Method, device, electronic device and computer-readable storage medium for calculating content display weight value

Abstract

The present disclosure discloses a method, device, electronic device and computer-readable storage medium for calculating the content display weight value. The method for calculating the content display weight value includes: obtaining the content to be displayed; obtaining the number of deep interactions of the content to be displayed, wherein the number of deep interactions is the number of deep interactions that the user has performed with the content to be displayed; judging whether the number of deep interactions is less than a first threshold; in response to the number of deep interactions being less than the first threshold, obtaining the first probability value and the second probability value of the content to be displayed; and calculating the display weight value according to the first probability value, the second probability value and the number of deep interactions. By judging the number of deep interactions and selecting the method for calculating the display weight in the above method, the technical problem of how to select appropriate content from multiple contents to display to users in the prior art is solved.

Description

Technical Field

[0001] The present disclosure relates to the field of information processing, and in particular to a method, device, electronic device and computer-readable storage medium for calculating a content display weight value. Background Art

[0002] With the development of computer networks, the Internet has become a platform for displaying and publishing more and more content, such as e-commerce, video platforms, etc. In the prior art, fixed content is generally published using a fixed publishing location, such as placing multiple video contents on the homepage of a video platform for users to choose from. However, with such a large amount of content on the Internet, how to select appropriate content from the numerous contents to display to users has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] This summary is provided to introduce concepts in a brief form that will be described in detail in the detailed description below. This summary is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0004] In a first aspect, an embodiment of the present disclosure provides a method for calculating a content display weight value, including:

[0005] Acquire content to be displayed; acquire the number of deep interactions of the content to be displayed, wherein the number of deep interactions is the number of deep interactions that the user has performed with the content to be displayed; determine whether the number of deep interactions is less than a first threshold; in response to the number of deep interactions being less than the first threshold, acquire 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 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; calculate a display weight value according to the first probability value, the second probability value and the number of deep interactions, wherein the display weight value is used to determine whether to display the content to be displayed.

[0006] In a second aspect, an embodiment of the present disclosure provides a content display method, including: obtaining multiple contents to be displayed; obtaining display weight values ​​of the multiple contents to be displayed to obtain multiple display weight values, wherein the multiple display weight values ​​correspond one-to-one to the multiple contents to be displayed, wherein the multiple display weight values ​​include the display weight values ​​calculated according to the method described in claim 1 and / or the display weight values ​​calculated according to the method described in claim 2; calculating to obtain the maximum display weight value among the multiple display weight values; and displaying the content to be displayed corresponding to the maximum display weight value.

[0007] In a third aspect, an embodiment of the present disclosure provides a device for calculating a content display weight value, including:

[0008] A first module for acquiring content to be displayed, used for acquiring content to be displayed; a module for acquiring the number of deep interactions of the content to be displayed, wherein the number of deep interactions is the number of deep interactions that the user has performed with the content to be displayed; a judgment module, used for judging whether the number of deep interactions is less than a first threshold; a first display weight parameter acquisition module, used for acquiring a first probability value and a second probability value of the content to be displayed in response to the number of deep interactions being less than the first threshold, wherein 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 calculation module, used for calculating a display weight value according to the first probability value, the second probability value and the number of deep interactions, wherein the display weight value is used to determine whether to display the content to be displayed.

[0009] In a fourth aspect, an embodiment of the present disclosure provides a content display device, comprising: a second module for acquiring content to be displayed, used to acquire multiple content to be displayed; a display weight acquisition module, used to acquire display weight values ​​of multiple content to be displayed to obtain multiple display weight values, wherein the multiple display weight values ​​correspond one-to-one to the multiple content to be displayed, wherein the multiple display weight values ​​include the display weight values ​​calculated according to the method described in claim 1 and / or the display weight values ​​calculated according to the method described in claim 2; a maximum display weight value calculation module, used to calculate to obtain the maximum display weight value among the multiple display weight values; and a content display module, used to display the content to be displayed corresponding to the maximum display weight value.

[0010] In a fifth aspect, an embodiment of the present disclosure provides an electronic device, including: at least one processor; and

[0011] A memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform any of the methods in the aforementioned aspects.

[0012] In a sixth aspect, an embodiment of the present disclosure provides a non-transitory computer-readable storage medium, characterized in that the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute any of the methods in the aforementioned aspects.

[0013] The present disclosure discloses a method, device, electronic device and computer-readable storage medium for calculating the content display weight value. The method for calculating the content display weight value includes: obtaining the content to be displayed; obtaining the number of deep interactions of the content to be displayed, wherein the number of deep interactions is the number of deep interactions that the user has performed with the content to be displayed; judging whether the number of deep interactions is less than a first threshold; in response to the number of deep interactions being less than the first threshold, obtaining the first probability value and the second probability value of the content to be displayed; and calculating the display weight value according to the first probability value, the second probability value and the number of deep interactions. By judging the number of deep interactions and selecting the method for calculating the display weight in the above method, the technical problem of how to select appropriate content from multiple contents to display to users in the prior art is solved.

