Excitation method and device for platform user, electronic equipment and medium

Abstract

The invention provides a platform user incentive method and device, electronic equipment and a medium, and relates to the technical field of computers, in particular to the technical field of machine learning and data processing. According to the implementation scheme, in response to the free use duration of a payment function of a platform obtained by a user, a user portrait of the user and behavior characteristics of the user for the platform are obtained; processing the user portrait and the behavior characteristics by using a target machine learning model so as to determine the degree of interest of the user in the additional free use duration for the payment function obtained by executing the target operation; according to the interested degree, determining the length of extra free use duration issued to the user; and prompting the user to obtain the extra free use duration by executing the target operation.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technologies, and more particularly to the fields of machine learning and data processing technologies. Specifically, the present disclosure relates to a method, apparatus, electronic device, computer-readable storage medium, and computer program product for motivating platform users. Background Art

[0002] Currently, platforms motivate users to perform specific operations by providing users with free usage durations of paid features.

[0003] The methods described in this section are not necessarily methods that have been previously conceived or adopted. Unless otherwise specified, no method described in this section should be considered prior art solely because it is included in this section. Similarly, unless otherwise specified, the problems mentioned in this section should not be considered to have been recognized in any prior art. Summary of the Invention

[0004] The present disclosure provides a method, apparatus, electronic device, computer-readable storage medium, and computer program product for motivating platform users.

[0005] According to one aspect of the present disclosure, there is provided a method for motivating platform users, including: in response to a user obtaining a free usage duration of a paid feature of a platform, acquiring the user profile of the user and the behavioral characteristics of the user with respect to the platform; using a target machine learning model to process the user profile and the behavioral characteristics to determine the degree of interest of the user in obtaining an additional free usage duration for the paid feature by performing the target operation; and determining the length of the additional free usage duration to be granted to the user according to the degree of interest; and prompting the user that the additional free usage duration can be obtained by performing the target operation.

[0006] According to another aspect of the present disclosure, there is provided a model training method, including: acquiring a training data set, where the training data set includes a plurality of training data pairs, and each training data pair in the plurality of training data pairs includes first data and second data, the first data indicating the sample user profile of a sample user and the sample behavioral characteristics of the sample user with respect to the platform, and the second data indicating the frequency of the sample user obtaining an additional free usage duration for the paid feature of the platform by performing a target operation; and using the training data set to train an initial machine learning model to obtain a target machine learning model, where the target machine learning model is used to process the target user profile of a target user and the target behavioral characteristics of the target user with respect to the platform to determine the degree of interest of the target user in obtaining the additional free usage duration by performing the target operation, and where the target machine learning model is used to execute the method as described above.

[0007] According to another aspect of the present disclosure, there is provided an incentive device for platform users, including: a first acquisition module configured to acquire a user profile of the user and behavioral characteristics of the user with respect to the platform in response to the user obtaining a free usage period of a paid function of the platform; a processing module configured to process the user profile and the behavioral characteristics using a target machine learning model to determine the degree of interest of the user in obtaining an additional free usage period for the paid function by performing the target operation; a determination module configured to determine the length of the additional free usage period to be granted to the user according to the degree of interest; and a prompting module configured to prompt the user that the additional free usage period can be obtained by performing the target operation.

[0008] According to another aspect of the present disclosure, there is provided a model training device, including: a second acquisition module configured to acquire a training data set, wherein the training data set includes a plurality of training data pairs, and each training data pair in the plurality of training data pairs includes first data and second data, the first data indicating a sample user profile of a sample user and sample behavioral characteristics of the sample user with respect to a platform, and the second data indicating the frequency of the sample user obtaining an additional free usage period for a paid function of the platform by performing a target operation; and a training module configured to train an initial machine learning model using the training data set to obtain a target machine learning model, wherein the target machine learning model is used to process a target user profile of a target user and target behavioral characteristics of the target user with respect to the platform to determine the degree of interest of the target user in obtaining the additional free usage period by performing the target operation, and wherein the target machine learning model is used to execute the method as described above.

[0009] According to another aspect of the present disclosure, there is provided an electronic device, including: at least one processor; and 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 execute the above method.

[0010] According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the above method.

[0011] According to another aspect of the present disclosure, there is provided a computer program product, including a computer program, wherein the computer program implements the above method when executed by a processor.

[0012] According to one or more embodiments of the present disclosure, a method for motivating platform users is provided. By using a machine learning model to process big data related to platform users (user profile data and behavioral feature data), the inherent correlation between the user profile and behavioral features of users and their degree of interest in obtaining additional free usage duration for paid functions by performing target operations is mined (for example, if a user has a high demand for a paid function but low consumption ability, then their degree of interest in obtaining additional free usage duration by performing target operations will be relatively high). Thus, the degree of interest of users in obtaining additional free usage duration by performing target operations can be accurately determined, making the issued additional free usage duration more meet the actual needs of users, effectively motivating platform users, and increasing user activity.

