiments of the present disclosure relate to the field of computer technologies, and in particular, to an information generation method, an information generation device, an electronic device, and a computer-readable medium.
Background
With the development of internet technology and the arrival of the e-commerce era, 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 user item label table reasonably, thereby facilitating adjustment of user item labels.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary 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 generating 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, 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 score value corresponding to the user tag name and a user tag use frequency value corresponding to the user tag score value, and the article tag information comprises 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; respectively generating a normalized user label scoring value set and a normalized article label scoring value set based on the user label information set and the article label information set; generating user label weights based on each normalized user label scoring value in the normalized user label scoring value set and the user label use frequency value corresponding to the normalized user label scoring value to obtain a user label weight set; generating an article label weight based on each normalized article label scoring value in the normalized article label scoring value set and an article label use frequency value corresponding to the normalized article label scoring value to obtain an article label weight set; and generating a user item label table based on the user label weight set and the item label weight set.
In a second aspect, some embodiments of the present disclosure provide an information generating apparatus, the apparatus comprising: an acquisition unit configured to acquire a set of user tag information and a set of article 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 usage frequency value corresponding to the user tag score value, and the article tag information includes an article tag name, an article tag score value corresponding to the article tag name, and an article tag usage frequency value corresponding to the article tag score value; a first generating unit configured to generate 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; a second generating unit, configured to generate a user tag weight based on each normalized user tag scoring value in the normalized user tag scoring value set and a user tag usage frequency value corresponding to the normalized user tag scoring value, so as to obtain a user tag weight set; a third generating unit, configured to generate an item label weight based on each normalized item label scoring value in the normalized item label scoring value set and an item label usage frequency value corresponding to the normalized item label scoring value, so as to obtain an item label weight set; and a fourth generating unit configured to generate a user item label table based on the user label weight set and the item label weight set.
In some embodiments, said generating an item tag weight based on each normalized item tag score value in said set of normalized item tag score values and an item tag usage frequency value corresponding to said normalized item tag score value comprises:
determining the quantity of item tag information included in the item tag information set;
determining a sum of article tag usage frequency values for each article tag information in the set of article tag information;
inputting the normalized item tag score value, the sum of the item tag usage frequency values of the respective item tag information, the item tag usage frequency value corresponding to the item tag score value, and the quantity of item tag information included in the item tag information set into the following formula to generate an item tag weight:
wherein W represents an article label weight of article label information including the article label score value, r represents the normalized article label score value, num represents an article label use frequency value corresponding to the article label score value, T represents a sum of article label use frequency values of the respective article label information, α represents a number of article label information included in the article label information set, and e represents a parameter whose value is 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 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-described various embodiments of the present disclosure has the following advantageous effects: firstly, respectively carrying out normalization processing on a user label scoring value of each user label information in a user label information set and an article label scoring value of each article label information in an article label information set to generate a normalized user label scoring value and a normalized article label scoring value, and obtaining a normalized user label scoring value set and a normalized article label scoring value set. The numerical values are normalized, so that the accuracy of the calculation result can be improved, and the data can be conveniently calculated. Then, the executing agent may perform numerical processing on each normalized user tag score value in the normalized user tag score value set and the user tag usage frequency value corresponding to the normalized user tag score value, generate a user tag weight, and 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 use frequency value corresponding to the normalized article label scoring value to generate an article label weight, so as to obtain an article label weight set. Optionally, the execution subject may establish 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 whole user tag set. Then, by ranking the label weights of the items in the item label weight set and establishing an item label weight matrix, the importance of each item label in the whole item label set can be determined. And multiplying the user label weight matrix and the article label weight matrix to obtain a user article label weight matrix. And inputting the user article label weight matrix into a user article label empty table to obtain a user article label table. The influence of the user tags and the item tags on the user can be comprehensively considered through the user item tag table. Thus, the executive agent may adjust the user item label according to the weight of each user item label in the user item label table. Thus, the system is facilitated to provide customized services for the user.
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The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and features are not necessarily drawn to scale.
