CN112214306A

CN112214306A - Content display weight value calculation method and device, electronic equipment and computer readable storage medium

Info

Publication number: CN112214306A
Application number: CN201910625730.3A
Authority: CN
Inventors: 何博威; 王晓宇; 杜振林; 洪春晓
Original assignee: Beijing ByteDance Network Technology Co Ltd
Current assignee: Beijing ByteDance Network Technology Co Ltd
Priority date: 2019-07-11
Filing date: 2019-07-11
Publication date: 2021-01-12

Abstract

The disclosure discloses a method and a device for calculating a content display weight value, electronic equipment and a computer-readable storage medium. The method for calculating the content display weight value comprises the following steps: acquiring content to be displayed; acquiring the number of deep interaction times of the content to be displayed, wherein the number of deep interaction times is the number of deep interaction times of a user and the content to be displayed; judging whether the number of the deep interaction times is smaller than a first threshold value; responding to the depth interaction times smaller than the first threshold value, and acquiring a first probability value and a second probability value of the content to be displayed; and calculating a display weight value according to the first probability value, the second probability value and the depth interaction times. By judging the number of deep interaction times and selecting the calculation method of the display weight in the method, the technical problem of how to select proper content from a plurality of contents to display to a user in the prior art is solved.

Description

Content display weight value calculation method and device, electronic equipment and computer readable storage medium

Technical Field

The present disclosure relates to the field of information processing, and in particular, to a method and an apparatus for calculating a weight value of a content display, an electronic device, and a computer-readable storage medium.

Background

As computer networks have evolved, networks have become an increasingly rich presentation, distribution platform, such as in electronic commerce, video platforms, and the like. In the prior art, a fixed publishing position is generally used to publish fixed content, such as placing a plurality of video contents on a homepage of a video platform for a user to select. However, how to select a suitable content from a large number of contents to display to a user becomes a technical problem to be solved urgently.

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.

In a first aspect, an embodiment of the present disclosure provides a method for calculating a weight value of a content display, including:

acquiring content to be displayed; acquiring the number of deep interaction times of the content to be displayed, wherein the number of deep interaction times is the number of deep interaction times of a user and the content to be displayed; judging whether the number of the deep interaction times is smaller than a first threshold value; responding to the number of the deep interaction times smaller than the first threshold, and acquiring a first probability value and a second probability value of the content to be displayed, wherein the first probability value is a probability value of first interaction between a user and the content to be displayed, and the second probability value is a probability value of deep interaction between the user and the content to be displayed; and calculating a display weight value according to the first probability value, the second probability value and the depth interaction times, wherein the display weight value is used for determining whether the content to be displayed is displayed or not.

In a second aspect, an embodiment of the present disclosure provides a content display method, including: acquiring a plurality of contents to be displayed; obtaining a plurality of display weight values of a plurality of contents to be displayed to obtain a plurality of display weight values, wherein the plurality of display weight values correspond to the plurality of contents to be displayed one by one, and the plurality of display weight values comprise the display weight values calculated according to the method in claim 1 and/or the display weight values calculated according to the method in claim 2; calculating to obtain a maximum display weight value of the plurality of display weight values; and displaying the content to be displayed corresponding to the maximum display weight value.

In a third aspect, an embodiment of the present disclosure provides a content display weight value calculation apparatus, including:

the first content to be displayed acquisition module is used for acquiring content to be displayed; the system comprises a deep interaction frequency acquisition module, a display module and a display module, wherein the deep interaction frequency acquisition module is used for acquiring the deep interaction frequency of the content to be displayed, and the deep interaction frequency is the frequency of the deep interaction between a user and the content to be displayed; the judging module is used for judging whether the number of the deep interaction times is smaller than a first threshold value; the first display weight parameter acquisition module is used for acquiring a first probability value and a second probability value of the content to be displayed in response to the number of the deep interaction times being smaller than the first threshold, wherein the first probability value is a probability value of first interaction between a user and the content to be displayed, and the second probability value is a probability value of deep interaction between the user and the content to be displayed; and the first display weight value calculating module is used for calculating a display weight value according to the first probability value, the second probability value and the depth interaction times, wherein the display weight value is used for determining whether the content to be displayed is displayed or not.

In a fourth aspect, an embodiment of the present disclosure provides a content display apparatus, including: the second content to be displayed acquisition module is used for acquiring a plurality of contents to be displayed; a display weight obtaining module, configured to obtain a plurality of display weight values of a plurality of contents to be displayed, so as to obtain the plurality of display weight values, where the plurality of display weight values correspond to the plurality of contents to be displayed one by one, and the plurality of display weight values include a display weight value calculated according to the method of claim 1 and/or a display weight value calculated according to the method of claim 2; a maximum display weight value calculation module for calculating to obtain a maximum display weight value of the plurality of display weight values; and the content display module is used for displaying the content to be displayed corresponding to the maximum display weight value.