[0013] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The drawings exemplarily illustrate embodiments and form a part of the specification, and are used together with the written description of the specification to explain the exemplary embodiments of the embodiments. The illustrated embodiments are for illustrative purposes only and do not limit the scope of the claims. In all the drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0015] Figure 1 is a schematic diagram illustrating an example system in which various methods described herein can be implemented according to an exemplary embodiment

[0016] Figure 2 shows a flowchart of a method for motivating platform users according to an embodiment of the present disclosure;

[0017] Figure 3 shows a partial flowchart of another method for motivating platform users according to an embodiment of the present disclosure;

[0018] Figure 4 shows a partial flowchart of another method for motivating platform users according to an embodiment of the present disclosure;

[0019] Figure 5 shows a partial flowchart of another method for motivating platform users according to an embodiment of the present disclosure;

[0020] Figure 6 shows a partial flowchart of another method for motivating platform users according to an embodiment of the present disclosure;

[0021] Figure 7Shows a partial flowchart of an incentive method for another platform user according to an embodiment of the present disclosure;

[0022] Figure 8 Shows a partial flowchart of an incentive method for another platform user according to an embodiment of the present disclosure;

[0023] Figure 9 Shows a flowchart of a model training method according to an embodiment of the present disclosure;

[0024] Figure 10 Shows a structural block diagram of an incentive device for a platform user according to an embodiment of the present disclosure;

[0025] Figure 11 Shows a structural block diagram of a model training device according to an embodiment of the present disclosure; and

[0026] Figure 12 Shows a structural block diagram of an exemplary electronic device capable of implementing an embodiment of the present disclosure. Detailed implementation manners

[0027] The following describes exemplary embodiments of the present disclosure with reference to the accompanying drawings. Various details of the embodiments of the present disclosure are included to facilitate understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following.

[0028] In the present disclosure, unless otherwise specified, the terms "first", "second", etc. are used to describe various elements and are not intended to limit the positional relationship, temporal relationship, or importance relationship of these elements. Such terms are only used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of the element, and in certain cases, based on the context description, they may also refer to different instances.

[0029] In the description of various examples in the present disclosure, the terms used are only for the purpose of describing specific examples and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element may be one or more. In addition, the term "and / or" used in the present disclosure covers any one of the listed items and all possible combinations.

[0030] In the related art, the platform motivates users to perform specific operations by granting users a free experience period for paid features. However, this method does not take into account the differences in demand for the same paid feature among different users. It not only has limited incentive for users who truly have a need, but may also increase the usage burden on users who have no need.

[0031] To solve the above problems, the present disclosure provides a method for motivating platform users. By using a machine learning model to process big data related to platform users (user profile data and behavioral feature data), the inherent correlation between the user profile and behavioral features of users and their interest in obtaining additional free usage duration for paid features by performing target operations is mined (for example, if a user has a high demand for a paid feature but low consumption ability, then their interest in obtaining additional free usage duration by performing target operations will be relatively high). Thus, it is possible to accurately determine the interest of users in obtaining additional free usage duration by performing target operations, making the granted additional free usage duration more meet the actual needs of users, effectively motivating platform users, and increasing user activity.

[0032] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0033] Figure 1 FIG. shows a schematic diagram of an exemplary system 100 in which the various methods and apparatuses described herein can be implemented according to an embodiment of the present disclosure. Referring to Figure 1 , the system 100 includes one or more client devices 101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 that couple the one or more client devices to the server 120. The client devices 101, 102, 103, 104, 105, and 106 can be configured to execute one or more application programs.

[0034] In an embodiment of the present disclosure, the server 120 can run one or more services or software applications that enable the implementation of the method for motivating platform users.

[0035] In certain embodiments, the server 120 can also provide other services or software applications that may include non-virtual environments and virtual environments. In certain embodiments, these services can be provided as web-based services or cloud services, for example, provided to users of the client devices 101, 102, 103, 104, 105, and / or 106 under a software as a service (SaaS) model.

[0036] In Figure 1In the configuration shown, server 120 may include one or more components that implement the functions performed by server 120. These components may include software components, hardware components, or combinations thereof that may be executed by one or more processors. Users operating client devices 101, 102, 103, 104, 105, and / or 106 may in turn utilize one or more client applications to interact with server 120 to utilize the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from system 100. Thus, Figure 1 is an example of a system for implementing the methods described herein and is not intended to be limiting.

[0037] Users may use client devices 101, 102, 103, 104, 105, and / or 106 to perform the incentive methods for platform users. The client device may provide an interface that enables a user of the client device to interact with the client device. The client device may also output information to the user via this interface. Although Figure 1 only six client devices are depicted, those skilled in the art will be able to understand that the present disclosure may support any number of client devices.