[0014] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the following preferred embodiments are specifically cited and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale.

[0016] Figure 1 A flowchart of an embodiment of a method for calculating a content display weight value provided by the present disclosure;

[0017] Figure 2 A specific example flow chart of step S104 in the content display weight value calculation method embodiment provided by the present disclosure;

[0018] Figure 3 A specific example flow chart of step S106 of the content display weight value calculation method embodiment provided by the present disclosure;

[0019] Figure 4 A flowchart of an embodiment of a content display method provided by the present disclosure;

[0020] Figure 5 A schematic diagram of the structure of an embodiment of a device for calculating a content display weight value provided by an embodiment of the present disclosure;

[0021] Figure 6 A schematic diagram of the structure of an embodiment of a content display device provided by an embodiment of the present disclosure;

[0022] Figure 7It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0023] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0024] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0025] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "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". The relevant definitions of other terms will be given in the following description.

[0026] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0027] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0028] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0029] Figure 1 This is a flowchart of an embodiment of a method for calculating a content display weight value provided by an embodiment of the present disclosure. The method for calculating a content display weight value provided by this embodiment can be executed by a content display weight value calculation device, which can be implemented as software, or as a combination of software and hardware. The content display weight value calculation device can be integrated in a device in a content display weight value calculation system, such as a content display weight value calculation server or a content display weight value calculation terminal device. Figure 1 As shown, the method comprises the following steps:

[0030] Step S101, obtaining content to be displayed;

[0031] In the present disclosure, the content to be displayed may include various content forms such as text, pictures, videos, audios, etc., or a combination of the above content forms. Typical content to be displayed may be video content and advertising content on a video platform, product content on an e-commerce platform, search results on a search platform, etc.

[0032] In a typical application, the content to be displayed is displayed at a display position on a page, and the obtaining of the content to be displayed is obtaining the content to be displayed at the display position. Typically, the content to be displayed is a video, and the display position is a recommendation position on the page. When each recommendation condition is triggered, the video to be displayed corresponding to the recommendation position is obtained.

[0033] Step S102, obtaining the number of deep interactions of the content to be displayed;

[0034] In this step, the number of deep interactions is the number of deep interactions that the user has performed with the content to be displayed. In this embodiment, each time the user performs a deep interaction with the content to be displayed, the system will record it. 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.

[0035] In this embodiment, the deep interaction is a predefined predetermined operation between a user and the content to be displayed. In a typical application, a user clicks on a video, likes the video, and then forwards the video after liking it. The forwarding here can be defined as a deep interaction, and the number of forwardings can be defined as the number of deep interactions. In another typical application, a user clicks on an advertisement, downloads the program in the advertisement, and consumes the program. This consumption action can be defined as a deep interaction, and the number of consumptions can be defined as the number of deep interactions.

[0036] It is understandable that the meaning of deep interaction can be defined as needed, and the above meaning is only an example and does not constitute a limitation to the present disclosure.

[0037] Step S103, determining whether the number of deep interactions is less than a first threshold;

[0038] The first threshold is a preset constant value, which should generally be a natural number greater than 0, and is used as a condition for selecting a calculation method for displaying the weight value. In this step, the number of deep interactions obtained from step S102 is directly compared with the first threshold.

[0039] Step S104, in response to the number of deep interactions being less than the first threshold, obtaining a first probability value and a second probability value of the content to be displayed;

[0040] Among them, the first probability value is the probability value of the user performing the first interaction with the content to be displayed, and the second probability value is the probability value of the user performing a deep interaction with the content to be displayed. The first interaction is the first interaction that the user performs with the content after clicking on it. In a typical application scenario, the first interaction is that the user likes the video being watched, so the user needs to first click on the video, then perform the first interaction on the video, that is, like the operation, and then perform a deep interaction on the liked video, that is, forwarding the operation, in which the first probability value is the probability of the user liking the video, and the second probability value is the probability of the user forwarding the liked video; in another typical application scenario, the first interaction is that the user clicks on the program corresponding to the download link in the advertisement, so the user needs to first click on the advertisement, then perform the 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 it in the program, in which the first probability value is the probability of the user downloading the program corresponding to the link in the advertisement, and the second probability value is the probability of the user consuming it in the downloading process.

[0041] It is understandable that the meanings of the first interaction and deep interaction are merely examples and do not constitute a limitation of the present disclosure. In fact, in different application scenarios, in order to use different factors to calculate the display weight value, the first interaction and deep interaction can be defined as any meaning.

[0042] Step S105: Calculate the display weight value according to the first probability value, the second probability value and the number of deep interactions.

[0043] The display weight value is used to determine whether to display the display content.

[0044] In this step, calculating the display weight value according to the first probability value, the second probability value and the number of deep interactions includes:

[0045] Calculate the activation factor according to the number of deep interactions and the first threshold;

[0046] The display weight value is calculated according to the activation factor, the first probability value and the second probability value.