In a fifth aspect, an embodiment of the present disclosure provides an electronic device, including: at least one processor; and the number of the first and second groups,

a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform any one of the methods of the preceding aspects.

In a sixth aspect, the disclosed embodiments provide a non-transitory computer-readable storage medium characterized by storing computer instructions for causing a computer to perform any of the methods of the preceding aspects.

The foregoing is a summary of the present disclosure, and for the purposes of promoting a clear understanding of the technical means of the present disclosure, the present disclosure may be embodied in other specific forms without departing from the spirit or essential attributes thereof.

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 flowchart of an embodiment of a method for calculating a weight value for content presentation provided by the present disclosure;

fig. 2 is a flowchart illustrating a specific example of step S104 in an embodiment of a method for calculating a weight value of a content display provided by the present disclosure;

fig. 3 is a flowchart illustrating a specific example of step S106 of the method for calculating a weight value of a content display provided by the present disclosure;

FIG. 4 is a flow chart of an embodiment of a content presentation method provided by the present disclosure;

FIG. 5 is a schematic structural diagram of an embodiment of a computing device for displaying weight values in content according to the present disclosure;

FIG. 6 is a schematic structural diagram of an embodiment of a content display apparatus according to an embodiment of the present disclosure;

fig. 7 is a schematic structural diagram of an electronic device provided according to an embodiment 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 present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather 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 understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.

The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.

It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.

It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.

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.

Fig. 1 is a flowchart of an embodiment of a method for calculating a content exhibition weight value according to an embodiment of the present disclosure, where the method for calculating a content exhibition weight value according to this embodiment may be executed by a content exhibition weight value calculation apparatus, the content exhibition weight value calculation apparatus may be implemented as software, or implemented as a combination of software and hardware, and the content exhibition weight value calculation apparatus may be integrated in a certain device in a content exhibition weight value calculation system, such as a content exhibition weight value calculation server or a content exhibition weight value calculation terminal device. As shown in fig. 1, the method comprises the steps of:

step S101, obtaining a content to be displayed;

in the present disclosure, the content to be shown may include various content forms of text, picture, video, audio, and the like, or a combination of the above content forms, and typically, the content to be shown may be video content of a video platform, advertisement content, commodity content of an e-commerce platform, search results of a search platform, and the like.

In a typical application, the content to be displayed is displayed at a display position on a page, the obtaining of the content to be displayed is to obtain the content to be displayed at the display position, typically, the content to be displayed is a video, the display position is a recommendation position on the page, and when each recommendation condition is triggered, the video to be displayed corresponding to the recommendation position is obtained.

Step S102, obtaining the depth interaction times of the content to be displayed;

in this step, the number of deep interactions is the number of deep interactions that have been performed by the user with the content to be presented. In this embodiment, the system records each time the user performs a deep interaction with the content to be displayed, typically, a recording parameter D is set, and the value of the parameter is increased by 1 each time the user performs a deep interaction with the content to be displayed.

In this embodiment, the deep interaction is a predefined operation performed by a user and the content to be displayed, in a typical application, the user clicks a video, and performs a praise operation on the video, and then performs a forwarding operation on the video, where forwarding may be defined as deep interaction, and the number of forwarding may be defined as the number of deep interaction; in another exemplary application, a user clicks on an advertisement, downloads a program in the advertisement, and consumes the program, and the consuming action may be defined as deep interaction, and the number of consuming may be defined as the number of deep interactions.

It is to be understood that the meaning of deep interaction is definable according to the needs, and the above meaning is only exemplary and does not constitute a limitation of the present disclosure.

Step S103, judging whether the number of the deep interaction times is less than a first threshold value;

the first threshold is a preset constant value, which is generally a natural number greater than 0, and is used as a condition for selecting a calculation mode for displaying the weight value. In this step, the magnitude relationship between the number of depth interactions acquired in step S102 and the first threshold is directly compared.

Step S104, responding to the fact that the number of the deep interaction times is smaller than the first threshold value, and obtaining a first probability value and a second probability value of the content to be displayed;

the first probability value is a probability value of first interaction between a user and the content to be displayed, and the second probability value is a probability value of deep interaction between the user and the content to be displayed. The first interaction is the first interaction which is performed with the content after the user clicks the content, in a typical application scene, the first interaction is that the user approves the watched video, the user needs to click the video firstly, then perform the first interaction, namely approve operation, on the video, and then perform the deep interaction, namely forward operation, on the approved video, wherein the first probability value is the probability that the user performs approve operation on the video, and the second probability value is the probability that the user performs forward operation on the approve video; in another typical application scenario, the first interaction is that a user clicks a program corresponding to a download link in an advertisement, and then the user needs to click the advertisement first, perform a first interaction on the advertisement, that is, download the program corresponding to the link in the advertisement, and then perform a deep interaction on the downloaded program, that is, consume the program, where the first probability value is a probability that the user downloads the program corresponding to the link in the advertisement, and the second probability value is a probability that the user consumes the program to the extent of downloading.