[0038] Client devices 101, 102, 103, 104, 105, and / or 106 may include various types of computer devices, such as portable handheld devices, general-purpose computers (such as personal computers and laptop computers), workstation computers, wearable devices, smart screen devices, self-service terminal devices, service robots, gaming systems, thin clients, various messaging devices, sensors, or other sensing devices, etc. These computer devices may run various types and versions of software applications and operating systems, such as MICROSOFT Windows, APPLE iOS, UNIX-like operating systems, Linux or Linux-like operating systems (such as GOOGLE Chrome OS); or include various mobile operating systems, such as MICROSOFT WindowsMobile OS, iOS, Windows Phone, Android. Portable handheld devices may include cellular phones, smart phones, tablets, personal digital assistants (PDAs), etc. Wearable devices may include head-mounted displays (such as smart glasses) and other devices. Gaming systems may include various handheld gaming devices, Internet-enabled gaming devices, etc. The client device is capable of executing various different applications, such as various Internet-related applications, communication applications (such as email applications), short message service (SMS) applications, and may use various communication protocols.

[0039] Network 110 can be any type of network well-known to those skilled in the art, which can support data communication using any one of a variety of available protocols (including but not limited to TCP / IP, SNA, IPX, etc.). By way of example only, one or more networks 110 can be a local area network (LAN), an Ethernet-based network, token ring, wide area network (WAN), the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a public switched telephone network (PSTN), an infrared network, a wireless network (such as Bluetooth, WIFI) and / or any combination of these and / or other networks.

[0040] Server 120 can include one or more general-purpose computers, dedicated server computers (such as PC (personal computer) servers, UNIX servers, midrange servers), blade servers, mainframes, server clusters or any other suitable arrangement and / or combination. Server 120 can include one or more virtual machines running a virtual operating system, or other computing architectures involving virtualization (such as one or more flexible pools of logical storage devices that can be virtualized to maintain virtual storage devices of the server). In various embodiments, server 120 can run one or more services or software applications that provide the functions described below.

[0041] The computing units in server 120 can run one or more operating systems including any of the above operating systems as well as any commercially available server operating systems. Server 120 can also run any one of a variety of additional server applications and / or middleware applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, etc.

[0042] In some embodiments, server 120 can include one or more applications to analyze and merge data feeds and / or event updates received from users of client devices 101, 102, 103, 104, 105 and 106. Server 120 can also include one or more applications to display data feeds and / or real-time events via one or more display devices of client devices 101, 102, 103, 104, 105 and 106.

[0043] In some embodiments, server 120 can be a server of a distributed system, or a server incorporating a blockchain. Server 120 can also be a cloud server, or an intelligent cloud computing server or intelligent cloud host with artificial intelligence technology. A cloud server is a host product in the cloud computing service system, which is used to solve the problems of difficult management and weak business scalability existing in traditional physical hosts and virtual private server (VPS, Virtual Private Server) services.

[0044] System 100 may also include one or more databases 130. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 130 may be used to store information such as audio files and video files. The databases 130 may reside in various locations. For example, the databases used by the server 120 may be local to the server 120, or may be remote from the server 120 and may communicate with the server 120 via a network-based or dedicated connection. The databases 130 may be of different types. In some embodiments, the databases used by the server 120 may be, for example, relational databases. One or more of these databases may store, update, and retrieve data to and from the databases in response to commands.

[0045] In some embodiments, one or more of the databases 130 may also be used by applications to store application data. The databases used by applications may be different types of databases, such as key-value stores, object stores, or conventional stores supported by a file system.

[0046] Figure 1 The system 100 can be configured and operated in various ways to enable the application of the various methods and apparatuses described in the present disclosure.

[0047] Figure 2 A flowchart of a method for motivating platform users according to an embodiment of the present disclosure is shown.

[0048] As Figure 2 shown, the method 200 for motivating platform users includes:

[0049] Step 210, in response to a user obtaining a free usage period of a paid function of the platform, obtaining the user profile and the user's behavioral characteristics for the platform;

[0050] Step 220, processing the user profile and behavioral characteristics using a target machine learning model to determine the degree of interest of the user in obtaining an additional free usage period for the paid function by performing a target operation;

[0051] Step 230, determining the length of the additional free usage period to be granted to the user according to the degree of interest; and

[0052] Step 240, prompting the user that an additional free usage period can be obtained by performing the target operation.

