CN111737587A - Device operation method, device, electronic device and computer readable medium - Google Patents
Device operation method, device, electronic device and computer readable medium Download PDFInfo
- Publication number
- CN111737587A CN111737587A CN202010846242.8A CN202010846242A CN111737587A CN 111737587 A CN111737587 A CN 111737587A CN 202010846242 A CN202010846242 A CN 202010846242A CN 111737587 A CN111737587 A CN 111737587A
- Authority
- CN
- China
- Prior art keywords
- tag
- user
- article
- user tag
- label
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 38
- 239000013598 vector Substances 0.000 claims abstract description 161
- 238000012545 processing Methods 0.000 claims description 25
- 238000004891 communication Methods 0.000 claims description 11
- 238000004590 computer program Methods 0.000 claims description 9
- 239000002537 cosmetic Substances 0.000 description 11
- 238000010586 diagram Methods 0.000 description 11
- 230000006870 function Effects 0.000 description 8
- 239000000126 substance Substances 0.000 description 8
- 230000000694 effects Effects 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 4
- 239000003795 chemical substances by application Substances 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000011017 operating method Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- User Interface Of Digital Computer (AREA)
Abstract
The embodiment of the disclosure discloses a device operation method, a device operation device, an electronic device and a computer readable medium. The specific implementation mode of the method comprises the following steps: vectorizing each user tag name in the user tag information set and each article tag name in the article tag information set respectively to generate a user tag name vector and an article tag name vector; determining the association degree between each user tag name vector in the user tag name vector set and the object tag name vector corresponding to the user tag name vector to obtain an association degree set; generating user tag weight based on each user tag scoring value in the user tag information set and the user tag use frequency value corresponding to the user tag scoring value; and generating an article label weight based on each article label scoring value in the article label information set and the article label use frequency value corresponding to the article label scoring value. This embodiment facilitates the rational generation of a tag relationship information table.
Description
Technical Field
Embodiments of the present disclosure relate to the field of computer technologies, and in particular, to a device operation method, an apparatus, 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 system may construct a user representation through user item tags. It is desirable to reasonably establish relationship data between user tags. Therefore, the tag relation information table can be reasonably generated, and the user representation can be favorably constructed.
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 a device operation 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 a method of device operation, the 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 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; vectorizing the user tag name of each piece of user tag information in the user tag information set and the article tag name of each piece of article tag information in the article tag information set respectively to generate a user tag name vector and an article tag name vector, and obtaining a user tag name vector set and an article tag name vector set; determining the association degree between each user tag name vector in the user tag name vector set and the object tag name vector in the object tag name vector set corresponding to the user tag name vector to obtain an association degree set; generating user label weights based on each user label scoring value in the user label information set and the user label use frequency value corresponding to the user label scoring value to obtain a user label weight set; and generating an article label weight based on each article label scoring value in the article label information set and the article label use frequency value corresponding to the article label scoring value to obtain an article label weight set.
In a second aspect, some embodiments of the present disclosure provide an apparatus for operating a device, 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 perform vectorization processing on a user tag name of each user tag information in the user tag information set and an article tag name of each article tag information in the article tag information set respectively to generate a user tag name vector and an article tag name vector, resulting in a user tag name vector set and an article tag name vector set; a determination unit configured to determine a degree of association between each user tag name vector in the set of user tag name vectors and an item tag name vector in the set of item tag name vectors corresponding to the user tag name vector, resulting in a set of degrees of association; a second generating unit, configured to generate a user tag weight based on each user tag score value in the user tag information set and a user tag usage frequency value corresponding to the user tag score value, so as to obtain a user tag weight set; and a third generating unit configured to generate an item label weight based on each item label scoring value in the item label information set and an item label use frequency value corresponding to the item label scoring value, so as to obtain an item label weight set.