[0047] Specifically, the display weight value is calculated using the following formula:

[0048]

[0049] Where Y is the display weight value, C is the current second probability value, avgC is the average value of the second probability value, D is the number of deep interactions, E is the first threshold, F = A*B, where A is the probability of the user clicking on the content to be displayed, B is the current first probability value, avgF is the average value of F, param>0, is the effect amplitude adjustment coefficient. In formula 1, That is, the initiating factor. Formula 1 is meaningful only when the value of is greater than 0, that is, only when D is less than E, the formula works. In Formula 1, since Formula 1 works only when the number of deep interactions is less than the first threshold, Formula 1 is used when the number of deep interactions is relatively small, that is, in the cold start stage. At this time, the number of deep interactions is small, so the second probability value cannot be accurately predicted by the model. In this stage, the estimation model is used to estimate the second probability value; So that when the second probability value keeps increasing, the value does not exceed 1.3; F = A*B, in formula 1, the values ​​of A and B are also estimated values, the more the F value exceeds the average value, the larger the value of Y is, that is to say, the value of F has an incentive effect on the value of Y, the larger the click probability and the first probability value, the larger the display weight value; param is the action amplitude adjustment coefficient, when 0 < param < 1, it has an amplifying effect on the value of Y, and when param > 1, it has a reducing effect on the value of Y. The display weight value can be used to determine whether to display the content to be displayed, such as when the display weight value is greater than a threshold, display the content to be displayed; or select the content to be displayed with the largest display weight value among multiple contents to be displayed.

[0050] The above steps S104 and S105 describe a method for calculating the display weight value when the number of deep interactions is less than the first threshold; in this embodiment, there are also the following situations:

[0051] Step S106, in response to the number of deep interactions being greater than or equal to the first threshold, obtaining a first probability value and / or a second probability value of content to be displayed, and a first resource consumption and / or a second resource consumption;

[0052] In this step, the first probability value is the probability that the user performs a first interaction with the content to be displayed, the second probability value is the probability that the user performs a deep interaction with the content to be displayed, the first resource consumption is the amount of resources consumed by the user performing a first interaction with the content to be displayed, and the second resource consumption is the amount of resources consumed by the user performing a deep interaction with the content to be displayed. In actual applications, the user's click probability value can also be obtained, and the click probability value is the probability that the user clicks on the content to be displayed. Among them, the click probability value, the first probability value and the second probability value are calculated using a corresponding model, and each time the content to be displayed is obtained, the click probability, the first probability and the second probability at the current moment are obtained in real time; the first resource consumption and the second resource consumption are respectively preset values, and the resources can be any resources required for the user to have a first interaction or a deep interaction with the content to be displayed, typical resources are network resources, time resources, value resources, etc., wherein network resources can be the traffic required for the user to have a first interaction or a deep interaction with the content to be displayed, time resources can be the time required for the user to have a first interaction or a deep interaction with the content to be displayed, and value resources can be the value required by the content publisher when the user has a first interaction or a deep interaction with the content to be displayed.

[0053] Step S107: calculating a display weight value according to the first probability value and the first resource consumption or the second probability value and the second resource consumption.

[0054] The display weight value is used to determine whether to display the content to be displayed.

[0055] On the one hand, in this step, the calculation of the display weight value according to one or more of the first probability value, the first resource consumption, the second probability value and the second resource consumption includes: calculating the product of the click probability value, the first probability value and the first resource consumption to obtain the display weight value of the content to be displayed. Specifically, the display weight value is calculated according to the following formula:

[0056] Y=A*B*X Formula (2)

[0057] Wherein Y represents the first weight value, A represents the click probability value, B represents the first probability value, and X represents the first resource consumption. In a typical application scenario, the first interaction is that the user likes the video being watched, so the user needs to first click on the video, and then like the video. In this scenario, the resources consumed by liking are traffic resources in network resources; in another typical application scenario, the first interaction is that the user downloads the program in the advertisement being watched, so the user needs to first click on the advertisement, and then click on the download link in the advertisement. In this scenario, the resources consumed by downloading are valuable resources, which can specifically be the advertising fees spent by the advertiser, etc. The display weight value can be used to determine whether to display the content to be displayed, such as when the display weight value is greater than a threshold, displaying the content to be displayed; or selecting the content to be displayed with the largest display weight value among multiple content to be displayed.