[0053] Based on this, by using a machine learning model to process the big data related to platform users (user portrait data and behavior feature data), the inherent correlation between the user portrait and behavior features of the user and the degree of interest of the user in obtaining additional free usage duration for the paid function by performing the target operation is mined (for example, if the user has a high demand for the paid function but a low consumption ability, then the degree of interest in obtaining additional free usage duration by performing the target operation will be relatively high). Thus, the degree of interest of the user in obtaining additional free usage duration by performing the target operation can be accurately determined, making the issued additional free usage duration more meet the actual needs of the user, effectively motivating platform users and increasing user activity.

[0054] In step 210, exemplarily, the platform can be, for example, a platform that provides paid functions such as a book reading website, a video website, a game application, etc. The paid function can be, for example, the reading and audiobook functions of paid book chapters, the viewing function of paid video content, the usage function of paid game content, the paid processing function of image processing software, etc., without limitation.

[0055] In step 210, exemplarily, the target operation can be at least one of the operations associated with user activity such as logging in and using the platform, participating in activities provided by the platform, and using specific functions of the platform.

[0056] According to some embodiments, the target operation includes at least one of watching an advertisement and sharing a link. Thus, on the basis of improving the activity of platform users, the service cost and user acquisition cost of the platform can be further reduced.

[0057] In one example, the user portrait and behavior features of the users who obtain free usage duration by performing the target operation can be obtained to accurately conduct subsequent incentives for users with needs, thereby improving efficiency and reducing the processing difficulty.

[0058] In another example, the platform directly provides free usage duration of the paid function for the user to experience. Therefore, the user portrait and behavior features can be obtained when the user directly obtains the free usage duration to conduct incentives for a wider range of users.

[0059] In step 220, the machine learning model can be, for example, a neural network model, a clustering model, a linear regression model, a logistic regression model, a decision tree model, a support vector machine model, and a Bayesian model, without limitation. Exemplarily, the machine learning model can also be, for example, a combination of at least two of the above models.

[0060] In step 220, the machine learning model can be trained based on a training data set, which includes a plurality of training data pairs. Each training data pair in the plurality of training data pairs includes first data and second data. The first data indicates the sample user profile of the sample user and the sample behavior characteristics of the sample user with respect to the platform, and the second data indicates the frequency of the sample user obtaining additional free usage duration of the paid function for the platform by performing the target operation.

[0061] Thus, the trained machine learning model can find the correlation between the user profile and behavior characteristics of the user and the frequency of performing the target operation to obtain additional free duration of the paid function, so as to be used for predicting the degree of interest of the user.

[0062] Exemplarily, the training data set can be obtained, for example, based on sample users extracted from the platform.

[0063] In one example, to further simplify the training process, classification can be performed based on the result of the second data, that is, the training data pairs including the second data with a frequency greater than or equal to 1 are used as positive samples (obtaining additional free duration by performing the target operation), and the training data pairs including the second data with a frequency less than 1 are used as negative samples (not obtaining additional free duration by performing the target operation), so as to train the machine learning model based on the classified positive and negative samples.

[0064] In step 220, the degree of interest can be, for example, the probability of the user obtaining additional free usage duration by performing the target operation.

[0065] In step 230, different lengths of additional free usage duration can be granted to different users according to the degree of interest of the users. In one example, longer additional free usage duration can be granted to users with a lower degree of interest to encourage more users to perform the target operation. In another example, longer additional free usage duration can be granted to users with a higher degree of interest to encourage the user to perform the target operation more times.

[0066] In step 240, exemplarily, the user can be prompted in the form of a pop-up window. Exemplarily, the user can be prompted when the free usage duration of the user is about to expire to better encourage the user.

[0067] Exemplarily, the user can be prompted once or multiple times to further encourage the user to perform the target operation by granting additional free usage duration once or multiple times.

[0068] Figure 3 Shows a partial flowchart of another method for motivating platform users according to an embodiment of the present disclosure.

[0069] According to some embodiments, the behavioral characteristics in step 210 at least include habitual behavioral characteristics, and the habitual behavioral characteristics indicate, for each of a plurality of first time periods within a day, the first degree of inclination of the user to use the paid function on the platform.

[0070] And wherein, as Figure 3 shown, step 220 at least includes:

[0071] Step 310, processing the habitual behavioral characteristics using a machine learning model to determine the second degree of inclination of the user to use the paid function on the platform at the current moment; and

[0072] Step 320, determining the degree of interest according to the second degree of inclination.

[0073] Thus, by introducing the habitual behavioral characteristics of the user, the accuracy of determining the degree of interest of the user can be improved. For example, if it is determined that the current moment falls within the high-frequency time period when the user uses the paid function, it can be determined that the user has a relatively high degree of interest in obtaining additional free usage time by performing the target operation at this time.

[0074] Exemplarily, the time of a day can be divided based on time units. For example, each hour can be used as a first time period. Exemplarily, the time of a day can be divided based on the number of required first time periods. For example, the time of a day can be divided into three first time periods, and each first time period includes eight hours.

