CN111242658A - Information sharing reward method and device and computer readable storage medium - Google Patents

Information sharing reward method and device and computer readable storage medium Download PDF

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Publication number
CN111242658A
CN111242658A CN201811438804.4A CN201811438804A CN111242658A CN 111242658 A CN111242658 A CN 111242658A CN 201811438804 A CN201811438804 A CN 201811438804A CN 111242658 A CN111242658 A CN 111242658A
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China
Prior art keywords
sharing
information
parameters
social
traffic
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CN201811438804.4A
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Chinese (zh)
Inventor
张之硕
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Beijing Jingdong Century Trading Co Ltd
Beijing Jingdong Shangke Information Technology Co Ltd
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Beijing Jingdong Century Trading Co Ltd
Beijing Jingdong Shangke Information Technology Co Ltd
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Priority to CN201811438804.4A priority Critical patent/CN111242658A/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
    • G06Q30/0214—Referral reward systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
    • G06Q30/0211—Determining the effectiveness of discounts or incentives

Abstract

The disclosure provides an information sharing reward method, an information sharing reward device and a computer readable storage medium, and relates to the technical field of information processing, wherein the method comprises the following steps: obtaining social parameters of sharing users sharing the first information; inputting the social parameters into a traffic prediction model to predict traffic parameters corresponding to the sharing users; and sending the flow parameter to an information source of the first information, so that the information source rewards the sharing user according to the flow parameter.