[0058] On the other hand, the calculating of the display weight value according to one or more of the first probability value, the first resource consumption, the second probability value and the second resource consumption includes: calculating the product of the click probability value, the first probability value, the second probability value and the second resource consumption to obtain the display weight value of the content to be displayed. Specifically, the display weight value is calculated according to the following formula:

[0059] Y=A*B*C*Z (Formula 3)

[0060] Where Y is the display weight value, A is the probability value of the user clicking on the content to be displayed, B is the current first probability value, C is the current second probability value, and Z is the second resource consumption. In a typical application scenario, the first interaction is that the user likes the video being watched, so the user needs to first click on the video and then like the video. The deep interaction is to forward the video after liking it. In this scenario, the resources consumed by forwarding are traffic resources in network resources; in another typical application scenario, the first interaction is that the user downloads the program in the advertisement being watched, so the user needs to first click on the advertisement and then click on the download link in the advertisement. The deep interaction is to consume the program in the downloading process. In this scenario, the resources consumed by the consumption operation are value resources, which can specifically be the advertising fee spent by the advertiser. The display weight value can be used to determine whether to display the content to be displayed, such as when the display weight value is greater than a threshold, display the content to be displayed; or select the content to be displayed with the largest display weight value among multiple contents to be displayed.

[0061] Since the first interaction and deep interaction can be of many types, the first probability and the second probability can refer to different values ​​in different scenarios. Typical first probability values ​​can be the probability of liking, the probability of downloading, etc., and typical second probability values ​​can be the probability of forwarding, the probability of paying, the probability of next-day retention, the probability of registration, etc. Therefore, different models can be used to predict and calculate different first probability values ​​and second probability values. For the case where the number of deep interactions is less than the first threshold, due to the small number of interactions, the prediction model cannot accurately predict the current first probability value and second probability value. Therefore, Figure 2 As shown, in one embodiment, the step S104 may further include:

[0062] Step S201: in response to the number of deep interactions being less than the first threshold, obtaining an estimation model of the first probability value and the second probability value;

[0063] Step S202: Calculate the first probability value and the second probability value according to the estimation model.

[0064] In this case, a preset estimation model is used to estimate the first probability value and the second probability value. The estimation model is used in the cold start phase, when there is less interaction data. The estimation model is used to facilitate the display of new content to be displayed to prevent the new content from being unable to be displayed due to a small display weight value.

[0065] When the number of deep interactions is greater than or equal to the first threshold, since the number of interactions is relatively large, the prediction model can be used to calculate the first probability value and the second probability value. Figure 3 As shown, in one embodiment, the step S106 may further include:

[0066] Step S301: in response to the number of deep interactions being greater than or equal to the first threshold, obtaining a first calculation model and / or a second calculation model for the first probability value and / or the second probability value;

[0067] Step S302: Calculate the first probability value and / or the second probability value according to the first calculation model and / or the second calculation model.

[0068] In this embodiment, different first probability value and second probability value calculation models are trained for different application scenarios. When used, different calculation models can be selected to calculate the first probability value or the second probability value according to different application scenarios.

[0069] After obtaining the display weight value, it is necessary to use the display weight to display the content to be displayed, such as Figure 4 As shown, in the present disclosure, it also includes:

[0070] Step S401, obtaining a plurality of contents to be displayed;

[0071] Step S402, acquiring 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 one-to-one to the plurality of contents to be displayed, wherein the plurality of display weight values ​​include the display weight values ​​calculated according to the method in the above steps S101-S105 and / or the display weight values ​​calculated according to the method in the above steps S101-S103, S106-S107;

[0072] Step S403, calculating to obtain the maximum display weight value among the multiple display weight values;

[0073] Step S404: display the to-be-displayed content corresponding to the maximum display weight value.

[0074] In this embodiment, the content to be displayed includes multiple contents, and there are multiple contents to be displayed at the same time, but only one can be displayed at a time. Therefore, the display weight values ​​of the multiple contents to be displayed are obtained to obtain multiple display weight values, each content to be displayed has a display weight value, and the weight value corresponds to the content to be displayed one by one; after obtaining the multiple display weight values, the multiple display weight values ​​are sorted according to size or directly compared in size to obtain the largest third weight value among the multiple display weight values; the content to be displayed corresponding to the largest display weight value is displayed.

[0075] The present disclosure discloses a method, device, electronic device and computer-readable storage medium for calculating the content display weight value. The method for calculating the content display weight value includes: obtaining the content to be displayed; obtaining the number of deep interactions of the content to be displayed, wherein the number of deep interactions is the number of deep interactions that the user has performed with the content to be displayed; judging whether the number of deep interactions is less than a first threshold; in response to the number of deep interactions being less than the first threshold, obtaining the first probability value and the second probability value of the content to be displayed; and calculating the display weight value according to the first probability value, the second probability value and the number of deep interactions. By judging the number of deep interactions and selecting the method for calculating the display weight in the above method, the technical problem of how to select appropriate content from multiple contents to display to users in the prior art is solved.

[0076] In the above, although the various steps in the above method embodiment are described in the above order, those skilled in the art should be aware that the steps in the embodiments of the present disclosure are not necessarily executed in the above order, and they may also be executed in other orders such as reverse order, parallel order, and cross order. Moreover, on the basis of the above steps, those skilled in the art may also add other steps. These obvious variations or equivalent replacement methods should also be included in the protection scope of the present disclosure and will not be repeated here.