FIG. 1 is a schematic diagram of one application scenario of an information generation method according to some embodiments of the present disclosure;
FIG. 2 is a flow diagram of some embodiments of an information generation method according to the present disclosure;
FIG. 3 is a flow diagram of further embodiments of an information generation method according to the present disclosure;
FIG. 4 is a schematic block 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.
Detailed Description
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 is to be understood that the 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 for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure 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 only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence of the functions performed by these devices, modules or units.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the 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 a set of normalized user tag score values and a set of normalized item tag score values 104 from a set of user tag information 102 and a set of item tag information 103. Second, computing device 101 may generate a set of user tag weights 105 and a set of item tag weights 106 from the set of normalized user tag score values and the set of normalized item tag score values 104. The computing device 101 may then generate a user item tag table 107 from the set of user tag weights 105 and the set of item tag weights 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, it may be implemented as a distributed cluster composed of multiple 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 enumerated above. It may be implemented, for example, as multiple software or software modules to provide distributed services, or as a single software or software module. And 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 implementation needs dictate.
With continued reference to fig. 2, a flow 200 of some embodiments of an 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 generation method comprises the following steps:
In some embodiments, an executing entity (e.g., a computing device shown in fig. 1) for the information generating method may obtain the user tag information set and the item tag information set associated with the user tag information set from a terminal through a wired connection or a wireless connection. The user tag information includes a user tag name, a user tag score value corresponding to the user tag name, and a user tag usage 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 min; 12 times }, { sport; 4, dividing; 10 times } ". The item tag information set is "{ didy; 4, dividing; 15 times }, { exercise wheel; 5 min; 11 times } ".
In some embodiments, the executing entity 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, so as 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 user tag information set may be "2, 3,4,1, 5". And normalizing the user label scoring values in the user label information set to obtain a normalized user label scoring value set of '0.13, 0.2,0.27,0.07 and 0.33'. Each item tag score value in the set of item tag information may be "3, 3,4,2, 5". And normalizing the label scoring values of the articles in the article label information set to obtain a normalized article label scoring value set of '0.17, 0.17,0.23,0.18 and 0.29'. Here, the numerical value after the normalization processing takes two digits after the decimal point.
In some optional implementations of some embodiments, the performing agent may generate the set of normalized user tag score values and the set of normalized item tag score values by:
the first step, the user label scoring values of all user label information in the user label information set are sequenced to obtain a user label scoring value sequence. Here, the manner of sorting is not limited.
As an example, each user tag score value in the user tag information set may be "2, 3,4,1, 5". Sequencing the user label scoring values in the user label information set from large to small according to numerical values, wherein the sequence of obtaining the user label scoring values is as follows: {5,4,3,2,1}.
And secondly, performing difference processing on every two adjacent user label score values in the user label score value sequence to generate a user label score difference value, so as to obtain a user label score difference value set.
By way of example, the user tag score value sequence may be {5,4,3,2,1 }. And performing difference processing on every two adjacent user label scoring values in the user label scoring value sequence to obtain a user label scoring difference set, wherein the user label scoring difference set comprises the following steps: {1,1,1,1}.
And thirdly, determining the average value of the user label score difference values in the user label score difference value set.
As an example, the set of user tag score difference values may be: {1,1,1,1}. The average value of the user tag score difference values in the user tag score difference value set may be determined to be "1".
And fourthly, selecting the user label scoring value which is greater than or equal to a first preset threshold value from the user label scoring value sequence as a first user label scoring value, and obtaining a first user label scoring value group. Here, the first predetermined threshold is not limited.
By way of example, the 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 which is 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 set as follows: {5,4,3}.
And fifthly, summing the average value of each first user label scoring value in the first user label scoring value set and the user label scoring difference value to generate a summed user label scoring value, and obtaining the summed user label scoring value set.
As an example, the first set of user tag scores may be {5,4,3 }. The average of the above user tag score difference is "1". And summing the average value of each first user label scoring value in the first user label scoring value group and the user label scoring difference value to generate a summed user label scoring value, wherein the summed user label scoring value set is {6,5,4 }.