Description

Information sharing reward method and device and computer readable storage medium
Technical Field
The present disclosure relates to the field of information processing technologies, and in particular, to an information sharing reward method, an information sharing reward device, and a computer-readable storage medium.
Background
When the E-commerce is popularized, the number of new users and the exposure of the E-commerce can be increased by sharing the popularization page by the users, so that the popularization effect is achieved.
In the related art, the user can obtain the reward, such as obtaining a red envelope or increasing the lottery probability, by sharing the promotion page.
Disclosure of Invention
The inventor notices that in the current mode, the effect of users with different friends after sharing the promotion page or the activity page is greatly different, but the users return to the e-commerce application or the website after sharing and then obtain the same reward. The mode can reduce the enthusiasm of sharing by users, and is not beneficial to popularization of the e-commerce.
In order to solve the above problem, the embodiments of the present disclosure propose the following solutions.
According to an aspect of the embodiments of the present disclosure, there is provided an information sharing reward method, including: obtaining social parameters of sharing users sharing the first information; inputting the social parameters into a traffic prediction model to predict traffic parameters corresponding to the sharing users; and sending the flow parameter to an information source of the first information, so that the information source rewards the sharing user according to the flow parameter.
In some embodiments, the traffic prediction model is trained according to the following: obtaining social parameters of sample users sharing second information; counting the historical flow parameters of the sample users after sharing the second information; and training the flow prediction model by taking the social parameters of the sample user as input and the historical flow parameters as output.
In some embodiments, the method further comprises: counting the actual flow parameters of the sharing users after sharing the first information; and training the flow prediction model by taking the social parameters of the sharing users as input and the actual flow parameters as output.
In some embodiments, the social parameters include one or more of the following: the gender and number of social objects, the number of views of information once shared, and the number of interactions.
In some embodiments, the first information is a website link, and the traffic parameter is the number of times the website link is clicked, or the traffic brought to the information source after the website link is clicked.
In some embodiments, the traffic prediction model is a linear model between traffic parameters and social parameters.
According to another aspect of the embodiments of the present disclosure, there is provided an information sharing bonus device, including: the acquisition module is used for acquiring social parameters of sharing users sharing the first information; the prediction module is used for inputting the social parameters into a traffic prediction model so as to predict traffic parameters corresponding to the sharing users; and the sending module is used for sending the flow parameters to an information source of the first information so that the information source rewards the sharing users according to the flow parameters.
In some embodiments, the obtaining module is further configured to obtain social parameters of a sample user sharing the second information; the device further comprises: the statistical module is used for counting the historical flow parameters of the sample users after sharing the second information; and the training module is used for training the flow prediction model by taking the social parameters of the sample user as input and the historical flow parameters as output.
In some embodiments, the statistics module is further configured to count an actual traffic parameter of the sharing user after sharing the first information; the training module is further used for training the flow prediction model by taking the social parameters of the sharing users as input and the actual flow parameters as output.
In some embodiments, the social parameters include one or more of the following: the gender and number of social objects, the number of views of information once shared, and the number of interactions.
In some embodiments, the first information is a website link, and the traffic parameter is the number of times the website link is clicked, or the traffic brought to the information source after the website link is clicked.
In some embodiments, the traffic prediction model is a linear model between traffic parameters and social parameters.
According to another aspect of the embodiments of the present disclosure, there is provided an information sharing bonus apparatus including: a memory; and a processor coupled to the memory, the processor configured to perform the method of any of the above embodiments based on instructions stored in the memory.
According to yet another aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the method according to any one of the embodiments described above.
In the embodiment of the disclosure, the traffic parameter of the sharing user can be predicted according to the social parameter of the sharing user by using the traffic prediction model, and the traffic parameter of the sharing user can be used as a reference for rewarding the sharing user. The mode can improve the sharing enthusiasm of users and is beneficial to information sources, such as the popularization of e-commerce.
The technical solution of the present disclosure is further described in detail by the accompanying drawings and examples.
Drawings
In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flow diagram of an information sharing reward method according to some embodiments of the present disclosure;
FIG. 2 is a schematic flow diagram of a method of training a traffic prediction model, according to some embodiments of the present disclosure;
FIG. 3 is a schematic diagram of an information sharing reward device according to some embodiments of the present disclosure;
FIG. 4 is a schematic diagram of an information sharing reward device according to further embodiments of the present disclosure;
fig. 5 is a schematic diagram of an information sharing reward device according to further embodiments of the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
The relative arrangement of the components and steps, the numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.
Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
Fig. 1 is a flow diagram of an information sharing incentive method according to some embodiments of the present disclosure.
In step 102, social parameters of a sharing user sharing the first information are obtained.
Here, the first information may be, for example, a website link, such as a promotion page or an activity page of an e-commerce. The social parameters may include one or more of the following: the gender and number of social objects, the number of views of information once shared, and the number of interactions.
For example, after the sharing user shares the first information to a social group (e.g., a WeChat group) or a personal social homepage (e.g., a circle of friends), the social parameters of the sharing user may be invoked in real time through the server interface.
Taking a circle of friends as an example, the social parameters may include the number of male friends sharing the user, the number of female friends, the number of views of the circle of friends, the number of interactions (e.g., the number of praise, the number of comments) of the circle of friends, and so on.
In step 104, the social parameters are input into the traffic prediction model to predict the traffic parameters corresponding to the sharing users.
Here, the traffic prediction model is a trained model that can predict the corresponding traffic parameters from the input social parameters. In some embodiments, the traffic prediction model may be, for example, a linear model between traffic parameters and social parameters. However, the present disclosure is not so limited, and the traffic prediction model may be other machine learning models, such as a neural network model.
