Recommended method, device, electronic equipment and storage medium

Abstract

The present disclosure provides a recommendation method and device, electronic equipment and storage medium. The recommendation method comprises: obtaining resource submission characteristics of a target user, wherein the resource submission characteristics comprise resource withdrawal characteristics for representing historical statistical conditions of the target user withdrawing submitted resources; determining whether the target user meets a condition of participating in a target submission activity of a target resource submission mode according to the obtained resource submission characteristics, wherein the target submission activity is an activity for promoting users to submit resources through the target resource submission mode; and recommending the target submission activity to the target user in a case where it is determined that the target user meets the condition.

Description

Technical Field

[0001] This disclosure generally relates to the field of data processing technology, and more specifically, to a recommended method, apparatus, electronic device, and storage medium. Background Technology

[0002] In the resource submission interface, users are usually provided with multiple resource submission methods to choose from. When a certain resource submission method is currently offering submission activities to encourage users to submit resources through that method, users' willingness to submit resources through that method will be greatly increased.

[0003] The current method for submitting resources for a submission activity typically does not recommend the activity to all users, but rather selects a subset of users. However, the current user evaluation method, which assesses users to determine whether to recommend the activity, results in the activity's promotional effect being less than ideal. Summary of the Invention

[0004] Exemplary embodiments of this disclosure provide a recommendation method, apparatus, electronic device, and storage medium that can solve the problem that the promotion effect of resource delivery methods in related technologies is not ideal.

[0005] According to a first aspect of the present disclosure, a recommendation method is provided, the recommendation method comprising: acquiring resource submission characteristics of a target user, wherein the resource submission characteristics include: resource withdrawal characteristics for characterizing historical statistics of the target user withdrawing submitted resources; determining, based on the acquired resource submission characteristics, whether the target user meets the conditions for participating in a target submission activity of a target resource submission method, wherein the target submission activity is an activity for promoting users to submit resources through the target resource submission method; and recommending the target submission activity to the target user if it is determined that the target user meets the conditions.

[0006] Optionally, the resource delivery feature further includes: a delivery method preference feature used to characterize the target user's preference for the resource delivery method.

[0007] Optionally, the step of determining whether the target user meets the conditions for participating in the target resource submission activity based on the acquired resource submission characteristics includes: determining the target user's preference level for the target resource submission method based on the submission method preference characteristics, and determining whether the preference level meets a first preset condition; determining the probability that the target user will withdraw resources after submitting resources through the target submission activity based on the resource withdrawal characteristics, and determining whether the probability meets a second preset condition; and determining that the target user meets the conditions for participating in the target submission activity if the preference level meets the first preset condition and the probability meets the second preset condition.

[0008] Optionally, the resource withdrawal feature includes at least one of the following features: the proportion of the total frequency of resource withdrawals by the target user in history to the total frequency of resource submissions; the proportion of the total size of resources withdrawn by the target user in history to the total size of resources submitted; the proportion of the total frequency of resource withdrawals after submission through the target resource submission activity in history to the total frequency of resource submissions through the target resource submission activity; the proportion of the total size of resources withdrawn after submission through the target resource submission activity in history to the total size of resources submitted through the target resource submission activity; the proportion of the total frequency of resource withdrawals after submission through the target resource submission method in history to the total frequency of resource submissions through the target resource submission method. The percentage of total frequency of resource submissions via the resource submission method; the percentage of the total size of resources submitted and withdrawn by the target user through the target resource submission method in history, relative to the total size of resources submitted through the target resource submission method; the percentage of the total frequency of resources submitted and withdrawn by the target user through the target resource submission method in history, relative to the total frequency of resources submitted through the target resource submission method; and the percentage of the total size of resources submitted and withdrawn by the target user through the target resource submission method in history, relative to the total size of resources submitted through the target resource submission method.

[0009] Optionally, the step of determining the probability that the target user will withdraw resources after submitting them through the target submission activity based on the resource withdrawal features includes: performing a weighted summation of each feature included in the resource withdrawal features to obtain the probability that the target user will withdraw resources after submitting them through the target submission activity.