And sixthly, selecting the user label scoring value smaller than the first preset threshold value from the user label scoring value sequence as a second user label scoring value to obtain a second user label scoring value group.
By way of example, the 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 set as follows: {2,1}.
And seventhly, performing difference processing on each second user label scoring value in the second user label scoring value set and the average value of the user label scoring difference values to generate difference user label scoring values, and obtaining the difference user label scoring value set.
As an example, the second set of user tag scores may be {2,1 }. The average of the above user tag score difference is "1". And performing difference processing on each second user label scoring value in the second user label scoring value set and the average value of the user label scoring difference values to generate difference user label scoring values, wherein the obtained difference user label scoring value set is {1 }. Here, the value with the difference user tag score value of zero is cleared by default.
And eighthly, combining the sum user label scoring value set and the difference user label scoring value set to generate a user label scoring value set to be processed.
As an example, the set of summed user tag credit values described above may be {6,5,4 }. The set of difference user tag scores may be {1 }. Merging the summation user label scoring value set and the difference user label scoring value set to generate a user label scoring value set to be processed, wherein the user label scoring value set to be processed comprises the following steps: {6,5,4,1}.
And ninthly, carrying out normalization processing on each user label scoring value to be processed in the user label scoring value set to be processed to generate a normalized user label scoring value to be processed as a normalized user label scoring value, and obtaining the normalized user label scoring value set.
By way of example, the set of pending user tag credit values may be {6,5,4,1 }. Normalizing each user label scoring value to be processed in the user label scoring value set to be processed to generate a normalized user label scoring value to be processed, wherein the normalized user label scoring value set is obtained by: {0.375,0.3125,0.25,0.0625}.
And step ten, sequencing the item label scoring values of the item label information in the item label information set to obtain an item label scoring 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 label scoring values of the articles in the article label information set from large to small according to numerical values, wherein the obtained article label scoring value sequence is as follows: {5,4,3,2,1}.
And step ten, performing difference processing on every two adjacent article label scoring values in the article label scoring value sequence to generate an article label scoring difference value, and obtaining an article label scoring difference value set.
As an example, the item tag score value sequence may be {5,4,3,2,1 }. Performing difference processing on every two adjacent article label scoring values in the article label scoring value sequence to obtain an article label scoring difference set, wherein the article label scoring difference set comprises the following steps: {1,1,1,1}.
And step ten, determining the mean value of the item label score difference values in the item label score difference value set.
As an example, the item tag score difference set may be: {1,1,1,1}. The mean value of the individual item tag score differences in the item tag score difference set may be determined to be "1".
And step thir, selecting the item label scoring value which is greater than or equal to a second preset threshold value from the item label scoring value sequence as a first item label scoring value, and obtaining a first item label scoring value group. Here, the setting of the second predetermined threshold 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 the item label scoring value which is greater than or equal to a second preset threshold value from the item label scoring value sequence, and obtaining a first item label scoring value set as follows: {5,4,3}.
And fourteenth, summing the average value of each first article label scoring value in the first article label scoring value group and the article label scoring difference value to generate a summed article label scoring value, so as to obtain a summed article label scoring value group.
As an example, the first set of item tag scores may be {5,4,3 }. The average value of the item label score difference is "1". And summing the average of each first item label scoring value in the first item label scoring value group and the item label scoring difference to generate a summed item label scoring value, wherein the summed item label scoring value set is {6,5,4 }.
And fifteenth, selecting the item label scoring value smaller than a second preset threshold value from the item label scoring value sequence as a second item label scoring value, and obtaining a second item label scoring 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 the item label score value smaller than a second preset threshold value from the item label score value sequence as a second item label score value, and obtaining a second item label score value group as follows: {2,1}.
Sixthly, performing difference processing on each second article label scoring value in the second article label scoring value set and the mean value of the article label scoring difference to generate a difference article label scoring value, and obtaining the difference article label scoring value set.
As an example, the second item label score value set may be {2,1 }. The average value of the item label score difference is "1". And performing difference processing on each second article label scoring value in the second article label scoring value set and the mean value of the article label scoring difference values to generate difference article label scoring values, wherein the obtained difference article label scoring value set is {1 }.