[0077] Figure 5 A schematic diagram of a structure of an embodiment of a device for calculating a content display weight value provided by an embodiment of the present disclosure, such as Figure 5 As shown, the device 500 includes: a first to-be-displayed content acquisition module 501, a deep interaction times acquisition module 502, a judgment module 503, a first display weight parameter acquisition module 504 and a first display weight value calculation module 505.

[0078] A first to-be-displayed content acquisition module 501, used to acquire to-be-displayed content;

[0079] A deep interaction times acquisition module 502 is used to acquire the deep interaction times of the content to be displayed, wherein the deep interaction times are the times that the user has deeply interacted with the content to be displayed;

[0080] A judging module 503 is used to judge whether the number of deep interactions is less than a first threshold;

[0081] A first display weight parameter acquisition module 504 is used to acquire a first probability value and a second probability value of the content to be displayed in response to the number of deep interactions being less than the first threshold, wherein the first probability value is a probability value of the user performing a first interaction with the content to be displayed, and the second probability value is a probability value of the user performing a deep interaction with the content to be displayed;

[0082] The first display weight value calculation module 505 is used to calculate the display weight value according to the first probability value, the second probability value and the number of deep interactions, wherein the display weight value is used to determine whether to display the content to be displayed.

[0083] Furthermore, the content display weight value calculation device 500 further includes:

[0084] A second display weight parameter acquisition module is used to acquire one or more of a first probability value, a second probability value, a first resource consumption amount, and a second resource consumption amount of the content to be displayed in response to the number of deep interactions being greater than or equal to the first threshold, wherein the first resource consumption amount is the amount of resources consumed by the user for the first interaction with the content to be displayed, and the second resource consumption amount is the amount of resources consumed by the user for the deep interaction with the content to be displayed;

[0085] The second display weight value calculation module is used to calculate the display weight value according to one or more of the first probability value, the first resource consumption, the second probability value and the second resource consumption, wherein the display weight value is used to determine whether to display the content to be displayed.

[0086] Furthermore, the first display weight value calculation module 505 is further used for:

[0087] The display weight value is calculated according to the following formula:

[0088]

[0089] Where Y is the display weight value, C is the current second probability value, avgC is the average value of the second probability value, D is the number of deep interactions, E is the first threshold, F=A*B, where A is the probability of the user clicking on the content to be displayed, B is the current first probability value, avgF is the average value of F value, param>0, which is the action amplitude adjustment coefficient.

[0090] Furthermore, the second display weight value calculation module is also used for:

[0091] The display weight value is calculated according to the following formula:

[0092] Y=A*B*X

[0093] Where Y is the display weight value, A is the probability of the user clicking on the content to be displayed, B is the current first probability value, and X is the first resource consumption; or,

[0094] The display weight value is calculated according to the following formula:

[0095] Y=A*B*C*Z

[0096] Wherein Y is the display weight value, A is the probability value of the user clicking on the content to be displayed, B is the current first probability value, C is the current second probability value, and Z is the second resource consumption.

[0097] Furthermore, the first display weight parameter acquisition module 504 further includes:

[0098] a parameter estimation model acquisition module, configured to acquire an estimation model of the first probability value and the second probability value in response to the number of deep interactions being less than the first threshold;

[0099] A parameter estimation module is used to calculate the first probability value and the second probability value according to the estimation model.

[0100] Furthermore, the second display weight parameter acquisition module further includes:

[0101] a parameter calculation model acquisition module, configured to acquire a first calculation model and / or a second calculation model for 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;

[0102] A parameter calculation module is used to calculate the first probability value and / or the second probability value according to the first calculation model and / or the second calculation model.

[0103] Figure 5 The device shown can perform Figure 1 , Figure 2 and Figure 3 For the method of the embodiment shown in the figure, the part not described in detail in this embodiment can be referred to Figure 1 , Figure 2 and Figure 3 The implementation process and technical effects of this technical solution refer to Figure 1 , Figure 2 and Figure 3 The description in the illustrated embodiment will not be repeated here.

[0104] Figure 6 A schematic diagram of the structure of an embodiment of a content display device provided in an embodiment of the present disclosure, such as Figure 6 As shown, the device 600 includes: a second to-be-displayed content acquisition module 601, a display weight acquisition module 602, a maximum display weight value calculation module 603 and a content display module 604.

[0105] The second to-be-displayed content acquisition module 601 is used to acquire a plurality of to-be-displayed contents;

[0106] The display weight acquisition module 602 is used to acquire 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 one-to-one to the plurality of contents to be displayed, wherein the plurality of display weight values ​​include the display weight values ​​calculated according to the method in the above steps S101-S105 and / or the display weight values ​​calculated according to the method in the above steps S101-S103, S106-S107;

[0107] A maximum display weight value calculation module 603 is used to calculate and obtain the maximum display weight value among the multiple display weight values;

[0108] The content display module 604 is used to display the to-be-displayed content corresponding to the maximum display weight value.