In the case that the first information is a website link, the traffic parameter may be, for example, the number of times the website link is clicked, or the traffic parameter may be traffic brought to an information source of the first information after the website link is clicked. As an example, the information source of the first information may be, for example, an e-commerce APP or other promoted APP.
In some embodiments, the traffic parameter may also be other parameters such as the number of users who registered by clicking on the first information, the number of users who shop after registration, and so on.
At step 106, the traffic parameter is sent to the information source of the first message, so that the information source rewards the enjoyment user according to the traffic parameter.
For example, the larger the traffic parameter corresponding to the sharing user is, the larger the reward given to the sharing user is, for example, the larger the number of red parcels given to the sharing user is, or the larger the probability that the sharing user draws a prize is.
In the above embodiment, the traffic parameter of the sharing user can be predicted according to the social parameter of the sharing user by using the traffic prediction model, and the traffic parameter of the sharing user can be used as a reference for rewarding the sharing user. The mode can improve the sharing enthusiasm of users and is beneficial to information sources, such as the popularization of e-commerce.
Fig. 2 is a flow diagram of a method of training a traffic prediction model according to some embodiments of the present disclosure.
In step 202, social parameters of sample users sharing the second information are obtained.
Here, the second information may be, for example, a website link, such as a promotion page or an activity page of an e-commerce. The social parameters of a plurality of sample users can be obtained, and a plurality of social parameters of the same sample user can also be obtained.
In step 204, historical traffic parameters of the sample user after sharing the second information are counted.
For example, after the sample user shares the second information and a period of time elapses, the traffic parameter caused by the sharing, that is, the historical traffic parameter, may be counted.
In step 206, a traffic prediction model is trained with the social parameters of the sample user as input and the historical traffic parameters as output.
In some embodiments, in order to improve the accuracy of the prediction of the traffic prediction model, the actual traffic parameters of the sharing users after sharing the first information may be counted; and then training the flow prediction model by taking the social parameters of the sharing users as input and the actual flow parameters as output.
Assuming that the traffic parameter is y, different social parameters (e.g., number of friends in male, number of friends in female, number of friends in circle browsing, number of friends in circle interacting, etc.) may be respectively represented as x1,x2,x3,x4,x5,x6,x7,x8…, y and x1,x2,x3,x4,x5,x6,x7,x8… may be expressed as follows:
y＝θ0+θ1x1+θ2x2+θ3x3+θ4x4+θ5x5+θ6x6+θ7x7+θ8x8+…
in each group x1,x2,x3,x4,x5,x6,x7,x8… as inputs, each group x1,x2,x3,x4,x5,x6,x7,x8… as output, training the linear model to obtain theta0,θ1,θ2,θ3,θ4,θ5,θ6,θ7,θ8…, so that the relationship between traffic parameters and social parameters can be found.
The above description is given only by taking the flow rate prediction model as a linear model. Those skilled in the art will appreciate that the traffic prediction model may also be other machine learning models, such as a neural network model. Similarly, the neural network model may be trained with the social parameters of the sample user as input and the historical traffic parameters as output, which is not described herein again.
In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts in the embodiments are referred to each other. For the device embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
Fig. 3 is a schematic structural diagram of an information sharing reward device according to some embodiments of the present disclosure.
As shown in fig. 3, the apparatus of this embodiment includes an obtaining module 301, a predicting module 302, and a sending module 303.
The obtaining module 301 is configured to obtain social parameters of a sharing user sharing the first information.
The social parameters include one or more of the following: the gender and number of social objects, the number of views of information once shared, and the number of interactions.
The prediction module 302 is configured to input the social parameters into the traffic prediction model to predict traffic parameters corresponding to the sharing users. The traffic prediction model may be, for example, a linear model between traffic parameters and social parameters.
For example, the first information may be a website link, and the traffic parameter may be the number of times the website link is clicked, or the traffic parameter may be traffic brought to the information source after the website link is clicked.
The sending module 303 is configured to send the traffic parameter to an information source of the first information, so that the information source rewards the sharing user according to the traffic parameter.
In the above embodiment, the traffic parameter of the sharing user can be predicted according to the social parameter of the sharing user by using the traffic prediction model, and the traffic parameter of the sharing user can be used as a reference for rewarding the sharing user. The mode can improve the sharing enthusiasm of users and is beneficial to information sources, such as the popularization of e-commerce.
Fig. 4 is a schematic structural diagram of an information sharing reward device according to further embodiments of the present disclosure. Compared to the embodiment shown in fig. 3, the apparatus of this embodiment further comprises a statistics module 401 and a training module 402.
The obtaining module 301 of this embodiment is further configured to obtain social parameters of the sample user. The statistical module 401 is configured to count the historical traffic parameters of the sample users after sharing the second information. The training module 402 is configured to train the traffic prediction model with the social parameters of the sample user as input and the historical traffic parameters as output.
In some embodiments, the statistics module 401 is further configured to count an actual traffic parameter of the sharing user after sharing the first information. The training module 402 is further configured to train the traffic prediction model with the social parameters of the sharing users as input and the actual traffic parameters as output.
Fig. 5 is a schematic diagram of an information sharing reward device according to further embodiments of the present disclosure.
As shown in fig. 5, the apparatus 500 of this embodiment includes a memory 501 and a processor 502 coupled to the memory 501, and the processor 502 is configured to execute the method of any one of the foregoing embodiments based on instructions stored in the memory 501.
The memory 501 may include, for example, a system memory, a fixed non-volatile storage medium, and the like. The system memory may store, for example, an operating system, application programs, a Boot Loader (Boot Loader), and other programs.
The apparatus 500 may also include an input-output interface 503, a network interface 504, a storage interface 505, and the like. The interfaces 503, 504, 505 and the memory 501 and the processor 502 may be connected by a bus 506, for example. The input/output interface 503 provides a connection interface for input/output devices such as a display, a mouse, a keyboard, and a touch screen. The network interface 504 provides a connection interface for various networking devices. The storage interface 505 provides a connection interface for external storage devices such as an SD card and a usb disk.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only exemplary of the present disclosure and is not intended to limit the present disclosure, so that any modification, equivalent replacement, or improvement made within the spirit and principle of the present disclosure should be included in the scope of the present disclosure.