[0075] Exemplarily, only the time periods with relatively high overall user activity during a day can be selected as the first time periods to further simplify the processing process.

[0076] According to some embodiments, the behavioral characteristics at least include user activity, and the user activity is associated with the frequency and duration of the user using the paid function on the platform.

[0077] Thus, by introducing the frequency and duration of the user using the paid function, the accuracy of determining the degree of interest of the user can be improved. For example, if it is determined that the frequency and duration of the user using the paid function are relatively high, it can be determined that the user has a stronger demand for the paid function, and the degree of interest in obtaining additional free usage time by performing the target operation is also relatively high.

[0078] According to some embodiments, the behavioral characteristics at least include consumption behavioral characteristics, and the consumption behavioral characteristics are associated with the historical consumption times and historical consumption amounts of the user for the paid function.

[0079] Thus, by introducing the user's historical consumption times and historical consumption amounts for the paid function, the accuracy of determining the user's degree of interest can be improved. For example, if it is determined that the user's historical consumption times and historical consumption amounts for the paid function are high, it can be determined that the user has a stronger demand for the paid function, and their degree of interest in obtaining additional free usage time by performing the target operation is also relatively high.

[0080] According to some embodiments, the user profile at least includes the user's age and education level.

[0081] The user's age and education level are generally positively correlated with the user's economic level. Thus, by introducing the user's age and education level, the accuracy of determining the user's degree of interest can be improved. For example, if it is determined that the user's age and education level are low, it can be determined that the user's economic level is generally low, and their degree of interest in obtaining additional free usage time by performing the target operation is also relatively high.

[0082] According to some embodiments, the paid function includes at least one of the reading function of the target text and the viewing function of the target video. The above reading function includes the function of reading books and the function of listening to books.

[0083] Figure 4 A partial flowchart of another method for motivating platform users according to an embodiment of the present disclosure is shown.

[0084] According to some embodiments, as Figure 4 shown, in addition to the above steps 210 to 230, method 200 further includes:

[0085] Step 410, in response to the paid function being the reading function of the target text and in response to the target text being a book, obtain the first completion progress of the book;

[0086] Step 420, obtain the user's current reading progress for the book; and

[0087] Step 430, use the first machine learning model to process according to the first completion progress, the current reading progress, the user profile, and the behavioral characteristics to determine the degree of interest.

[0088] For the situation where the user is using the free reading duration of the paid text, by introducing the serial status of the book currently being read by the user and the user's reading progress, the accuracy of determining the user's degree of interest can be improved. For example, the user's willingness to pay for a new chapter of a serial book is higher than that for a completed book. Therefore, the degree of interest of a user who is currently reading a new chapter of a serial book in obtaining additional free usage time by performing the target operation is also relatively high.

[0089] Exemplarily, the first completion progress may be, for example, the completion status of a book (serialized or completed) and the current total number of words of the book, etc.

[0090] Figure 5 A partial flowchart of an incentive method for another platform user according to an embodiment of the present disclosure is shown.

[0091] According to some embodiments, as Figure 5 shown, in addition to the above steps 210 to step 230, method 200 further includes:

[0092] Step 510, in response to the payment function being the reading function of the target text and in response to the target text being a book, obtain the first book type that the user is interested in and the second book type of the book that the user is currently reading;

[0093] Step 520, determine the first matching degree between the first book type and the second book type; and

[0094] Step 530, process according to the first matching degree, user profile and behavioral characteristics using a machine learning model to determine the degree of interest.

[0095] For the case where the user is using the free reading duration of the paid text, by introducing a comparison between the book types favored by the user and the book type currently being read, the accuracy of determining the user's degree of interest can be improved. For example, if the book type currently being read by the user matches the book type they like, the user's degree of interest in obtaining additional free usage time by performing the target operation is also relatively high.

[0096] Exemplarily, the first book type and the second book type may be, for example, science fiction, history, fantasy, ancient and modern, etc. Exemplarily, for a book reading platform, the historical reading records and historical search records of the user can be obtained, and a large model can be used to understand the historical reading records and historical search records of the user to determine the first book type that the user is interested in from them.

[0097] Figure 6 A partial flowchart of an incentive method for another platform user according to an embodiment of the present disclosure is shown.

[0098] According to some embodiments, as Figure 6 shown, in addition to the above steps 210 to step 230, method 200 further includes:

[0099] Step 610, in response to the payment function being the viewing function of the target video and in response to the target video being a drama series, obtain the second completion progress of the drama series;

[0100] Step 620, obtain the current viewing progress of the user for the drama series; and

[0101] Step 630: Process using a machine learning model based on the second completion progress, current viewing progress, user profile, and behavioral characteristics to determine the degree of interest.