[0109] Figure 6 The device shown can perform Figure 4 For the method of the embodiment shown in the figure, the part not described in detail in this embodiment can be referred to Figure 4 The implementation process and technical effects of this technical solution refer to Figure 4 The description in the illustrated embodiment will not be repeated here.

[0110] Reference below Figure 7 , which shows an electronic device (eg, Figure 1The terminal device in the embodiment of the present disclosure may include but is not limited to mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 7 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0111] like Figure 7 As shown, the electronic device 700 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage device 706 into a random access memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the electronic device 700 are also stored. The processing device 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0112] Typically, the following devices may be connected to the I / O interface 705: an input device 706 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 707 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 706 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 709. The communication device 709 may allow the electronic device 700 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 7 The electronic device 700 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0113] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device 709, or installed from a storage device 706, or installed from a ROM 702. When the computer program is executed by the processing device 701, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0114] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0115] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0116] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0117] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains the content to be displayed; obtains the number of deep interactions of the content to be displayed, wherein the number of deep interactions is the number of deep interactions that the user has performed with the content to be displayed; determines whether the number of deep interactions is less than a first threshold; in response to the number of deep interactions being less than the first threshold, obtains 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 the user performing a first interaction with the content to be displayed, and the second probability value is a probability value of the user performing a deep interaction with the content to be displayed; calculates a display weight value based on the first probability value, the second probability value and the number of deep interactions, wherein the display weight value is used to determine whether to display the content to be displayed.

[0118] Alternatively, the computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains multiple contents to be displayed; obtains display weight values ​​of the multiple contents to be displayed to obtain multiple display weight values, wherein the multiple display weight values ​​correspond one-to-one to the multiple contents to be displayed, wherein the multiple display weight values ​​include the display weight values ​​calculated according to the method in the above steps S101-S105 and / or the display weight values ​​calculated according to the methods in the above steps S101-S103, S106-S107; calculates to obtain the maximum display weight value among the multiple display weight values; and displays the content to be displayed corresponding to the maximum display weight value.

[0119] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving 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 may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0120] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0121] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0122] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0123] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A 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, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0124] According to one or more embodiments of the present disclosure, a method for calculating a content display weight value is provided, comprising: obtaining content to be displayed; obtaining the number of deep interactions of the content to be displayed, wherein the number of deep interactions is the number of deep interactions that a user has performed with the content to be displayed; determining whether the number of deep interactions is less than a first threshold; in response to the number of deep interactions being less than the first threshold, obtaining 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 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; and calculating a display weight value according to the first probability value, the second probability value and the number of deep interactions, wherein the display weight value is used to determine whether to display the content to be displayed.

[0125] Furthermore, the method also includes: in response to the number of deep interactions being greater than or equal to the first threshold, obtaining 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 amount of resources required for the user to perform a first interaction with the content to be displayed, and the second resource consumption is the amount of resources required for the user to perform a deep interaction with the content to be displayed; calculating a display weight value based on one or more of the first probability value, the first resource consumption, the second probability value, and the second resource consumption, wherein the display weight value is used to determine whether to display the content to be displayed.

[0126] Further, calculating the display weight value according to the first probability value, the second probability value and the number of deep interactions includes: calculating the display weight value according to the following formula:

[0127] Where Y is the display weight value, C is the current second probability value, avgC is the average value of the second probability value, D is the number of deep interactions, E is the first threshold, F=A*B, where A is the probability of the user clicking on the content to be displayed, B is the current first probability value, avgF is the average value of F value, param>0, which is the action amplitude adjustment coefficient.

[0128] Further, calculating the display weight value according to the first probability value and the first resource consumption or the second probability value and the second resource consumption includes: calculating the display weight value according to the following formula:

[0129] Y=A*B*X, where Y is the display weight value, A is the probability of a user clicking on the content to be displayed, B is the current first probability value, and X is the first resource consumption; or, the display weight value is calculated according to the following formula:

[0130] Y=A*B*C*Z, where Y is the display weight value, A is the probability value of the user clicking the content to be displayed, B is the current first probability value, C is the current second probability value, and Z is the second resource consumption.

[0131] Furthermore, in response to the number of deep interactions being less than the first threshold, obtaining a first probability value and a second probability value of the content to be displayed includes: in response to the number of deep interactions being less than the first threshold, obtaining an estimation model of the first probability value and the second probability value; and calculating the first probability value and the second probability value according to the estimation model.

[0132] Furthermore, in response to the number of deep interactions being greater than or equal to the first threshold, obtaining a first probability value and / or a second probability value of the content to be displayed includes: in response to the number of deep interactions being greater than or equal to the first threshold, obtaining a first calculation model and / or a second calculation model for the first probability value and / or the second probability value; calculating the first probability value and / or the second probability value according to the first calculation model and / or the second calculation model.