In some embodiments, said determining a degree of association between each user tag name vector in said set of user tag name vectors and an item tag name vector in said set of item tag name vectors corresponding to said user tag name vector comprises: respectively carrying out negation processing on each dimension in the user tag name vector and the article tag name vector to generate a negated user tag name vector and a negated article tag name vector;
determining the number of dimensions included in the inverted user tag name vector;
determining a degree of association between the user tag name vector and the item tag name vector by:
wherein the content of the first and second substances,
representing a degree of association between the user tag name vector and the item tag name vector,
representing the number of dimensions comprised by the inverted user tag name vector,
second to represent the user tag name vector after negation
The value of the dimension(s) is,
the second of the inverted article tag name vector
The value of the dimension.
In some embodiments, the generating a user tag weight based on each user tag score value in the set of user tag information and a user tag usage frequency value corresponding to the user tag score value comprises:
performing non-dimensionalization on the user tag score value to generate a non-dimensionalized user tag score value;
determining the number of user tag information included in the user tag information set;
inputting the number of the user tag information included in the user tag information set, the non-dimensionalized user tag score value and the user tag usage frequency value corresponding to the user tag score value into the following formula to generate a user tag weight:
wherein the content of the first and second substances,
a user tag weight indicating user tag information including the user tag score value,
representing the non-dimensionalized user tag score value,
indicating a user tag usage frequency value corresponding to the user tag score value,
represents the amount of user tag information included in the set of user tag information,
is shown as
A non-dimensionalized user tag score value of individual user tag information,
is shown as
User label of individual user label informationThe tag uses the frequency value of the frequency,
representing a parameter with a value of 0.081819.
In some embodiments, said generating an item tag weight based on each item tag score value in said set of item tag information and an item tag usage frequency value corresponding to said item tag score value comprises:
performing non-dimensionalization on the item label score value to generate a non-dimensionalized item label score value;
determining the quantity of item tag information included in the item tag information set;
inputting the number of item label information included in the item label information set, the non-dimensionalized item label score value and an item label usage frequency value corresponding to the item label score value into the following formula to generate an item label weight:
wherein the content of the first and second substances,
a user tag weight representing item tag information including the item tag score value,
representing the non-dimensionalized item label credit value,
indicating an item tag usage frequency value corresponding to the item tag score value,
representing the amount of item tag information included in the set of item tag information,
is shown as
A non-dimensionalized item tag score value of individual item tag information,
is shown as
The item tag usage frequency value for the individual item tag information,
representing a parameter with 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 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: first, a user tag name of each user tag information in the user tag information set and an article tag name of each article tag information in the article tag information set are respectively subjected to vectorization processing to generate a user tag name vector and an article tag name vector, and a user tag name vector set and an article tag name vector set are obtained. Then, the association degree between each user tag name vector in the user tag name vector set and the article tag name vector in the article tag name vector set corresponding to the user tag name vector is determined, and an association degree set is obtained. Therefore, the degree of closeness among the labels can be determined according to the relevance in the relevance set, and a foundation is established for subsequently constructing the user portrait. Then, the executing body may perform numerical processing on each user tag score value in the user tag score value set and the user tag use frequency value corresponding to the user tag score value, generate a user tag weight, and obtain a user tag weight set. And performing numerical processing on each item label scoring value in the item label scoring value set and the item label use frequency value corresponding to the item label scoring value to generate an item label weight, so as to obtain an item label weight set. Optionally, the executing body may further combine each user tag weight in the association degree set, the association degree corresponding to the user tag weight, and an item tag weight in the item tag weight set corresponding to the user tag weight to generate a triple, so as to obtain a triple set. The execution body may establish a tag relationship information empty table, and input each triplet in the triplet set into the tag relationship information empty table to generate the tag relationship information table. The influence of the relationship between the user tag and the article tag on the user portrait can be comprehensively considered through the tag relationship information table. Finally, the execution body may resize or resize the user representation representing the tag name according to a relationship between the tag weight and the tag. Thus, the system is facilitated to provide customized services for user portrayal of users.