[0102] For the case where the user is using the free viewing duration of a paid drama series, by introducing the serial status of the drama series the user is currently watching and the user's viewing progress, the accuracy of determining the user's degree of interest can be improved. For example, the user's willingness to pay for new episodes of a serial drama series is higher than that for a completed drama series. Therefore, the degree of interest of a user who is currently watching new episodes of a serial drama series in obtaining additional free usage time by performing the target operation is also relatively high.

[0103] Exemplarily, the first completion progress can be, for example, the completion status (serial or completed) of the drama series and the total number of episodes of the drama series, etc.

[0104] Figure 7 Shows a partial flowchart of another method for motivating users of a platform according to an embodiment of the present disclosure.

[0105] According to some embodiments, as Figure 7 shown, in addition to the above steps 210 to 230, method 200 further includes:

[0106] Step 710: In response to the payment function being the viewing function of the target video and in response to the target video being a drama series, obtain the first drama series type that the user is interested in and the second drama series type of the drama series that the user is currently watching;

[0107] Step 720: Determine the second matching degree between the first drama series type and the second drama series type; and

[0108] Step 730: Process using a machine learning model based on the second matching degree, user profile, and behavioral characteristics to determine the degree of interest.

[0109] For the case where the user is using the free viewing duration of a paid drama series, by introducing a comparison between the drama series type the user likes and the drama series type the user is currently watching, the accuracy of determining the user's degree of interest can be improved. For example, if the drama series type the user is currently watching matches the drama series type the user likes, the degree of interest of the user in obtaining additional free usage time by performing the target operation is also relatively high.

[0110] Exemplarily, the first drama series type and the second drama series type can be, for example, science fiction, history, period, and suspense, etc. Exemplarily, for a video viewing platform, the historical viewing records and historical search records of the user can be obtained, and a large model can be used to understand the historical viewing records and historical search records of the user to determine the first drama series type that the user is interested in from them.

[0111] Figure 8 The figure shows a partial flowchart of an incentive method for another platform user according to an embodiment of the present disclosure.

[0112] According to some embodiments, as Figure 8 shown, in addition to the above steps 210 to 230, method 200 further includes:

[0113] Step 810, obtaining the historical number of times the user performs the target operation within a second time period; and

[0114] Step 820, determining the length of the additional free usage duration granted to the user according to the historical number of times and the degree of interest.

[0115] Based on this, the total length of the free usage duration that the user can use can be controlled to effectively balance the service cost paid by the platform and the user activity.

[0116] In step 810, the second time period can be, for example, the day before the current moment, or the time period between when the user registers on the platform and the current moment.

[0117] In step 820, exemplarily, more additional free usage duration can be granted to users with a lower historical number of times of performing the target operation to encourage them to perform the target operation more and improve their activity.

[0118] Figure 9 The figure shows a flowchart of a model training method according to an embodiment of the present disclosure.

[0119] As Figure 9 shown, the incentive method 900 for platform users includes:

[0120] Step 910, obtaining a training data set, where the training data set includes a plurality of training data pairs, and each training data pair in the plurality of training data pairs includes first data and second data. The first data indicates the sample user profile of the sample user and the sample behavior characteristics of the sample user for the platform, and the second data indicates the frequency of the sample user obtaining additional free usage duration for the paid functions of the platform by performing the target operation; and

[0121] Step 920, using the training data set to train an initial machine learning model to obtain a target machine learning model, where the target machine learning model is used to process the target user profile of the target user and the target behavior characteristics of the target user for the platform to determine the degree of interest of the target user in obtaining additional free usage duration by performing the target operation, and where the target machine learning model is used to execute the method as described above.

[0122] For the detailed description of method 900, reference can be made to the detailed description of step 220 above, which will not be repeated here.

[0123] Figure 10 The flowchart of the incentive device for platform users according to an embodiment of the present disclosure is shown.

[0124] According to another aspect of the present disclosure, as Figure 10 shown, an incentive device 1000 for platform users is provided, including: a first acquisition module 1010 configured to acquire a user profile of a user and behavioral characteristics of the user with respect to the platform in response to the user obtaining a free usage period of a paid function of the platform; a processing module 1020 configured to process the user profile and behavioral characteristics using a target machine learning model to determine the degree of interest of the user in obtaining an additional free usage period for the paid function by performing a target operation; a determination module 1030 configured to determine the length of the additional free usage period to be granted to the user according to the degree of interest; and a prompting module 1040 configured to prompt the user that an additional free usage period can be obtained by performing the target operation.

[0125] Figure 11 The flowchart of the model training device according to an embodiment of the present disclosure is shown.