It is to be understood that the first interaction, the deep interaction, described above is meant only as an example and not to be a limitation of the present disclosure. Indeed, in different application scenarios, to enable the calculation of the presentation weight values using different factors, the first interaction and the deep interaction may be defined to any meaning.

And step S105, calculating a display weight value according to the first probability value, the second probability value and the depth interaction times.

The display weight value is used for determining whether the display content is displayed.

In this step, the calculating a display weight value according to the first probability value, the second probability value, and the number of deep interactions includes:

calculating a starting factor according to the number of deep interaction times and a first threshold value;

and calculating a display weight value according to the starting factor, the first probability value and the second probability value.

Specifically, the display weight value is calculated using the following formula:

and Y is a display weight value, C is a current second probability value, avgC is an average value of the second probability value, D is the depth interaction times, E is a first threshold value, F is A B, wherein A is the probability of clicking the content to be displayed by the user, B is a current first probability value, avgF is an average value of F values, param is greater than 0, and is an action amplitude adjusting coefficient. In the case of the equation 1, the,

i.e. the start factor, only if

The formula 1 is meaningful if the value of (D) is greater than 0, i.e. it only works if D is smaller than E. In the formula 1, since the formula 1 only functions when the number of deep interactions is less than the first threshold, the formula 1 is used in a cold start phase when the number of deep interactions is small, and the number of deep interactions is small at this time, so that the second probability value cannot be accurately predicted by the model, and the second probability value is predicted by using a prediction model in the phase;

so that the second probability value does not exceed 1.3, if it becomes larger; in the formula 1, the values of a and B are also estimated values, the more F exceeds the average value, the larger the value of Y is, that is, the larger the value of F has an incentive effect on the value of Y, and the larger the click probability and the first probability value are, the larger the weight value is shown; param is effectiveThe amplitude adjustment coefficient has an effect of amplifying the value of Y when the ratio of 0 < param < 1 and a reduction effect when param > 1. The display weight value may be used to determine whether to display the content to be displayed, for example, when the display weight value is greater than a threshold, the content to be displayed is displayed; or selecting the content to be displayed with the largest display weight value from the plurality of contents to be displayed to display the contents to be displayed.

The above steps S104 and S105 describe a calculation method of the presentation weight value when the number of deep interactions is less than the first threshold; in this embodiment, there are also the following cases:

step S106, responding to the fact that the number of the deep interaction times is larger than or equal to the first threshold value, and acquiring a first probability value and/or a second probability value of the content to be displayed, and a first resource consumption amount and/or a second resource consumption amount;

in this step, the first probability value is a probability of a first interaction between the user and the content to be displayed, the second probability value is a probability of a deep interaction between the user and the content to be displayed, the first resource consumption amount is a resource amount consumed by the first interaction between the user and the content to be displayed, and the second resource consumption amount is a resource amount consumed by the deep interaction between the user and the content to be displayed. In practical application, a click probability value of the user can be obtained, wherein the click probability value is the probability that the user clicks the content to be displayed. The click probability value, the first probability value and the second probability value are obtained by calculating through corresponding models, and the click probability, the first probability value and the second probability at the current moment are obtained in real time each time when the content to be displayed is obtained; the first resource consumption and the second resource consumption are respectively preset values, the resource may be any resource that needs to be consumed by a user for performing a first interaction or a deep interaction with the content to be displayed, typical resources are network resources, time resources, value resources, and the like, where the network resource may be a flow that needs to be consumed by the user for performing the first interaction or the deep interaction with the content to be displayed, the time resource may be time that needs to be consumed by the user for performing the first interaction or the deep interaction with the content to be displayed, and the value resource may be a value that needs to be consumed by a content publisher when the user performs the first interaction or the deep interaction with the content to be displayed.

And S107, calculating a display weight value according to the first probability value and the first resource consumption amount or the second probability value and the second resource consumption amount.

Wherein the display weight value is used to determine whether to display the content to be displayed.

In one aspect, in this step, the calculating a show weight value according to one or more of the first probability value, the first resource consumption amount, the second probability value, and the second resource consumption amount includes: and calculating the product of the click probability value, the first probability value and the first resource consumption to obtain a display weight value of the content to be displayed. Specifically, the display weight value is calculated according to the following formula:

y is A B X formula (2)

Wherein Y represents a first weight value, A represents a click probability value, B represents a first probability value, and X represents a first resource consumption amount. In a typical application scene, if the first interaction is that the user approves the watched video, the user needs to click the video first and then approve the video, and the resources consumed by approval in the scene are flow resources in network resources; in another typical application scenario, the first interaction is that the user downloads a program in a viewed advertisement, and then the user needs to click the advertisement first and then click a download link in the advertisement, and resources consumed for downloading in the scenario are valuable resources, which may cause an advertisement fee to be spent by an advertiser. The display weight value may be used to determine whether to display the content to be displayed, for example, when the display weight value is greater than a threshold, the content to be displayed is displayed; or selecting the content to be displayed with the largest display weight value from the plurality of contents to be displayed to display the contents to be displayed.