Drawings
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 a device operation method according to some embodiments of the present disclosure;
fig. 2 is a flow diagram of some embodiments of a method of operation of a device according to the present disclosure;
FIG. 3 is a flow chart of further embodiments of a method of operation of an apparatus according to the present disclosure;
FIG. 4 is a schematic block diagram of some embodiments of a device operation 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 a device operation 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 user tag name vectors and a set of item tag name vectors 104 from a set of user tag information 102 and a set of item tag information 103. Second, the computing device 101 may determine the set of degrees of association 105 from the set of user tag name vectors and the set of item tag name vectors 104. The computing device 101 may then generate a set of user tag weights 106 and a set of item tag weights 107 from the set of user tag information 102 and the set of item tag information 103. Finally, optionally, the computing device 101 may generate a tag relationship information table 108 based on the set of associations 105, the set of user tag weights 106, and the set of item tag weights 107. And (4) optional. The computing device 101 may output the tag relationship information table 108 for display on the display screen 109.
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 a method of operation of a device 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. Here, the association may be a one-to-one correspondence relationship between the user tag information and the article tag information. 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 user tag information set may be "{ 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 execution body may generate the user tag name vector and the item tag name vector by:
the first step is to extract a user tag name of each user tag information in the user tag information set and an article tag name of each article tag information in the article tag information set to obtain a user tag name set and an article tag name set.
As an example, the user tag information set may be "{ 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 } ". Extracting tag names from the set respectively, wherein the obtained tag name set of the user is 'cosmetics'; sport ". The resulting set of article tag names is "didi; exercise wheel ".
And secondly, carrying out unique hot coding processing on each user tag name in the user tag name set and each article tag name in the article tag name set.
As an example, each user tag name in the set of user tag names described above may be "cosmetics; sport ". Each item tag name in the set of item tag names may be "didy; exercise wheel ". The set of user tag name vectors obtained by the one-hot encoding process is: { "cosmetic": [10001] (ii) a "move": [00101]}. The set of item tag name vectors is: { "edi": [01001] (ii) a "exercise wheel": [00011]}.
In some embodiments, the execution body may determine a degree of association between each user tag name vector in the set of user tag name vectors and an item tag name vector in the set of item tag name vectors corresponding to the user tag name vector by various methods, resulting in a set of degrees of association.
In some optional implementations of some embodiments, the execution subject may determine a degree of association between each user tag name vector in the set of user tag name vectors and an item tag name vector in the set of item tag name vectors corresponding to the user tag name vector by:
first, each dimension in the user tag name vector and the article tag name vector is subjected to negation processing to generate a negated user tag name vector and a negated article tag name vector. Here, inverting refers to converting the value in each dimension in the vector from 1 to 0 and from 0 to 1.
As an example, each user tag name vector in the user tag name vector set is inverted, and an inverted user tag name vector is obtained: { "cosmetic": [01110]}. And performing negation processing on the object tag name vectors to obtain a set of negated object tag name vectors: { "edi": [10110]}.
And secondly, determining the number of dimensions included in the inverted user tag name vector.
As an example, the user tag name vector after negation is { "cosmetic": [01110]}. The number of dimensions included in the inverted user tag name vector is "5".
Third, the execution body may determine a degree of association between the user tag name vector and the item tag name vector by the following formula:
wherein the content of the first and second substances,
and indicating the degree of association between the user tag name vector and the item tag name vector.
Representing the number of dimensions included in the inverted user tag name vector.
Second to represent the user tag name vector after negation
The value of the dimension.
The second of the inverted article tag name vector
The value of the dimension.
As an example, the user tag name vector after negation may be { "cosmetics": [01110]}. The inverted item tag name vector may be { "edi": [10110]}. Length of the inverted user tag name vector
Is "5". Inputting the numerical values into a formula to obtain the association degree as follows:
the degree of association between the two words "cosmetic" and "didi" was found to be "0.8".
In some alternative implementations of some embodiments, converting the tag names into vectors facilitates quantizing the tag names, and determining the degree of association between the vectors by the above formula. Thus, the degree of closeness between the tags may be determined, establishing the basis for subsequent construction of the user representation.
And 204, generating user label weights based on the user label scoring values in the user label information set and the user label use frequency values corresponding to the user label scoring values, and obtaining a user label weight set.
In some embodiments, the executing agent may input each user tag scoring value in the user tag information set and the user tag corresponding to the user tag scoring value using a frequency value to generate a user tag weight according to the following formula:
wherein the content of the first and second substances,
is shown as
User tag weight of individual user tag information.
Is shown as
A user tag score value of individual user tag information.