Seventhly, merging the summation article label scoring value set and the difference article label scoring value set to generate a to-be-processed article label scoring value set.
As an example, the above-described set of summed item tag credit values may be {6,5,4 }. The above set of difference item tag scores may be {1 }. Merging the sum article label scoring value set and the difference article label scoring value set to generate a to-be-processed article label scoring value set, wherein the to-be-processed article label scoring value set comprises the following steps: {6,5,4,1}.
And eighteen, carrying out normalization processing on each label scoring value of the to-be-processed article in the label scoring value set of the to-be-processed article to generate a normalized article label scoring value to be used as a normalized article label scoring value, and obtaining a normalized article label scoring value set.
As an example, the above set of label credit values for the pending item may be {6,5,4,1 }. Normalizing each label score value of the articles to be processed in the label score value set of the articles to be processed to generate a normalized article label score value, wherein the obtained normalized article label score value set is as follows: {0.375,0.3125,0.25,0.0625}.
In some embodiments, the execution subject may determine the number of user tag information included in the user tag information set. Then, the number of user tag information included in the user tag information set, each normalized user tag score value in the normalized user tag score value set, and the user tag usage frequency value corresponding to the normalized user tag score value may be input to the following formula to generate a user tag weight:
wherein, WnA user tag weight representing nth user tag information. A. thenA normalized user tag score value representing nth user tag information. B isnThe user tag use frequency value representing the nth user tag information. u represents the number of user tag information included in the user tag information set. A. theiA normalized user tag score value representing the ith user tag information. B isiThe user tag use frequency value representing the ith user tag information. Here, the value range after the decimal point 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 pieces 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 scoring value in the normalized user tag scoring value set may be "6, 5". Inputting the values into a formula respectively to generate user label weights:
the user label weight set is obtained as 0.59, 0.41.
And 204, generating an article label weight based on each normalized article label scoring value in the normalized article label scoring value set and the article label use frequency value corresponding to the normalized article label scoring value, so as to obtain an article label weight set.
In some embodiments, the executing entity may determine a quantity of item tag information included in the set of item tag information. Inputting the quantity of the item label information included in the item label information set, each normalized item label scoring value in the normalized item label scoring value set, and the item label corresponding to the normalized item label scoring value to the following formula by using a frequency value to generate an item label weight:
wherein, WjAn item tag weight representing jth item tag information. MjA normalized item tag score value representing jth item tag information. N is a radical ofjAn item tag usage frequency value representing jth item tag information. t represents the number of item tag information included in the item tag information set. MiA normalized item tag score value representing the ith item tag information. N is a radical ofiAn item tag usage frequency value representing the ith item tag information. Here, the value range after the decimal point of the item label 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 value of the article tag usage frequency corresponding to each normalized article tag scoring value in the set of normalized article tag scoring values may be "4, 6". Inputting the above values into a formula to generate item label weights:
the weight set of the item label is obtained as 0.44, 0.56.
In some embodiments, the executing agent may generate the user tag table by:
firstly, combining each user label weight in the user label weight set with an article label weight in the article label weight set corresponding to the user label weight to generate a binary group, so as to obtain a binary group set.
As an example, the user tag weight set may be {0.59,0.41 }. The set of item tag weights is {0.44,0.56 }. Combining each user label weight in the user label weight set with the article label weight in the article label weight set corresponding to the user label weight to generate a binary set, wherein the binary set is obtained by: { (0.59, 0.44); (0.41,0.56)}.
And secondly, establishing a user article label empty table, and inputting each binary group in the binary group set into the user article label empty table to generate a user article label table.
As an example, the tuple set may be { (0.59, 0.44); (0.41,0.56)}. Establishing a user article label empty table, inputting each binary group in the binary group set into the user article label empty table, and generating a user article label table:
| item tag weight (W)1) | Item tag weight (W)2) | |
| User tag weight (W)1) | (0.59,0.44) | |
| User tag weight (W)2) | (0.41,0.56) |
In some optional implementations of some embodiments, the executing agent may further generate the user item tag table by:
and step one, constructing a user label weight matrix according to the user label weight set. Here, the method of constructing the matrix is not limited.