[0126] According to another aspect of the present disclosure, as Figure 11 shown, an incentive device 1100 for platform users is provided, including: a second acquisition module 1110 configured to acquire a training data set, where the training data set includes a plurality of training data pairs, and each training data pair in the plurality of training data pairs includes first data and second data, the first data indicating a sample user profile of a sample user and sample behavioral characteristics of the sample user with respect to the platform, and the second data indicating the frequency of the sample user obtaining an additional free usage period for a paid function of the platform by performing a target operation; and a training module 1120 configured to train an initial machine learning model using the training data set to obtain a target machine learning model, where the target machine learning model is used to process a target user profile of a target user and target behavioral characteristics of the target user with respect to the platform to determine the degree of interest of the target user in obtaining an additional free usage period by performing the target operation, and where the target machine learning model is used to execute the method described above.

[0127] According to another aspect of the present disclosure, an electronic device is further provided, including: at least one processor; and a memory communicatively connected to the at least one processor; where 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 execute the foregoing method.

[0128] According to another aspect of the present disclosure, there is also provided a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the foregoing method.

[0129] According to another aspect of the present disclosure, there is also provided a computer program product including a computer program, wherein the computer program, when executed by a processor, implements the foregoing method.

[0130] As Figure 12 shown, the electronic device 1200 includes a computing unit 1201, which can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 1202 or the computer program loaded from the storage unit 1208 into the random access memory (RAM) 1203. In the RAM 1203, various programs and data required for the operation of the electronic device 1200 can also be stored. The computing unit 1201, the ROM 1202, and the RAM 1203 are connected to each other through a bus 1204. The input / output (I / O) interface 1205 is also connected to the bus 1204.

[0131] A plurality of components in the electronic device 1200 are connected to the I / O interface 1205, including: an input unit 1206, an output unit 1207, a storage unit 1208, and a communication unit 1209. The input unit 1206 can be any type of device capable of inputting information into the electronic device 1200. The input unit 1206 can receive input digital or character information, and generate key signal inputs related to the user settings and / or function controls of the electronic device, and can include but are not limited to a mouse, a keyboard, a touch screen, a trackpad, a trackball, a joystick, a microphone, and / or a remote control. The output unit 1207 can be any type of device capable of presenting information, and can include but are not limited to a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 1208 can include but are not limited to a magnetic disk, an optical disk. The communication unit 1209 allows the electronic device 1200 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include but are not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth TM device, an 802.11 device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0132] The computing unit 1201 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 1201 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 1201 executes the various methods and processes described above, such as the GPU-based matrix calculation method. For example, in some embodiments, the GPU-based matrix calculation method can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 1208. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 1200 via the ROM 1202 and / or the communication unit 1209. When the computer program is loaded into the RAM 1203 and executed by the computing unit 1201, one or more steps of the GPU-based matrix calculation method described above can be executed. Alternatively, in other embodiments, the computing unit 1201 can be configured to execute the GPU-based matrix calculation method by any other suitable means (e.g., by means of firmware).

[0133] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field-programmable gate arrays (FPGA), application-specific integrated circuits (ASIC), application-specific standard products (ASSP), systems-on-a-chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a dedicated or general-purpose programmable processor, receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0134] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to the processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program codes can be executed entirely on the machine, partially on the machine, as an independent software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0135] In the context of this disclosure, a machine-readable medium can 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. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, 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.

[0136] In order to provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, speech input, or tactile input).

[0137] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by 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), and the Internet.

[0138] A computer system can include a client and a server. The client and the server are generally far apart from each other and typically interact through a communication network. The relationship between the client and the server is generated by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, can also be a server of a distributed system, or a server incorporating a blockchain.

[0139] It should be understood that the various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and no limitations are imposed herein.

[0140] Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above methods, systems, and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but is only defined by the authorized claims and their equivalent scope. Various elements in the embodiments or examples can be omitted or replaced by their equivalent elements. In addition, the steps can be executed in an order different from that described in the present disclosure. Further, the various elements in the embodiments or examples can be combined in various ways. Importantly, with the evolution of technology, many of the elements described herein can be replaced by equivalent elements that emerge after the present disclosure.

Claims

1. A method for motivating platform users, comprising: In response to the user obtaining free usage time of a paid function of the platform, obtaining a user profile of the user and a behavioral characteristic of the user with respect to the platform; Processing the user profile and the behavioral characteristics using a target machine learning model to determine the user's interest in obtaining additional free usage time for the paid function by performing the target operation; as well as Determining the length of the additional free usage time issued to the user according to the interest level; as well as The user is prompted that the additional free usage time can be obtained by performing the target operation.

2. The method according to claim 1, wherein: The behavior characteristics at least include a habit behavior characteristic, wherein the habit behavior characteristic indicates, for each first time period in a plurality of first time periods within a day, a first tendency of the user to use the paid function on the platform, And wherein, the processing of the behavioral features using a machine learning model to determine the user's interest in obtaining additional free usage time for the paid function by performing the target operation comprises at least: Processing the habitual behavior features using the machine learning model to determine a second propensity of the user to use the paid function on the platform at the current moment; and The interest level is determined based on the second tendency level.