Is shown as
The user tag usage frequency value of the individual user tag information.
The number of user tag names representing the respective user tag information.
Is shown as
A user tag score value of individual user tag information.
Is shown as
The user tag usage frequency value of the individual user tag information. Here, the value range after the decimal point of the user tag weight is not limited.
As an example, the user tag information set may be "{ cosmetics; 5 min; 12 times }, { sport; 4, dividing; 10 times } ". Each user tag score value in the user tag information set is "5 scores; 4 minutes ". The frequency value of each user tag in the user tag information set is' 12 times; 10 times. The number of user tag information included in the user tag information set is "2". Inputting the numerical values into a formula to generate a user label weight:
and obtaining a user label weight set of {0.6,0.4 }.
In some embodiments, the executing agent may input each item tag score value in the item tag information set and an item tag usage frequency value corresponding to the item tag score value to the following formula to generate an item tag weight:
wherein the content of the first and second substances,
is shown as
Item tag weight of individual item tag information.
Is shown as
An item tag score value of the individual item tag information.
Is shown as
The item tag usage frequency value of the individual item tag information.
The number of article tag names representing the individual article tag information.
Is shown as
A user tag score value of individual user tag information.
Is shown as
The item tag usage frequency value of the individual item tag information. Here, the value range after the decimal point of the item label weight is not limited.
As an example, the above item tag information set may be "{ didy; 4, dividing; 15 times }, { exercise wheel; 5 min; 11 times } ". Each item label score value in the item label information set is "4 points; 5 minutes ". The usage frequency value of each article tag in the article tag information set is "15 times; 11 times. The number of item tag information included in the item tag information set is "2". Inputting the numerical values into a formula to generate the item label weight:
the weight set of the item label is obtained as 0.52, 0.48.
Optionally, a tag relationship information table is generated based on the association degree set, the user tag weight set, and the article tag weight set.
In some embodiments, the executing agent may generate the tag relationship information table by:
the method comprises the steps of firstly, combining each user label weight in the user label weight set, the association degree corresponding to the user label weight and the item label weight in the item label weight set corresponding to the user label weight to generate a triple, and obtaining a triple set.
As an example, the user tag weight is "0.6". The above-described association degree may be "0.8". An item label weight in the item label weight set corresponding to the user label weight is "0.52". Combining each user tag weight in the user tag weight set, the association degree corresponding to the user tag weight, and the item tag weight in the item tag weight set corresponding to the user tag weight to generate a triplet of (0.6, 0.8, 0.52). The user tag weight is "0.4". The above-described association degree may be "0.8". An item label weight in the item label weight set corresponding to the user label weight is "0.48". And combining each user label weight in the association degree set, the association degree corresponding to the user label weight and the item label weight in the item label weight set corresponding to the user label weight to generate a triple of (0.4, 0.8, 0.48). The ternary set is obtained as follows: { (0.6, 0.8, 0.52); (0.4,0.8,0.48)}.
And secondly, establishing a tag relation information empty table, inputting each triple in the triple set into the tag relation information empty table, and generating the tag relation information table.
As an example, a triple set may be: { (0.6, 0.8, 0.52); (0.4,0.8,0.48)}. Establishing a tag relation information empty table, inputting each triplet in the triplet set into the tag relation information empty table, and generating a tag relation information table:
| tag number | User tag weight | Degree of association | Item tag weight |
| 1 | 0.6 | 0.8 | 0.52 |
| 2 | 0.4 | 0.8 | 0.48 |
Optionally, the display device controlling the communication connection displays the tag relationship information table.
Optionally, the operating device controlling the communication connection with the display device constructs the user representation based on the tag relation information table.
As an example, the display device "001" that controls communication connection displays the above-described tag relationship information table. The operation device can construct the user portrait according to the weight of each label and the relation between each label in the label relation information table. For example, a user representation representing a label name may be resized or repositioned based on the relationship between label weights and labels.