Claims (14)

1. An information sharing reward method, comprising:
obtaining social parameters of sharing users sharing the first information;
inputting the social parameters into a traffic prediction model to predict traffic parameters corresponding to the sharing users;
and sending the flow parameter to an information source of the first information, so that the information source rewards the sharing user according to the flow parameter.
2. The method of claim 1, wherein the flow prediction model is trained according to:
obtaining social parameters of sample users sharing second information;
counting the historical flow parameters of the sample users after sharing the second information;
and training the flow prediction model by taking the social parameters of the sample user as input and the historical flow parameters as output.
3. The method of claim 2, further comprising:
counting the actual flow parameters of the sharing users after sharing the first information;
and training the flow prediction model by taking the social parameters of the sharing users as input and the actual flow parameters as output.
4. The method of claim 1, wherein the social parameters comprise one or more of:
the gender and number of social objects, the number of views of information once shared, and the number of interactions.
5. The method of claim 1, wherein the first information is a website link, and the traffic parameter is a number of times the website link is clicked, or a traffic brought to the information source after the website link is clicked.
6. The method of any of claims 1-5, wherein the traffic prediction model is a linear model between traffic parameters and social parameters.
7. An information sharing reward device comprising:
the acquisition module is used for acquiring social parameters of sharing users sharing the first information;
the prediction module is used for inputting the social parameters into a traffic prediction model so as to predict traffic parameters corresponding to the sharing users;
and the sending module is used for sending the flow parameters to an information source of the first information so that the information source rewards the sharing users according to the flow parameters.
8. The apparatus of claim 7, wherein the obtaining module is further configured to obtain social parameters of a sample user sharing second information;
the device further comprises:
the statistical module is used for counting the historical flow parameters of the sample users after sharing the second information;
and the training module is used for training the flow prediction model by taking the social parameters of the sample user as input and the historical flow parameters as output.
9. The apparatus of claim 8, wherein,
the statistic module is further used for counting the actual flow parameters of the sharing users after sharing the first information;
the training module is further used for training the flow prediction model by taking the social parameters of the sharing users as input and the actual flow parameters as output.
10. The apparatus of claim 7, wherein the social parameters comprise one or more of:
the gender and number of social objects, the number of views of information once shared, and the number of interactions.
11. The apparatus of claim 7, wherein the first information is a website link, and the traffic parameter is a number of times the website link is clicked, or a traffic brought to the information source after the website link is clicked.
12. The apparatus of any of claims 7-11, wherein the traffic prediction model is a linear model between traffic parameters and social parameters.
13. An information sharing reward device comprising:
a memory; and
a processor coupled to the memory, the processor configured to perform the method of any of claims 1-6 based on instructions stored in the memory.
14. A computer readable storage medium having computer program instructions stored thereon, wherein the instructions, when executed by a processor, implement the method of any of claims 1-6.
CN201811438804.4A 2018-11-29 2018-11-29 Information sharing reward method and device and computer readable storage medium Pending CN111242658A (en)

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111340573A (en) * 2020-05-15 2020-06-26 支付宝(杭州)信息技术有限公司 Method and device for processing shared service
CN111914170A (en) * 2020-07-17 2020-11-10 中国联合网络通信集团有限公司 Method, system, terminal device and computer readable storage medium for issuing preferential
CN111931106A (en) * 2020-07-29 2020-11-13 上海博泰悦臻电子设备制造有限公司 Data processing method and related device
CN112767051A (en) * 2021-01-29 2021-05-07 支付宝(杭州)信息技术有限公司 Business activity processing method, device, equipment and storage medium

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140025448A1 (en) * 2012-07-20 2014-01-23 Social2Step, LLC Method and apparatus for providing a marketing engine
CN105825397A (en) * 2016-03-15 2016-08-03 北京小月科技有限公司 Link publishing method
CN107330562A (en) * 2017-07-03 2017-11-07 扬州大学 Information dissemination method based on individual consumer's feature
CN107609889A (en) * 2017-08-01 2018-01-19 百度在线网络技术（北京）有限公司 A kind of method and device for encouraging user's sharing web page
CN108304867A (en) * 2018-01-24 2018-07-20 重庆邮电大学 Information popularity prediction technique towards social networks and system

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140025448A1 (en) * 2012-07-20 2014-01-23 Social2Step, LLC Method and apparatus for providing a marketing engine
CN105825397A (en) * 2016-03-15 2016-08-03 北京小月科技有限公司 Link publishing method
CN107330562A (en) * 2017-07-03 2017-11-07 扬州大学 Information dissemination method based on individual consumer's feature
CN107609889A (en) * 2017-08-01 2018-01-19 百度在线网络技术（北京）有限公司 A kind of method and device for encouraging user's sharing web page
CN108304867A (en) * 2018-01-24 2018-07-20 重庆邮电大学 Information popularity prediction technique towards social networks and system

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111340573A (en) * 2020-05-15 2020-06-26 支付宝(杭州)信息技术有限公司 Method and device for processing shared service
CN111914170A (en) * 2020-07-17 2020-11-10 中国联合网络通信集团有限公司 Method, system, terminal device and computer readable storage medium for issuing preferential
CN111914170B (en) * 2020-07-17 2023-06-20 中国联合网络通信集团有限公司 Coupon issuing method, coupon issuing system, terminal device and computer readable storage medium
CN111931106A (en) * 2020-07-29 2020-11-13 上海博泰悦臻电子设备制造有限公司 Data processing method and related device
CN112767051A (en) * 2021-01-29 2021-05-07 支付宝(杭州)信息技术有限公司 Business activity processing method, device, equipment and storage medium
CN112767051B (en) * 2021-01-29 2022-12-06 支付宝(杭州)信息技术有限公司 Business activity processing method, device, equipment and storage medium

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