[0133] According to one or more embodiments of the present disclosure, a content display method is provided, comprising: obtaining a plurality of contents to be displayed; obtaining display weight values ​​of the plurality of contents to be displayed to obtain a plurality of display weight values, wherein the plurality of display weight values ​​correspond one-to-one to the plurality of contents to be displayed, wherein the plurality of display weight values ​​include display weight values ​​calculated according to any of the above-mentioned methods for calculating content display weight values; calculating to obtain a maximum display weight value among the plurality of display weight values; and displaying the content to be displayed corresponding to the maximum display weight value.

[0134] According to one or more embodiments of the present disclosure, a device for calculating a content display weight value is provided, comprising: a first to-be-displayed content acquisition module, used to acquire the to-be-displayed content; a deep interaction times acquisition module, used to acquire the deep interaction times of the to-be-displayed content, wherein the deep interaction times are the times that a user has deeply interacted with the to-be-displayed content; a judgment module, used to judge whether the deep interaction times are less than a first threshold; a first display weight parameter acquisition module, used to acquire a first probability value and a second probability value of the to-be-displayed content in response to the deep interaction times being less than the first threshold, wherein the first probability value is a probability value of a user having a first interaction with the to-be-displayed content, and the second probability value is a probability value of a user having a deep interaction with the to-be-displayed content; a first display weight value calculation module, used to calculate a display weight value according to the first probability value, the second probability value and the deep interaction times, wherein the display weight value is used to determine whether to display the to-be-displayed content.

[0135] Furthermore, the content display weight value calculation device also includes: a second display weight parameter acquisition module, which is used to obtain 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 in response to the number of deep interactions being greater than or equal to the first threshold, wherein the first resource consumption is the amount of resources required for the user to perform a first interaction with the content to be displayed, and the second resource consumption is the amount of resources required for the user to perform a deep interaction with the content to be displayed; a second display weight value calculation module, which is used to calculate a display weight value based on one or more of the first probability value, the first resource consumption, the second probability value and the second resource consumption, wherein the display weight value is used to determine whether to display the content to be displayed.

[0136] Furthermore, the first display weight value calculation module is further used to calculate the display weight value according to the following formula: Where Y is the display weight value, C is the current second probability value, avgC is the average value of the second probability value, D is the number of deep interactions, E is the first threshold, F=A*B, where A is the probability of the user clicking on the content to be displayed, B is the current first probability value, avgF is the average value of F value, param>0, which is the action amplitude adjustment coefficient.

[0137] Furthermore, the second display weight value calculation module is also used to: calculate the display weight value according to the following formula: Y=A*B*X, where Y is the display weight value, A is the probability of the user clicking on the content to be displayed, B is the current first probability value, and X is the first resource consumption; or, calculate the display weight value according to the following formula: Y=A*B*C*Z, where Y is the display weight value, A is the probability value of the user clicking on the content to be displayed, B is the current first probability value, C is the current second probability value, and Z is the second resource consumption.

[0138] Furthermore, the first display weight parameter acquisition module also includes: a parameter estimation model acquisition module, used to obtain an estimation model of the first probability value and the second probability value in response to the number of deep interactions being less than the first threshold; and a parameter estimation module, used to calculate the first probability value and the second probability value according to the estimation model.

[0139] Furthermore, the second display weight parameter acquisition module also includes: a parameter calculation model acquisition module, used to obtain the first calculation model and / or 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, used to calculate the first probability value and / or the second probability value based on the first calculation model and / or the second calculation model.

[0140] According to one or more embodiments of the present disclosure, a content display device is provided, comprising: a second module for acquiring content to be displayed, used to acquire multiple content to be displayed; a display weight acquisition module, used to acquire display weight values ​​of multiple content to be displayed to obtain multiple display weight values, wherein the multiple display weight values ​​correspond one-to-one to the multiple content to be displayed, wherein the multiple display weight values ​​include display weight values ​​calculated according to any of the above-mentioned content display weight value calculation methods; a maximum display weight value calculation module, used to calculate to obtain the maximum display weight value among the multiple display weight values; and a content display module, used to display the content to be displayed corresponding to the maximum display weight value.

[0141] According to one or more embodiments of the present disclosure, an electronic device is provided, comprising: a memory for storing computer-readable instructions; and a processor for executing the computer-readable instructions, so that the processor implements any one of the above methods when executed.

[0142] According to one or more embodiments of the present disclosure, a non-transitory computer-readable storage medium is provided for storing computer-readable instructions. When the computer-readable instructions are executed by a computer, the computer is enabled to execute any one of the above-described methods.

[0143] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

Claims

1. A method for calculating a content display weight value, comprising: Get the content to be displayed; Acquire the number of deep interactions of the content to be displayed, wherein the number of deep interactions is the number of deep interactions that the user has performed with the content to be displayed; the deep interactions are predefined predetermined operations performed by the user with the content to be displayed; Determining whether the number of deep interactions is less than a first threshold; In response to the number of deep interactions being less than the first threshold, obtaining 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 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; the first interaction is a first type of interaction between the user and the content to be displayed after the user clicks on the content to be displayed; A display weight value is calculated according to the first probability value, the second probability value and the number of deep interactions, wherein the display weight value is used to determine whether to display the content to be displayed.