One of the above-described various embodiments of the present disclosure has the following advantageous effects: first, a user tag name of each user tag information in the user tag information set and an article tag name of each article tag information in the article tag information set are respectively subjected to vectorization processing to generate a user tag name vector and an article tag name vector, and a user tag name vector set and an article tag name vector set are obtained. Then, the association degree between each user tag name vector in the user tag name vector set and the article tag name vector in the article tag name vector set corresponding to the user tag name vector is determined, and an association degree set is obtained. Therefore, the degree of closeness among the labels can be determined according to the relevance in the relevance set, and a foundation is established for subsequently constructing the user portrait. Then, the executing body may perform numerical processing on each user tag score value in the user tag score value set and the user tag use frequency value corresponding to the user tag score value, generate a user tag weight, and obtain a user tag weight set. And performing numerical processing on each item label scoring value in the item label scoring value set and the item label use frequency value corresponding to the item label scoring value to generate an item label weight, so as to obtain an item label weight set. Optionally, the execution subject may combine each user tag weight in the association set, the association degree corresponding to the user tag weight, and an item tag weight in the item tag weight set corresponding to the user tag weight to generate a triple, so as to obtain a triple set. The execution body may establish a tag relationship information empty table, and input each triplet in the triplet set into the tag relationship information empty table to generate the tag relationship information table. The influence of the relationship between the user tag and the article tag on the user portrait can be comprehensively considered through the tag relationship information table. Finally, the execution body may resize or resize the user representation representing the tag name according to a relationship between the tag weight and the tag. Thus, the system is facilitated to provide customized services for user portrayal of users.
With further reference to fig. 3, a flow 300 of further embodiments of a method of operation of an apparatus according to the present disclosure is shown. The method may be performed by the computing device 101 of fig. 1. The device operating method comprises the following steps:
In some embodiments, the specific implementation manner and technical effects of steps 301 and 303 can refer to steps 201 and 203 in the embodiments corresponding to fig. 2, which are not described herein again.
In some embodiments, the executive may perform non-dimensionalization on the user tag score value to generate a non-dimensionalized user tag score value.
As an example, each user tag score value in the user tag information set described above may be "6, 5". The user label scoring value "6" in the user label information set is subjected to non-dimensionalization processing to obtain a non-dimensionalized user label scoring value: "0.545". Here, the numeric value after the dimensionless processing is not limited to a range.
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 user tag information set may be "{ 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 execution main body may input the number of user tag names of the respective user tag information, the non-dimensionalized user tag score value, and the user tag corresponding to the user tag score value to the following formula using a frequency value to generate a user tag weight:
wherein the content of the first and second substances,
and a user tag weight indicating user tag information including the user tag score value.
Representing the above-mentioned non-dimensionalized user tag score value.
And indicating the user label use frequency value corresponding to the user label scoring value.
The number of user tag information included in the user tag information set is indicated.
Is shown as
The user tag scoring value is non-dimensionalized for individual user tag information.
Is shown as
The user tag usage frequency value of the individual user tag information.
Representing a parameter with a value of 0.081819. Here, the user tag weight decimal pointThe latter ranges are not limiting. Here, the first and second liquid crystal display panels are,
the value range of (2) is (0, 1), and the specific value varies with the number of the item tag information included in the item tag information set. The greater the number of the one or more,
the larger the value of (a) is,
the initial value of (a) is not limited within the range of values.
As an example, each non-dimensionalized user tag score value in the set of non-dimensionalized user tag score values may be "{ 0.545}, {0.454 }". The non-dimensionalized user tag score of the user tag information
Is "0.545". Number of user tag information included in user tag information set
Is "2". The user tag usage frequency value corresponding to each non-dimensionalized user tag score value in the non-dimensionalized user tag score value set may be "{ 6 }; {5}". User tag usage frequency value corresponding to the user tag score value
Is "6". Inputting the values into a formula respectively to generate user label weights:
and obtaining the user label weight of {0.586 }.
In some embodiments, the executive body may perform non-dimensionalization on the item tag score value to generate a non-dimensionalized item tag score value.
As an example, each user tag scoring value in the item tag information set may be "{ 6 }; {5}". Carrying out non-dimensionalization processing on the user label scoring value "6" in the item label information set to obtain a non-dimensionalized item label scoring value: "0.545". Here, the numeric value after the dimensionless processing is not limited to a range.