In another aspect, the computing a show weight value as a function of one or more of the first probability value, first resource consumption amount, second probability value, and second resource consumption amount comprises: and calculating the product of the click probability value, the first probability value, the second probability value and the second resource consumption to obtain a display weight value of the content to be displayed. Specifically, the display weight value is calculated according to the following formula:

y ═ a ═ B ═ C ═ Z (formula 3)

Y is a display weight value, A is a probability value of the user clicking the content to be displayed, B is a current first probability value, C is a current second probability value, and Z is a second resource consumption amount. In a typical application scene, the first interaction is that a user approves a watched video, the user needs to click the video at first, then approve the video, the deep interaction is that the video after approval is forwarded, and the resources consumed by forwarding in the scene are flow resources in network resources; in another typical application scenario, the first interaction is that the user downloads a program in a viewed advertisement, and then the user needs to click the advertisement first and then click a download link in the advertisement, and the deep interaction consumes the program to the extent of downloading, and resources consumed by the consuming operation in the scenario are valuable resources, specifically, the advertisement cost consumed by the advertiser can be reduced, and the like. The display weight value may be used to determine whether to display the content to be displayed, for example, when the display weight value is greater than a threshold, the content to be displayed is displayed; or selecting the content to be displayed with the largest display weight value from the plurality of contents to be displayed to display the contents to be displayed.

Since the first interaction and the deep interaction may be of a wide variety, the first probability and the second probability may refer to different values in different scenarios, typical first probability values may be probabilities of like, downloading, and so on, typical second probability values may be probabilities of forwarding, paying, remaining next day, registering, and so on, and thus different models may be used for predictive computation for the different first probability values and the second probability values. For the case that the number of deep interactions is smaller than the first threshold, since the number of interactions is small, the prediction model cannot accurately predict the current first probability value and the second probability value, as shown in fig. 2, in an embodiment, the step S104 may further include:

step S201: obtaining pre-estimated models of the first probability value and the second probability value in response to the number of the deep interactions being smaller than the first threshold;

step S202: and calculating the first probability value and the second probability value according to the pre-estimation model.

Under this condition, use the pre-established model of estimating to predict first probability value and second probability value, should estimate the model and be used for the cold start stage, interactive data is less this moment, uses to estimate the model and is convenient for show away new content of treating the show to prevent that the new content that the show weighted value is less leads to can't show.

For the case that the number of deep interactions is greater than or equal to the first threshold, since the number of interactions is relatively large, the first probability value and the second probability value may be calculated by using a prediction model, and thus as shown in fig. 3, in an embodiment, the step S106 may further include:

step S301: in response to the number of deep interactions being greater than or equal to the first threshold, obtaining a first and/or second computational model of the first and/or second probability value;

step S302: calculating the first probability value and/or the second probability value according to the first computational model and/or the second computational model.

In this embodiment, different first and second probability value calculation models are trained for different application scenarios, and in use, different calculation models may be selected to calculate the second probability value or the second probability value depending on different application scenarios.

After obtaining the display weight value, the content to be displayed needs to be displayed by using the display weight, specifically, as shown in fig. 4, in the present disclosure, the method further includes:

step S401, obtaining a plurality of contents to be displayed;

step S402, obtaining a plurality of display weight values of the contents to be displayed to obtain a plurality of display weight values, wherein the display weight values correspond to the contents to be displayed one by one, and the display weight values comprise display weight values calculated according to the method in the steps S101-S105 and/or display weight values calculated according to the methods in the steps S101-S103 and S106-S107;

step S403, calculating to obtain a maximum display weight value in the plurality of display weight values;

and S404, displaying the content to be displayed corresponding to the maximum display weight value.

In this embodiment, the content to be displayed includes a plurality of contents to be displayed, and there are a plurality of contents to be displayed at the same time, but only one content can be displayed at a time, so that display weight values of the plurality of contents to be displayed are obtained to obtain a plurality of display weight values, each content to be displayed has one display weight value, and the weight values correspond to the contents to be displayed one by one; after a plurality of display weight values are obtained, the display weight values are sorted according to the size or directly compared with the size to obtain a third weight value which is the largest in the display weight values; and displaying the content to be displayed corresponding to the maximum display weight value.

In the above, although the steps in the above method embodiments are described in the above sequence, it should be clear to those skilled in the art that the steps in the embodiments of the present disclosure are not necessarily performed in the above sequence, and may also be performed in other sequences such as reverse, parallel, and cross, and further, on the basis of the above steps, other steps may also be added by those skilled in the art, and these obvious modifications or equivalents should also be included in the protection scope of the present disclosure, and are not described herein again.