2. The method for calculating the content display weight value according to claim 1, further comprising: In response to the number of deep interactions being greater than or equal to the first threshold, obtaining 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 amount of resources consumed by the user for the first interaction with the content to be displayed, and the second resource consumption is the amount of resources consumed by the user for the deep interaction with the content to be displayed; A display weight value is calculated according to one or more of the first probability value, the first resource consumption, the second probability value, and the second resource consumption, wherein the display weight value is used to determine whether to display the content to be displayed.

3. The method for calculating the content display weight value according to claim 1, wherein the step of calculating the display weight value according to the first probability value, the second probability value and the number of deep interactions comprises: The display weight value is calculated according to the following formula: ， Where Y is the display weight value, C is the current second probability value, avgC is the average value of the second probability value, D is the number of deep interactions, E is the first threshold, F=A*B, where A is the probability of the user clicking on the content to be displayed, B is the current first probability value, and avgF is the average value of F. >0, it is the action amplitude adjustment coefficient.

4. The method for calculating the content display weight value according to claim 2, wherein the display weight value is calculated according to the first probability value, the second probability value and the number of deep interactions, comprising: The display weight value is calculated according to the following formula: Where Y is the display weight value, A is the probability of the user clicking on the content to be displayed, B is the current first probability value, and X is the first resource consumption; or, The display weight value is calculated according to the following formula: Wherein Y is the display weight value, A is the probability value of the user clicking on the content to be displayed, B is the current first probability value, C is the current second probability value, and Z is the second resource consumption.

5. The method for calculating the content display weight value according to claim 1, wherein in response to the number of deep interactions being less than the first threshold, obtaining the first probability value and the second probability value of the content to be displayed comprises: In response to the number of deep interactions being less than the first threshold, acquiring an estimation model of the first probability value and the second probability value; The first probability value and the second probability value are calculated according to the estimation model.

6. The method for calculating the content display weight value according to claim 2, wherein in response to the number of deep interactions being greater than or equal to the first threshold, obtaining the first probability value and / or the second probability value of the content to be displayed comprises: In response to the number of deep interactions being greater than or equal to the first threshold, acquiring a first calculation model and / or a second calculation model for the first probability value and / or the second probability value; The first probability value and / or the second probability value are calculated according to the first calculation model and / or the second calculation model.

7. A content display method, comprising: Get multiple contents to be displayed; Acquire 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 one-to-one to the plurality of contents to be displayed, wherein the plurality of display weight values ​​include the display weight value calculated according to the method described in claim 1 and / or the display weight value calculated according to the method described in claim 2; Calculating to obtain a maximum display weight value among the multiple display weight values; The to-be-displayed content corresponding to the maximum display weight value is displayed.

8. A device for calculating a content display weight value, comprising: A first module for acquiring content to be displayed, used for acquiring content to be displayed; A deep interaction times acquisition module is used to acquire the deep interaction times of the content to be displayed, wherein the deep interaction times are the times of deep interactions that the user has performed with the content to be displayed; the deep interactions are predefined predetermined operations performed by the user with the content to be displayed; A judging module, used to judge whether the number of deep interactions is less than a first threshold; A first display weight parameter acquisition module is used to acquire a first probability value and a second probability value of the content to be displayed in response to the number of deep interactions being less than the first threshold, wherein the first probability value is a probability value of a user performing a first interaction with the content to be displayed, and the second probability value is a probability value of a user performing a deep interaction with the content to be displayed; the first interaction is a first type of interaction performed by a user with the content to be displayed after the user clicks on the content to be displayed; The first display weight value calculation module is used to calculate the display weight value according to the first probability value, the second probability value and the number of deep interactions, wherein the display weight value is used to determine whether to display the content to be displayed.

9. A content display device, comprising: A second module for acquiring content to be displayed, used for acquiring a plurality of content to be displayed; a display weight acquisition module, configured to acquire 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 one-to-one to the plurality of contents to be displayed, wherein the plurality of display weight values ​​include the display weight value calculated according to the method described in claim 1 and / or the display weight value calculated according to the method described in claim 2; a maximum display weight value calculation module, configured to calculate and obtain a maximum display weight value among the plurality of display weight values; The content display module is used to display the content to be displayed corresponding to the maximum display weight value.

10. An electronic device, comprising: a memory for storing computer readable instructions; as well as A processor, configured to execute the computer-readable instructions, so that the processor implements the method according to any one of claims 1 to 6 when executed.

11. A non-transitory computer-readable storage medium, used for storing computer-readable instructions, which, when executed by a computer, causes the computer to execute the method according to any one of claims 1 to 6.

Images