In some embodiments, the execution body may directly determine the number of article tag names of the respective article tag information in the article tag information set.
As an example, the above item tag information set may be "{ didy; 5 min; 12 times }, { exercise wheel; 4, dividing; 10 times } ". It is determined that the number of item tag information included in the item tag information set is "2".
In some embodiments, the executor may input the number of item tag information included in the item tag information set, the non-dimensionalized item tag score value, and an item tag usage frequency value corresponding to the item tag score value into the following formula to generate an item tag weight:
wherein the content of the first and second substances,
a user tag weight indicating the item tag information including the item tag score value.
And (3) representing the label scoring value of the non-dimensionalized article.
And indicating the item label use frequency value corresponding to the item label scoring value.
Indicating the number of item tag information included in the item tag information set.
Is shown as
A non-dimensionalized item tag score value for individual item tag information.
Is shown as
The item tag usage frequency value of the individual item tag information.
Representing a parameter with a value of 0.081819. Here, the value range after the weight decimal point of the item label is not limited.
As an example, each non-dimensionalized item label score value in the set of non-dimensionalized item label scores may be {0.545}, {0.454 }. The non-dimensionalized label score of the label information
Is "0.545". Number of article tag information included in article tag information set
Is "2". The item tag usage frequency value corresponding to each non-dimensionalized item tag score value in the non-dimensionalized item tag score value set may be "{ 6}, {5 }". Article tag use frequency value corresponding to the article tag score value
Is "6". Inputting the above values into a formula to generate item label weights:
the resulting item tag weight is {0.586 }.
One of the above-described various embodiments of the present disclosure has the following advantageous effects: firstly, carrying out non-dimensionalization processing on the user label scoring value of each user label information in the user label information set and the article label scoring value of each article label information in the article label information set respectively to generate a non-dimensionalized user label scoring value and a non-dimensionalized article label scoring value, and obtaining a non-dimensionalized user label scoring value set and a non-dimensionalized article label scoring value set. By performing the dimensionless processing, the accuracy of the calculation result can be improved. Then, the number of user tag names of the respective user tag information, the dimensionless user tag score value, and the user tag use frequency value are input to a formula to generate a user tag weight. The user label weight generated by the formula can improve the accuracy of the label weight and is beneficial to adjusting the user label. Similarly, the item label weight generated by the formula is beneficial to adjusting the item label.
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 a device operating apparatus, which correspond to those of the method embodiments described above for fig. 2, and which may be applied in particular to various electronic devices.
As shown in fig. 4, the device operating apparatus 400 of some embodiments includes: an acquisition unit 401, a first generation unit 402, a determination unit 403, a second generation unit 404, and a third 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 perform vectorization processing on the user tag name of each user tag information in the user tag information set and the article tag name of each article tag information in the article tag information set to generate a user tag name vector and an article tag name vector, respectively, resulting in a user tag name vector set and an article tag name vector set. A determining unit 403 configured to determine a degree of association between each user tag name vector in the set of user tag name vectors and an item tag name vector in the set of item tag name vectors corresponding to the user tag name vector, resulting in a set of degrees of association. A second generating unit 404, configured to generate a user tag weight based on each user tag score value in the user tag information set and a user tag usage frequency value corresponding to the user tag score value, so as to obtain a user tag weight set. A third generating unit 405 configured to generate an item label weight based on each item label scoring value in the item label information set and an item label usage frequency value corresponding to the item label scoring value, so as to obtain an item label weight set.
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 be interconnected 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; vectorizing the user tag name of each piece of user tag information in the user tag information set and the article tag name of each piece of article tag information in the article tag information set respectively to generate a user tag name vector and an article tag name vector, and obtaining a user tag name vector set and an article tag name vector set; determining the association degree between each user tag name vector in the user tag name vector set and the object tag name vector in the object tag name vector set corresponding to the user tag name vector to obtain an association degree set; generating user label weights based on each user label scoring value in the user label information set and the user label use frequency value corresponding to the user label scoring value to obtain a user label weight set; and generating an article label weight based on each article label scoring value in the article label information set and the article label use frequency value corresponding to the article label scoring value to obtain an article 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 determination unit, a second generation unit, and a third generation unit. Where the names of these units do not in some cases constitute a limitation on the unit itself, for example, the determination unit may also be described as "a unit that determines a degree of association between each user tag name vector in the above-mentioned set of user tag name vectors and an item tag name vector in the above-mentioned set of item tag name vectors corresponding to the above-mentioned user tag name vector, resulting in a set of degrees of association".