As an example, the user tag weight set may be {0.59,0.41 }. Sequencing the user label weights in the user label weight set from large to small according to numerical values to obtain a user label weight sequence { 0.59; 0.41}. Constructing a user label weight matrix according to the sequence of the user label weights in the user label weight sequence to obtain:
and secondly, constructing an article label weight matrix according to the article label weight set.
As an example, the set of item tag weights described above may be {0.59,0.41 }. Sequencing the label weights of all the articles in the article label weight set from large to small according to numerical values to obtain an article label weight sequence { 0.56; 0.44}. Constructing a user label weight matrix according to the sequence of the user label weights in the user label weight sequence to obtain: [0.560.44].
And thirdly, multiplying the user label weight matrix and the article label weight matrix to generate a user article label weight matrix.
As an example, the user tag weight matrix may be:
the user tag weight matrix may be: [0.560.44]. Multiplying the two matrixes to obtain a user article label weight matrix:
and fourthly, establishing a user article label empty table, inputting each user article label weight in the user article label weight matrix into the user article label empty table, and generating the user article label table.
As an example, a user item label empty table is established, and each user item label weight in the user item label weight matrix is input to the user item label empty table to generate a user item label table. Here, the manner of creating the user item label empty table is not limited.
| Item tag weight (W)1) | Item tag weight (W)2) | |
| User' sLabel weight (W)1) | 0.3304 | 0.2596 |
| User tag weight (W)2) | 0.2296 | 0.1804 |
Optionally, the display device controlling the communication connection displays the user article tag table, so that the operation device adjusts the user article tag based on the user article tag table.
As an example, a display device controlling the communication connection displays the user item tag table described above. The operating device may adjust the user item tag name by the size of each user item tag weight in the user item tag table. For example, the image representing the tag name of the user item corresponding to the tag weight of the user item is resized or repositioned.
In some optional implementation manners of some embodiments, the execution subject may establish the user label weight matrix by sorting the user label weights in the user label weight set. Thus, the importance of each user's tag in the entire user's tag collection can be determined. Then, an item label weight matrix is established by sequencing the item label weights in the item label weight set, and the importance of each item label in the whole item label set can be displayed. And multiplying the user label weight matrix and the article label weight matrix to obtain a user article label weight matrix. And inputting the user article label weight matrix into a user article label empty table to obtain a user article label table. The influence of the user label and the article label on the user can be comprehensively considered through the user article label table. Finally, the executive body can adjust the user item label according to the weight of each user item label in the user item label table.
One of the above-described various embodiments of the present disclosure has the following advantageous effects: firstly, respectively carrying out normalization processing on a user label scoring value of each user label information in a user label information set and an article label scoring value of each article label information in an article label information set to generate a normalized user label scoring value and a normalized article label scoring value, and obtaining a normalized user label scoring value set and a normalized article label scoring value set. And normalization processing is carried out, so that the accuracy of a calculation result can be improved, and the data can be conveniently calculated. Then, the executing agent may perform numerical processing on each normalized user tag score value in the normalized user tag score value set and the user tag usage frequency value corresponding to the normalized user tag score value, generate a user tag weight, and 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 use frequency value corresponding to the normalized article label scoring value to generate an article label weight, so as to obtain an article label weight set. Optionally, the execution subject may establish 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 whole user tag set. Then, by ranking the label weights of the items in the item label weight set and establishing an item label weight matrix, the importance of each item label in the whole item label set can be determined. And multiplying the user label weight matrix and the article label weight matrix to obtain a user article label weight matrix. And inputting the user article label weight matrix into a user article label empty table to obtain a user article label table. The influence of the user tags and the item tags on the user can be comprehensively considered through the user item tag table. Finally, the executive body can adjust the user item label according to the weight of each user item label in the user item label table. Thus, the system is facilitated to provide customized services for the user.