Fig. 5 is a schematic structural diagram of an embodiment of a computing device for displaying weight values of contents according to an embodiment of the disclosure, as shown in fig. 5, the device 500 includes: a first to-be-displayed content obtaining module 501, a depth interaction number obtaining module 502, a judging module 503, a first display weight parameter obtaining module 504, and a first display weight value calculating module 505. Wherein the content of the first and second substances,

a first to-be-displayed content obtaining module 501, configured to obtain a to-be-displayed content;

a deep interaction frequency obtaining module 502, configured to obtain a deep interaction frequency of a content to be displayed, where the deep interaction frequency is a frequency of deep interaction that a user has performed with the content to be displayed;

a determining module 503, configured to determine whether the number of deep interactions is smaller than a first threshold;

a first display weight parameter obtaining module 504, configured to, in response to that the number of deep interactions is smaller than the first threshold, obtain a first probability value and a second probability value of the content to be displayed, where the first probability value is a probability value of a first interaction between the user and the content to be displayed, and the second probability value is a probability value of a deep interaction between the user and the content to be displayed;

a first display weight value calculating module 505, configured to calculate a display weight value according to the first probability value, the second probability value, and the number of deep interactions, where the display weight value is used to determine whether to display the content to be displayed.

Further, the computing device 500 for content exhibiting weight value further comprises:

a second display weight parameter obtaining module, configured to obtain, in response to that the number of deep interactions is greater than or equal to the first threshold, one or more of a first probability value, a second probability value, a first resource consumption amount, and a second resource consumption amount of a content to be displayed, where the first resource consumption amount is a resource amount that needs to be consumed by a user for performing a first interaction with the content to be displayed, and the second resource consumption amount is a resource amount that needs to be consumed by the user for performing deep interactions with the content to be displayed;

and the second display weight value calculating module is used for calculating a display weight value according to one or more of the first probability value, the first resource consumption amount, the second probability value and the second resource consumption amount, wherein the display weight value is used for determining whether the content to be displayed is displayed or not.

Further, the first weight value calculating module 505 is further configured to:

calculating a show weight value according to the following formula:

and Y is a display weight value, C is a current second probability value, avgC is an average value of the second probability value, D is the depth interaction times, E is a first threshold value, F is A B, wherein A is the probability of clicking the content to be displayed by the user, B is a current first probability value, avgF is an average value of F values, param is greater than 0, and is an action amplitude adjusting coefficient.

Further, the second display weight value calculating module is further configured to:

calculating a show weight value according to the following formula:

Y＝A*B*X

y is a display weight value, A is the probability of clicking the content to be displayed by the user, B is a current first probability value, and X is a first resource consumption; or the like, or, alternatively,

calculating a show weight value according to the following formula:

Y＝A*B*C*Z

y is a display weight value, A is a probability value of the user clicking the content to be displayed, B is a current first probability value, C is a current second probability value, and Z is a second resource consumption amount.

Further, the first display weight parameter obtaining module 504 further includes:

the parameter pre-estimation model acquisition module is used for responding to the fact that the depth interaction times are smaller than the first threshold value, and acquiring pre-estimation models of the first probability value and the second probability value;

and the parameter estimation module is used for calculating the first probability value and the second probability value according to the estimation model.

Further, the second display weight parameter obtaining module further includes:

a parameter calculation model obtaining module, configured to obtain a first calculation model and/or a second calculation model of the first probability value and/or the second probability value in response to the number of deep interactions being greater than or equal to the first threshold;

a parameter calculation module for calculating the first probability value and/or the second probability value according to the first calculation model and/or the second calculation model.

The apparatus shown in fig. 5 can perform the method of the embodiments shown in fig. 1, fig. 2 and fig. 3, and the detailed description of the embodiment may refer to the related description of the embodiments shown in fig. 1, fig. 2 and fig. 3. The implementation process and technical effect of the technical solution refer to the descriptions in the embodiments shown in fig. 1, fig. 2, and fig. 3, and are not described herein again.

Fig. 6 is a schematic structural diagram of an embodiment of a content display apparatus provided in an embodiment of the present disclosure, and as shown in fig. 6, the apparatus 600 includes: a second to-be-displayed content obtaining module 601, a display weight obtaining module 602, a maximum display weight value calculating module 603, and a content display module 604. Wherein the content of the first and second substances,

a second to-be-displayed content obtaining module 601, configured to obtain multiple to-be-displayed contents;

a display weight obtaining module 602, configured to obtain a plurality of display weight values of a plurality of contents to be displayed, so as to obtain a plurality of display weight values, where the plurality of display weight values correspond to the plurality of contents to be displayed one by one, and the plurality of display weight values include display weight values calculated according to the method in the above steps S101 to S105 and/or display weight values calculated according to the methods in the above steps S101 to S103, S106 to S107;

a maximum display weight value calculating module 603, configured to calculate to obtain a maximum display weight value in the plurality of display weight values;

the content displaying module 604 is configured to display the content to be displayed corresponding to the maximum display weight value.