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 (6)
1. A method of device operation, 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;
vectorizing the user tag name of each piece of user tag information in the user tag information set and the article tag name of each piece of article tag information in the article tag information set respectively to generate a user tag name vector and an article tag name vector, and obtaining a user tag name vector set and an article tag name vector set;
determining the association degree between each user tag name vector in the user tag name vector set and the object tag name vector in the object tag name vector set corresponding to the user tag name vector to obtain an association degree set;
generating user label weights based on each user label scoring value in the user label information set and the user label use frequency value corresponding to the user label scoring value to obtain a user label weight set;
and generating an article label weight based on each article label scoring value in the article label information set and the article label use frequency value corresponding to the article label scoring value to obtain an article label weight set.
2. The method of claim 1, wherein the method further comprises:
generating a label relation information table based on the association degree set, the user label weight set and the article label weight set;
controlling a display device in communication connection to display the label relation information table;
and controlling an operating device in communication connection with the display device to construct the user portrait based on the label relation information table.
3. The method of claim 2, wherein generating a tag relationship information table based on the set of relevancy, the set of user tag weights, and the set of item tag weights comprises:
combining each user label weight in the user label weight set, the association degree corresponding to the user label weight and the item label weight in the item label weight set corresponding to the user label weight to generate a triple, so as to obtain a triple set;
and establishing a tag relation information empty table, inputting each triple in the triple set into the tag relation information empty table, and generating a tag relation information table.
4. An apparatus operating device 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 perform vectorization processing on a user tag name of each user tag information in the user tag information set and an article tag name of each article tag information in the article tag information set respectively to generate a user tag name vector and an article tag name vector, resulting in a user tag name vector set and an article tag name vector set;
a determination unit configured to determine a degree of association between each user tag name vector in the set of user tag name vectors and an item tag name vector in the set of item tag name vectors corresponding to the user tag name vector, resulting in a set of degrees of association;
a second generating unit, configured to generate a user tag weight based on each user tag scoring value in the user tag information set and a user tag usage frequency value corresponding to the 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 item label scoring value in the item label information set and an item label use frequency value corresponding to the item label scoring value, resulting in an item label weight set.
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, cause the one or more processors to implement the method of any one of claims 1-3.
6. 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 1-3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010846242.8A CN111737587B (en) | 2020-08-21 | 2020-08-21 | Device operation method, device, electronic device and computer readable medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010846242.8A CN111737587B (en) | 2020-08-21 | 2020-08-21 | Device operation method, device, electronic device and computer readable medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111737587A true CN111737587A (en) | 2020-10-02 |
| CN111737587B CN111737587B (en) | 2020-12-11 |
Family
ID=72658642
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010846242.8A Active CN111737587B (en) | 2020-08-21 | 2020-08-21 | Device operation method, device, electronic device and computer readable medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111737587B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112288359A (en) * | 2020-10-28 | 2021-01-29 | 北京每日优鲜电子商务有限公司 | Abnormal article information positioning method and device, electronic equipment and computer medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109086787A (en) * | 2018-06-06 | 2018-12-25 | 平安科技（深圳）有限公司 | User's portrait acquisition methods, device, computer equipment and storage medium |
| US20190104341A1 (en) * | 2017-09-29 | 2019-04-04 | International Business Machines Corporation | Cognitive digital video filtering based on user preferences |