With further reference to fig. 3, a flow 300 of further embodiments of an 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 generation method comprises the following steps:
In some embodiments, the specific implementation manner and technical effects of the steps 301 and 302 can refer to the steps 201 and 202 in the embodiments corresponding to fig. 2, which are not described herein again.
In some embodiments, the execution subject 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 min; 12 times }, { sport; 4, dividing; 10 times } ". It is determined that the number of user tag information included in the user tag information set is "2".
In some embodiments, the executing agent may add the user tag usage frequency values of the 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 min; 12 times }, { sport; 4, dividing; 10 times } ". The usage frequency value of each user tag in the user tag information set is "12; 10". The sum of the frequency values used by the individual user tags is "22".
In some embodiments, the executing entity may input the normalized user tag score value, the 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 user tag information included in the user tag information set to the following formula to generate a user tag weight:
wherein K represents a user tag weight of the user tag information including the user tag score value. t represents the normalized user tag score value described above. And mum represents the user label use frequency value corresponding to the user label scoring value. U represents the sum of user tag usage frequency values of the respective user tag information. θ represents the number of user tag information included in the user tag information set.
As an example, the normalized user tag score t described above may be "0.59". The user tag usage frequency value mum corresponding to the above user tag score value may be "12". The sum U of the user tag use frequency values of the respective user tag information is "22". The number θ of user tag information included in the user tag information set is "2". Inputting the values into a formula to generate user label weights:
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 above item tag information set may be "{ didy; 4, dividing; 15 times }, { exercise wheel; 5 min; 11 times } ". It is counted that the number of item tag information included in the item tag information set is "2".
In some embodiments, the executing agent may add the article tag usage frequency values of the article tag information in the article tag information set to obtain a sum of the article tag usage frequency values.
As an example, the above item tag information set may be "{ didy; 4, dividing; 15 times }, { exercise wheel; 5 min; 11 times } ". An article tag use frequency value of each article tag information in the article tag information set is "15; 11". The sum of the frequency values for the article tags of the individual article tag information is "26".
In some embodiments, the executor 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 a quantity of item tag information included in the item tag information set into the following formula to generate an item tag weight:
wherein W represents an article label weight of article label information including the article label score value, r represents the normalized article label score value, num represents an article label usage frequency value corresponding to the article label score value, T represents a sum of the article label usage frequency values of the respective article label information, α represents a number of article label information included in the article label information set, and e represents a parameter whose value is 0.081819. Here, the value range of e is (0,1), and the specific value varies depending on the number of item tag information included in the item tag information set. The larger the quantity is, the larger the value of e is, and the initial value of e is not limited in the value range.
As an example, the normalized item tag score value r described above may be "0.56". The item tag usage frequency value num corresponding to the above item tag score value may be "15". The sum T of the article tag use frequency values of the individual article tag information is "26". The number α of item tag information included in the item tag information set is "2". Inputting the values into a formula to generate an item label weight:
In some embodiments, the specific implementation manner and technical effects of step 309 may refer to step 205 in those embodiments corresponding to fig. 2, and are not described herein again.
One of the above-described various embodiments of the present disclosure has the following advantageous effects: first, the executing agent may determine the number of user tag information included in the user tag information set and determine the sum of usage frequency values of each user tag in the user tag information set. Thus, the calculation of the weight of the tag is facilitated in consideration of the mutual influence between the respective user tag names in the user tag information set. And then, inputting the normalized user tag scoring 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 weight. The user label weight generated by the formula improves the accuracy of the user label weight and is beneficial to adjusting the user label name. Similarly, the object label weight generated by the formula improves the accuracy of the object label weight and is beneficial to adjusting the object label name.
With further reference to fig. 4, as an implementation of the above-described method for the above-described figures, the present disclosure provides some embodiments of an information generating apparatus, which correspond to those of the method embodiments described above for fig. 2, and which may be applied in various electronic devices in particular.