The apparatus shown in fig. 6 can perform the method of the embodiment shown in fig. 4, and reference may be made to the related description of the embodiment shown in fig. 4 for a part of this embodiment that is not described in detail. The implementation process and technical effect of the technical solution refer to the description in the embodiment shown in fig. 4, and are not described herein again.

Referring now to fig. 7, a schematic diagram of an electronic device (e.g., the terminal device or the server of fig. 1) 700 suitable for implementing embodiments of the present disclosure is shown. The terminal device in the embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), and the like, and a stationary terminal such as a digital TV, a desktop computer, and the like. The electronic device shown in fig. 7 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. 7, the electronic device 700 may include a processing means (e.g., central processing unit, graphics processor, etc.) 701 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)702 or a program loaded from a storage means 706 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data necessary for the operation of the electronic apparatus 700 are also stored. The processing device 701, the ROM 702, and the RAM 703 are connected to each other by a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

Generally, the following devices may be connected to the I/O interface 705: input devices 706 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 707 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 706 including, for example, magnetic tape, hard disk, etc.; and a communication device 709. The communication means 709 may allow the electronic device 700 to communicate wirelessly or by wire with other devices to exchange data. While fig. 7 illustrates an electronic device 700 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.

In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 709, or may be installed from the storage means 706, or may be installed from the ROM 702. The computer program, when executed by the processing device 701, performs the above-described functions defined in the methods of the embodiments of the present disclosure.

It should be noted that the computer readable medium in the present disclosure can 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 the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (hypertext transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.

The computer readable medium may be embodied in the electronic device; 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 content to be displayed; acquiring the number of deep interaction times of the content to be displayed, wherein the number of deep interaction times is the number of deep interaction times of a user and the content to be displayed; judging whether the number of the deep interaction times is smaller than a first threshold value; responding to the number of the deep interaction times smaller than the first threshold, and acquiring a first probability value and a second probability value of the content to be displayed, wherein the first probability value is a probability value of first interaction between a user and the content to be displayed, and the second probability value is a probability value of deep interaction between the user and the content to be displayed; and calculating a display weight value according to the first probability value, the second probability value and the depth interaction times, wherein the display weight value is used for determining whether the content to be displayed is displayed or not.

Alternatively, the computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring a plurality of contents to be displayed; obtaining a plurality of display weight values of a plurality of contents to be displayed to obtain a plurality of display weight values, wherein the plurality of display weight values correspond to the plurality of contents to be displayed one by one, and the plurality of display weight values comprise display weight values calculated according to the method in the steps S101-S105 and/or display weight values calculated according to the methods in the steps S101-S103 and S106-S107; calculating to obtain a maximum display weight value of the plurality of display weight values; and displaying the content to be displayed corresponding to the maximum display weight value.

Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to 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 the embodiments of the present disclosure may be implemented by software or hardware. Where the name of an element does not in some cases constitute a limitation on the element itself.

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.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on 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.

According to one or more embodiments of the present disclosure, there is provided a method for calculating a content presentation weight value, including: acquiring content to be displayed; acquiring the number of deep interaction times of the content to be displayed, wherein the number of deep interaction times is the number of deep interaction times of a user and the content to be displayed; judging whether the number of the deep interaction times is smaller than a first threshold value; responding to the number of the deep interaction times smaller than the first threshold, and acquiring a first probability value and a second probability value of the content to be displayed, wherein the first probability value is a probability value of first interaction between a user and the content to be displayed, and the second probability value is a probability value of deep interaction between the user and the content to be displayed; and calculating a display weight value according to the first probability value, the second probability value and the depth interaction times, wherein the display weight value is used for determining whether the content to be displayed is displayed or not.

Further, the method further comprises: responding to the number of deep interactions being larger than or equal to the first threshold, acquiring one or more of a first probability value, a second probability value, a first resource consumption and a second resource consumption of the content to be displayed, wherein the first resource consumption is the resource amount required by a user to perform first interaction with the content to be displayed, and the second resource consumption is the resource amount required by the user to perform deep interactions with the content to be displayed; calculating a display weight value according to one or more of the first probability value, the first resource consumption amount, the second probability value and the second resource consumption amount, wherein the display weight value is used for determining whether to display the content to be displayed.

Further, the calculating a display weight value according to the first probability value, the second probability value and the number of deep interactions includes: calculating a show weight value according to the following formula:

Further, the calculating a display weight value according to the first probability value and the first resource consumption amount or the second probability value and the second resource consumption amount includes: calculating a show weight value according to the following formula:

y is A B X, wherein Y is a display weight value, A is the probability of clicking the content to be displayed by the user, B is a current first probability value, and X is first resource consumption; or, calculating the display weight value according to the following formula:

and Y is A, B, C and Z, wherein Y is a display weight value, A is a probability value of clicking the content to be displayed by the user, B is a current first probability value, C is a current second probability value, and Z is second resource consumption.

Further, the obtaining a first probability value and a second probability value of the content to be displayed in response to the number of deep interactions being smaller than the first threshold includes: obtaining pre-estimated models of the first probability value and the second probability value in response to the number of the deep interactions being smaller than the first threshold; and calculating the first probability value and the second probability value according to the pre-estimation model.