| CN110427563A (en) * | 2019-08-30 | 2019-11-08 | 杭州智策略科技有限公司 | A kind of professional domain system cold start-up recommended method of knowledge based map |
| CN110555747A (en) * | 2018-06-01 | 2019-12-10 | 北京京东尚科信息技术有限公司 | method and device for determining target user |
| CN111191713A (en) * | 2019-12-27 | 2020-05-22 | 大象慧云信息技术有限公司 | User portrait method and device based on invoice data |
| CN111310035A (en) * | 2020-02-03 | 2020-06-19 | 浙江连信科技有限公司 | Recommendation method and device based on psychological and behavioral characteristics |
-
2020
- 2020-08-21 CN CN202010846242.8A patent/CN111737587B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190104341A1 (en) * | 2017-09-29 | 2019-04-04 | International Business Machines Corporation | Cognitive digital video filtering based on user preferences |
| CN110555747A (en) * | 2018-06-01 | 2019-12-10 | 北京京东尚科信息技术有限公司 | method and device for determining target user |
| CN109086787A (en) * | 2018-06-06 | 2018-12-25 | 平安科技（深圳）有限公司 | User's portrait acquisition methods, device, computer equipment and storage medium |
| CN110427563A (en) * | 2019-08-30 | 2019-11-08 | 杭州智策略科技有限公司 | A kind of professional domain system cold start-up recommended method of knowledge based map |
| CN111191713A (en) * | 2019-12-27 | 2020-05-22 | 大象慧云信息技术有限公司 | User portrait method and device based on invoice data |
| CN111310035A (en) * | 2020-02-03 | 2020-06-19 | 浙江连信科技有限公司 | Recommendation method and device based on psychological and behavioral characteristics |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112288359A (en) * | 2020-10-28 | 2021-01-29 | 北京每日优鲜电子商务有限公司 | Abnormal article information positioning method and device, electronic equipment and computer medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111737587B (en) | 2020-12-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR102342604B1 (en) | Method and apparatus for generating neural network | |
| CN112035753B (en) | Recommendation page generation method and device, electronic equipment and computer readable medium | |
| CN112650841A (en) | Information processing method and device and electronic equipment | |
| CN111737587B (en) | Device operation method, device, electronic device and computer readable medium | |
| CN111932321B (en) | Method and device for pushing article information for user, electronic equipment and medium | |
| CN112036990B (en) | Article information pushing method and device, electronic equipment and computer readable medium | |
| CN113051933B (en) | Model training method, text semantic similarity determination method, device and equipment | |
| CN112365109B (en) | Distribution path information generation method and device, electronic equipment and readable medium | |
| CN112381184B (en) | Image detection method, image detection device, electronic equipment and computer readable medium | |
| CN111563117B (en) | Structured information display method and device, electronic equipment and computer readable medium | |
| CN111950907B (en) | Information generation method, apparatus, electronic device and computer readable medium | |
| CN114021010A (en) | Training method, device and equipment of information recommendation model | |
| CN113723095A (en) | Text auditing method and device, electronic equipment and computer readable medium | |
| CN112200643B (en) | Article information pushing method and device, electronic equipment and computer readable medium | |
| CN112464039A (en) | Data display method and device of tree structure, electronic equipment and medium | |
| CN112529672B (en) | Article information pushing method and device, electronic equipment and computer readable medium | |
| CN112073202B (en) | Information generation method and device, electronic equipment and computer readable medium | |
| CN114040014B (en) | Content pushing method, device, electronic equipment and computer readable storage medium | |
| CN111625692B (en) | Feature extraction method, device, electronic equipment and computer readable medium | |
| CN113077352B (en) | Insurance service article recommending method based on user information and insurance related information | |
| CN117215631A (en) | Method for generating object tree through configuration, data judging method and device | |
| CN112270170A (en) | Analysis method, device, medium and electronic equipment for implicit expression statement | |
| CN116074008A (en) | Data processing method, device, electronic equipment and computer readable medium | |
| CN115277421A (en) | Configuration information pushing method and device, electronic equipment and computer readable medium | |
| CN117112490A (en) | Target model file generation method, value information generation method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20231109 Address after: 518000, 705, No. 121 Minsheng Avenue, Shangcun Community, Gongming Street, Guangming District, Shenzhen City, Guangdong Province Patentee after: Shenzhen Hongli Intellectual Property Service Co.,Ltd. Address before: 100102 room 801, 08 / F, building 7, yard 34, Chuangyuan Road, Chaoyang District, Beijing Patentee before: BEIJING MISSFRESH E-COMMERCE Co.,Ltd. |