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. The obtaining unit 401 is configured to obtain a set of user tag information and a set of article 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 usage frequency value corresponding to the user tag score value, and the article tag information includes an article tag name, an article tag score value corresponding to the article tag name, and an article tag usage frequency value corresponding to the article tag score value. A first generating unit 402, configured to generate 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. A second generating unit 403, configured to generate a user tag weight based on each normalized user tag scoring value in the normalized user tag scoring value set and a user tag usage frequency value corresponding to the normalized user tag scoring value, so as to obtain a user tag weight set. A third generating unit 404, configured to generate an item label weight based on each normalized item label scoring value in the normalized item label scoring value set and an item label usage frequency value corresponding to the normalized item label scoring value, to obtain an item label weight set. A fourth generating unit 405 configured to generate a user item label table based on the user label weight set and the item label weight set.
In some optional implementations of some embodiments, the fourth generating unit 405 of the information generating apparatus 400 is further configured to: constructing a user label weight matrix according to the user label weight set; constructing an article label weight matrix according to the article label weight set; multiplying the user label weight matrix and the article label weight matrix to generate a user article label weight matrix; and establishing a user article label empty table, inputting each user article label weight in the user article label weight matrix into the user article label empty table, and generating the user article label table.
It will be understood that the elements described in the apparatus 400 correspond to various steps in the method described with reference to fig. 2. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 400 and the units included therein, and will not be described herein again.
Referring now to FIG. 5, a block diagram of an electronic device (e.g., computing device 101 of FIG. 1)500 suitable for use in implementing some embodiments of the present disclosure is shown. The server shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 5, electronic device 500 may include a processing means (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes in accordance with 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 necessary 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 through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
Generally, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; storage devices 508 including, for example, magnetic tape, hard disk, etc.; and a communication device 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 illustrates an electronic device 500 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 5 may represent one device or may represent multiple devices as desired.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams 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 illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. The computer program, when executed by the processing device 501, performs the above-described functions defined in the methods of some embodiments of the present disclosure.
It should be noted that the computer readable medium described above in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination 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 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, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. 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, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications 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 network.
The computer readable medium may be embodied in the apparatus; or may exist separately without being assembled 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 score value corresponding to the user tag name and a user tag use frequency value corresponding to the user tag score value, and the article tag information comprises 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; respectively generating a normalized user label scoring value set and a normalized article label scoring value set based on the user label information set and the article label information set; generating user label weights based on each normalized user label scoring value in the normalized user label scoring value set and the user label use frequency value corresponding to the normalized user label scoring value to obtain a user label weight set; generating an article label weight based on each normalized article label scoring value in the normalized article label scoring value set and an article label use frequency value corresponding to the normalized article label scoring value to obtain an article label weight set; and generating a user item label table based on the user label weight set and the item label weight set.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of 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 type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart 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 software, and may also be implemented by hardware. The described units may also be provided in a processor, and may be 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 in some cases form a limitation to the unit itself, and for example, the fourth generation unit may also be described as "a unit that generates a user item label table based on the user label weight set and the item label weight set".
The functions described herein above 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: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology 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-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (8)
1. An information generating 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 scoring value corresponding to the user tag name and a user tag use frequency value corresponding to the user tag scoring value, and the article tag information comprises an article tag name, an article tag scoring value corresponding to the article tag name and an article tag use frequency value corresponding to the article tag scoring value;
respectively generating a normalized user label score value set and a normalized article label score value set based on the user label information set and the article label information set;
generating user label weights based on each normalized user label scoring value in the normalized user label scoring value set and the user label use frequency value corresponding to the normalized user label scoring value to obtain a user label weight set;
generating an article label weight based on each normalized article label scoring value in the normalized article label scoring value set and an article label use frequency value corresponding to the normalized article label scoring value to obtain an article label weight set;
and generating a user item label table based on the user label weight set and the item label weight set.
2. The method of claim 1, wherein 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 label weight matrix according to the user label weight set;
constructing an article label weight matrix according to the article label weight set;
multiplying the user label weight matrix and the article label weight matrix to generate a user article label weight matrix;
and establishing a user article label empty table, inputting each user article label weight in the user article label weight matrix into the user article label empty table, and generating the user article label table.