Further, the obtaining a first probability value and/or a second probability value of the content to be displayed in response to the number of deep interactions being greater than or equal to the first threshold value includes: in response to the number of deep interactions being greater than or equal to the first threshold, obtaining a first and/or second computational model of the first and/or second probability value; calculating the first probability value and/or the second probability value according to the first computational model and/or the second computational model.

According to one or more embodiments of the present disclosure, there is provided a content presentation method including: acquiring a plurality of contents to be displayed; obtaining a plurality of display weight values of a plurality of contents to be displayed to obtain a plurality of display weight values, wherein the display weight values correspond to the contents to be displayed one by one, and the display weight values comprise display weight values calculated according to a calculation method of any one content display weight value; calculating to obtain a maximum display weight value of the plurality of display weight values; and displaying the content to be displayed corresponding to the maximum display weight value.

According to one or more embodiments of the present disclosure, there is provided a content exhibition weight value calculation apparatus including: the first content to be displayed acquisition module is used for acquiring content to be displayed; the system comprises a deep interaction frequency acquisition module, a display module and a display module, wherein the deep interaction frequency acquisition module is used for acquiring the deep interaction frequency of the content to be displayed, and the deep interaction frequency is the frequency of the deep interaction between a user and the content to be displayed; the judging module is used for judging whether the number of the deep interaction times is smaller than a first threshold value; the first display weight parameter acquisition module is used for acquiring a first probability value and a second probability value of the content to be displayed in response to the number of the deep interaction times being smaller than the first threshold, wherein the first probability value is a probability value of first interaction between a user and the content to be displayed, and the second probability value is a probability value of deep interaction between the user and the content to be displayed; and the first display weight value calculating module is used for calculating a display weight value according to the first probability value, the second probability value and the depth interaction times, wherein the display weight value is used for determining whether the content to be displayed is displayed or not.

Further, the computing device for content display weight value further comprises: a second display weight parameter obtaining module, configured to obtain, in response to that the number of deep interactions is greater than or equal to the first threshold, one or more of a first probability value, a second probability value, a first resource consumption amount, and a second resource consumption amount of a content to be displayed, where the first resource consumption amount is a resource amount that needs to be consumed by a user for performing a first interaction with the content to be displayed, and the second resource consumption amount is a resource amount that needs to be consumed by the user for performing deep interactions with the content to be displayed; and the second display weight value calculating module is used for calculating a display weight value according to one or more of the first probability value, the first resource consumption amount, the second probability value and the second resource consumption amount, wherein the display weight value is used for determining whether the content to be displayed is displayed or not.

Further, the first display weight value calculating module is further configured to: calculating a show weight value according to the following formula:

Further, the second display weight value calculating module is further configured to: calculating a show weight value according to the following formula: y is A B X, wherein Y is a display weight value, A is the probability of clicking the content to be displayed by the user, B is a current first probability value, and X is first resource consumption; or, calculating the display weight value according to the following formula: and Y is A, B, C and Z, wherein Y is a display weight value, A is a probability value of clicking the content to be displayed by the user, B is a current first probability value, C is a current second probability value, and Z is second resource consumption.

Further, the first display weight parameter obtaining module further includes: the parameter pre-estimation model acquisition module is used for responding to the fact that the depth interaction times are smaller than the first threshold value, and acquiring pre-estimation models of the first probability value and the second probability value; and the parameter estimation module is used for calculating the first probability value and the second probability value according to the estimation model.

Further, the second display weight parameter obtaining module further includes: a parameter calculation model obtaining module, configured to obtain a first calculation model and/or a second calculation model of the first probability value and/or the second probability value in response to the number of deep interactions being greater than or equal to the first threshold; a parameter calculation module for calculating the first probability value and/or the second probability value according to the first calculation model and/or the second calculation model.

According to one or more embodiments of the present disclosure, there is provided a content presentation apparatus including: the second content to be displayed acquisition module is used for acquiring a plurality of contents to be displayed; the display weight acquiring module is used for acquiring display weight values of a plurality of contents to be displayed so as to obtain a plurality of display weight values, wherein the display weight values correspond to the contents to be displayed one by one, and the display weight values comprise display weight values calculated according to a calculation method of any one content display weight value; a maximum display weight value calculation module for calculating to obtain a maximum display weight value of the plurality of display weight values; and the content display module is used for displaying the content to be displayed corresponding to the maximum display weight value.

According to one or more embodiments of the present disclosure, there is provided an electronic device including: a memory for storing computer readable instructions; and a processor for executing the computer readable instructions, such that the processor when executing implements the method of any one of the above.

According to one or more embodiments of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer-readable instructions which, when executed by a computer, cause the computer to perform the method of any one of the above.

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 disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.