3. The method according to claim 1 or 2, wherein: The behavioral characteristics include at least user activity, and the user activity is associated with the frequency and duration of the user's use of the paid function on the platform.

4. The method according to any one of claims 1 to 3, wherein: The behavior characteristics at least include consumption behavior characteristics, and the consumption behavior characteristics are associated with the user's historical consumption times and historical consumption amounts for the paid function.

5. The method according to any one of claims 1 to 4, wherein: The user portrait includes at least the user's age and education level.

6. The method according to any one of claims 1 to 5, wherein: The paid function includes at least one of a reading function of a target text and a viewing function of a target video.

7. The method according to claim 6, wherein: Also includes: In response to the payment function being a reading function of the target text, and in response to the target text being a book, acquiring a first completion progress of the book; Obtaining the user's current reading progress for the book; as well as The first completion progress, the current reading progress, the user portrait and the behavioral characteristics are processed using the machine learning model to determine the interest level.

8. The method according to claim 6 or 7, further comprising: In response to the payment function being a reading function of the target text, and in response to the target text being a book, acquiring a first book type that the user is interested in and a second book type of the book that the user is currently reading; determining a first degree of matching between the first book type and the second book type; as well as The first matching degree, the user profile, and the behavioral characteristics are processed using the machine learning model to determine the interest level.

9. The method according to claim 6, wherein: Also includes: In response to the payment function being a viewing function of the target video, and in response to the target video being a TV series, obtaining a second completion progress of the TV series; Obtaining the user's current viewing progress of the episode; as well as The second completion progress, the current viewing progress, the user portrait and the behavioral characteristics are processed using the machine learning model to determine the interest level.

10. The method according to claim 6 or 7, further comprising: In response to the payment function being a viewing function of the target video, and in response to the target video being a TV series, obtaining a first TV series type that the user is interested in and a second TV series type of the TV series currently being watched by the user; determining a second degree of matching between the first episode type and the second episode type; as well as The second matching degree, the user profile, and the behavioral characteristics are processed using the machine learning model to determine the interest level.

11. The method according to any one of claims 1 to 10, wherein: The target action includes at least one of viewing an advertisement and sharing a link.

12. The method according to any one of claims 1 to 11, further comprising: Obtaining a historical number of times the user performs the target operation within a second time period; as well as The length of the additional free usage time issued to the user is determined according to the historical number of times and the interest level.

13. A model training method, comprising: Acquire a training data set, wherein the training data set includes a plurality of training data pairs, each of the plurality of training data pairs includes first data and second data, the first data indicating a sample user portrait of a sample user and a sample behavior characteristic of the sample user with respect to a platform, and the second data indicating a frequency of the sample user obtaining additional free usage time for a paid function of the platform by performing a target operation; and An initial machine learning model is trained using the training data set to obtain a target machine learning model, wherein the target machine learning model is used to process a target user portrait of a target user and target behavior characteristics of the target user for the platform to determine the target user's interest in obtaining the additional free usage time by performing the target operation, and wherein the target machine learning model is used to execute the method as described in any one of claims 1-12.

14. A device for motivating platform users, comprising: A first acquisition module is configured to acquire a user portrait of the user and a behavior characteristic of the user with respect to the platform in response to the free usage time of a paid function of the platform obtained by the user; a processing module configured to process the user portrait and the behavior characteristics using a target machine learning model to determine the user's interest in obtaining additional free usage time for the paid function by performing the target operation; as well as a determination module configured to determine the length of the additional free usage time issued to the user according to the interest level; as well as The prompt module is configured to prompt the user that the user can obtain the additional free usage time by performing the target operation.

15. A model training device, comprising: a second acquisition module configured to acquire a training data set, wherein the training data set includes a plurality of training data pairs, each of the plurality of training data pairs includes first data and second data, the first data indicating a sample user portrait of a sample user and a sample behavior characteristic of the sample user with respect to a platform, and the second data indicating a frequency of the sample user obtaining additional free usage time for a paid function of the platform by performing a target operation; and A training module is configured to train an initial machine learning model using the training data set to obtain a target machine learning model, wherein the target machine learning model is used to process a target user portrait of a target user and the target behavior characteristics of the target user for the platform to determine the target user's interest in obtaining the additional free usage time by performing the target operation, and wherein the target machine learning model is used to execute the method as described in any one of claims 1-12.

16. An electronic device, comprising: at least one processor; as well as a memory communicatively coupled to the at least one processor; in The memory stores instructions that can be executed 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 the method according to any one of claims 1 to 12.

17. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-12.

18. A computer program product comprising a computer program, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 12 is implemented.

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