3. The method of claim 2, wherein the method further comprises:
and controlling a display device in communication connection to display the user article label table so that the operation device can adjust the user article label based on the user article label table.
4. The method of claim 3, wherein said generating a set of normalized user tag scores and a set of normalized item tag scores based on said set of user tag information and said set of item tag information, respectively, comprises:
sorting the user tag scoring values of all user tag information in the user tag information set to obtain a user tag scoring value sequence;
performing difference processing on every two adjacent user tag scoring values in the user tag scoring value sequence to generate a user tag scoring difference value, and obtaining a user tag scoring difference value set;
determining the average value of all user label score difference values in the user label score difference value set;
selecting a user tag score value which is greater than or equal to a first preset threshold value from the user tag score value sequence as a first user tag score value to obtain a first user tag score value group;
summing the average value of each first user label scoring value in the first user label scoring value group and the user label scoring difference value to generate a summed user label scoring value, so as to obtain a summed user label scoring value group;
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 label scoring value in the second user label scoring value set and the average value of the user label scoring difference values to generate difference user label scoring values to obtain difference user label scoring value sets;
merging the summation user label scoring value set and the difference user label scoring value set to generate a user label scoring value set to be processed;
normalizing each user label scoring value to be processed in the user label scoring value set to be processed to generate a normalized user label scoring value to be processed as a normalized user label scoring value, and obtaining the normalized user label scoring value set;
sorting the item label scoring values of the item label information in the item label information set to obtain an item label scoring value sequence;
performing difference processing on every two adjacent article label scoring values in the article label scoring value sequence to generate an article label scoring difference value, so as to obtain an article label scoring difference value set;
determining a mean value of each item tag score difference in the item tag score difference set;
selecting an article label score value which is greater than or equal to a second preset threshold value from the article label score value sequence as a first article label score value to obtain a first article label score value group;
summing the average value of each first article label scoring value in the first article label scoring value set and the article label scoring difference value to generate a summed article label scoring value, so as to obtain a summed article label scoring value set;
selecting an article label score value smaller than a second preset threshold value from the article label score value sequence as a second article label score value to obtain a second article label score value group;
performing difference processing on each second article label scoring value in the second article label scoring value set and the mean value of the article label scoring difference values to generate difference article label scoring values to obtain difference article label scoring value sets;
merging the sum article label score value set and the difference article label score value set to generate an article label score value set to be processed;
and carrying out normalization processing on each label scoring value of the articles to be processed in the label scoring value set of the articles to be processed to generate a normalized article label scoring value to be processed as a normalized article label scoring value, so as to obtain the normalized article label scoring value set.
5. The method of claim 4, 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 usage frequency 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 the sum of the user tag use frequency values of each piece of 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 user tag information, the user tag use 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 into the following formula to generate a user tag weight:
wherein K represents a user tag weight of the user tag information including the user tag score value, t represents the normalized user tag score value, mum represents a user tag usage frequency value corresponding to the user tag score value, U represents a sum of the user tag usage frequency values of the respective user tag information, and θ represents a number of user tag information included in the user tag information set.
6. An information generating apparatus comprising:
an acquisition unit configured to acquire a set of user tag information and a set of article 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 usage frequency value corresponding to the user tag score value, and the article tag information includes an article tag name, an article tag score value corresponding to the article tag name, and an article tag usage frequency value corresponding to the article tag score value;
a first generating unit configured to generate 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;
a second generating unit, configured to generate a user tag weight based on each normalized user tag scoring value in the normalized user tag scoring value set and a user tag usage frequency value corresponding to the normalized user tag scoring value, so as to obtain a user tag weight set;
a third generating unit configured to generate an item label weight based on each normalized item label scoring value in the normalized item label scoring value set and an item label usage frequency value corresponding to the normalized item label scoring value, resulting in an item label weight set;
a fourth generating unit configured to generate a user item label table based on the user label weight set and the item label weight set.
7. 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, cause the one or more processors to implement the method of any one of claims 1-5.
8. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-5.
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