Claims

1. A method for calculating a content presentation weight value includes:

acquiring content to be displayed;

acquiring the number of deep interaction times of the content to be displayed, wherein the number of deep interaction times is the number of deep interaction times of a user and the content to be displayed;

judging whether the number of the deep interaction times is smaller than a first threshold value;

responding to the number of the deep interaction times smaller than the first threshold, and acquiring a first probability value and a second probability value of the content to be displayed, wherein the first probability value is a probability value of first interaction between a user and the content to be displayed, and the second probability value is a probability value of deep interaction between the user and the content to be displayed;

and calculating a display weight value according to the first probability value, the second probability value and the depth interaction times, wherein the display weight value is used for determining whether the content to be displayed is displayed or not.

2. The method of calculating a content presentation weight value of claim 1, the method further comprising:

responding to the number of deep interactions being larger than or equal to the first threshold, acquiring one or more of a first probability value, a second probability value, a first resource consumption and a second resource consumption of the content to be displayed, wherein the first resource consumption is the resource amount required by a user to perform first interaction with the content to be displayed, and the second resource consumption is the resource amount required by the user to perform deep interactions with the content to be displayed;

calculating a display weight value according to one or more of the first probability value, the first resource consumption amount, the second probability value and the second resource consumption amount, wherein the display weight value is used for determining whether to display the content to be displayed.

3. The content presentation method of claim 1, wherein the calculating presentation weight values from the first probability value, second probability value, and number of deep interactions comprises:

calculating a show weight value according to the following formula:

4. The content presentation method of claim 2, wherein the calculating a presentation weight value according to the first probability value and the first resource consumption amount or the second probability value and the second resource consumption amount comprises:

calculating a show weight value according to the following formula:

Y＝A*B*X

calculating a show weight value according to the following formula:

Y＝A*B*C*Z

5. The method for calculating a weight value for content presentation according to claim 1, wherein the obtaining a first probability value and a second probability value of the content to be presented in response to the number of deep interactions being less than the first threshold comprises:

obtaining pre-estimated models of the first probability value and the second probability value in response to the number of the deep interactions being smaller than the first threshold;

and calculating the first probability value and the second probability value according to the pre-estimation model.

6. The method for calculating a weight value for content presentation according to claim 2, wherein the obtaining a first probability value and/or a second probability value of the content to be presented in response to the number of deep interactions being greater than or equal to the first threshold value comprises:

in response to the number of deep interactions being greater than or equal to the first threshold, obtaining a first and/or second computational model of the first and/or second probability value;

calculating the first probability value and/or the second probability value according to the first computational model and/or the second computational model.

7. A method of content presentation, comprising:

acquiring a plurality of contents to be displayed;

obtaining a plurality of display weight values of a plurality of contents to be displayed to obtain a plurality of display weight values, wherein the plurality of display weight values correspond to the plurality of contents to be displayed one by one, and the plurality of display weight values comprise the display weight values calculated according to the method in claim 1 and/or the display weight values calculated according to the method in claim 2;

calculating to obtain a maximum display weight value of the plurality of display weight values;

and displaying the content to be displayed corresponding to the maximum display weight value.

8. A computing device for content presentation weight values, comprising:

the first content to be displayed acquisition module is used for acquiring content to be displayed;

the system comprises a deep interaction frequency acquisition module, a display module and a display module, wherein the deep interaction frequency acquisition module is used for acquiring the deep interaction frequency of the content to be displayed, and the deep interaction frequency is the frequency of the deep interaction between a user and the content to be displayed;

the judging module is used for judging whether the number of the deep interaction times is smaller than a first threshold value;

the first display weight parameter acquisition module is used for acquiring a first probability value and a second probability value of the content to be displayed in response to the number of the deep interaction times being smaller than the first threshold, wherein the first probability value is a probability value of first interaction between a user and the content to be displayed, and the second probability value is a probability value of deep interaction between the user and the content to be displayed;

and the first display weight value calculating module is used for calculating a display weight value according to the first probability value, the second probability value and the depth interaction times, wherein the display weight value is used for determining whether the content to be displayed is displayed or not.

9. A content presentation device, comprising:

the second content to be displayed acquisition module is used for acquiring a plurality of contents to be displayed;

a display weight obtaining module, configured to obtain a plurality of display weight values of a plurality of contents to be displayed, so as to obtain the plurality of display weight values, where the plurality of display weight values correspond to the plurality of contents to be displayed one by one, and the plurality of display weight values include a display weight value calculated according to the method of claim 1 and/or a display weight value calculated according to the method of claim 2;

a maximum display weight value calculation module for calculating to obtain a maximum display weight value of the plurality of display weight values;

and the content display module is used for displaying the content to be displayed corresponding to the maximum display weight value.

10. An electronic device, comprising:

a memory for storing computer readable instructions; and

a processor for executing the computer readable instructions such that the processor when executed implements the method of any of claims 1-7.

11. A non-transitory computer readable storage medium storing computer readable instructions which, when executed by a computer, cause the computer to perform the method of any one of claims 1-7.