[0010] Optionally, the submission method preference feature includes at least one of the following features: the proportion of the total frequency of the target user's historical resource submissions via the target resource submission method to the total frequency of resource submissions; the proportion of the total size of the target user's historical resource submissions via the target resource submission method to the total size of resource submissions.

[0011] Optionally, the step of determining the target user's preference for the target resource delivery method based on the delivery method preference features includes: performing a weighted summation of each feature included in the delivery method preference features to obtain the target user's preference for the target resource delivery method.

[0012] Optionally, the first preset condition includes: greater than a first threshold and less than a second threshold; the second preset condition includes: less than a third threshold.

[0013] Optionally, the step of recommending the target submission activity to the target user includes: displaying information about the target submission activity to the target user in an interface for the target user to select the current resource submission method from a variety of available resource submission methods.

[0014] Optionally, the step of determining whether the target user meets the conditions for participating in the target resource submission activity based on the acquired resource submission features includes: inputting the acquired resource submission features into a pre-trained machine learning model to obtain the classification result of whether the target user meets the conditions for participating in the target submission activity output by the machine learning model.

[0015] According to a second aspect of the present disclosure, a recommendation apparatus is provided, the recommendation apparatus comprising: a feature acquisition unit configured to acquire resource submission features of a target user, wherein the resource submission features include: resource withdrawal features for characterizing historical statistics of the target user withdrawing submitted resources; a determination unit configured to determine, based on the acquired resource submission features, whether the target user meets the conditions for participating in a target submission activity of a target resource submission method, wherein the target submission activity is an activity for promoting users to submit resources through the target resource submission method; and a recommendation unit configured to recommend the target submission activity to the target user if it is determined that the target user meets the conditions.

[0016] Optionally, the resource delivery feature further includes: a delivery method preference feature used to characterize the target user's preference for the resource delivery method.

[0017] Optionally, the determining unit is configured to: determine the target user's preference level for the target resource submission method based on the submission method preference features, and determine whether the preference level meets a first preset condition; determine the probability that the target user will withdraw the resource after submitting it through the target submission activity based on the resource withdrawal features, and determine whether the probability meets a second preset condition; and determine that the target user meets the conditions for participating in the target submission activity if the preference level meets the first preset condition and the probability meets the second preset condition.

[0018] Optionally, the resource withdrawal feature includes at least one of the following features: the proportion of the total frequency of resource withdrawals by the target user in history to the total frequency of resource submissions; the proportion of the total size of resources withdrawn by the target user in history to the total size of resources submitted; the proportion of the total frequency of resource withdrawals after submission through the target resource submission activity in history to the total frequency of resource submissions through the target resource submission activity; the proportion of the total size of resources withdrawn after submission through the target resource submission activity in history to the total size of resources submitted through the target resource submission activity; the proportion of the total frequency of resource withdrawals after submission through the target resource submission method in history to the total frequency of resource submissions through the target resource submission method. The percentage of total frequency of resource submissions via the resource submission method; the percentage of the total size of resources submitted and withdrawn by the target user through the target resource submission method in history, relative to the total size of resources submitted through the target resource submission method; the percentage of the total frequency of resources submitted and withdrawn by the target user through the target resource submission method in history, relative to the total frequency of resources submitted through the target resource submission method; and the percentage of the total size of resources submitted and withdrawn by the target user through the target resource submission method in history, relative to the total size of resources submitted through the target resource submission method.

[0019] Optionally, the determining unit is configured to: perform a weighted summation of each feature included in the resource withdrawal feature to obtain the probability that the target user will withdraw the resource after submitting it through the target submission activity.

[0020] Optionally, the submission method preference feature includes at least one of the following features: the proportion of the total frequency of the target user's historical resource submissions via the target resource submission method to the total frequency of resource submissions; the proportion of the total size of the target user's historical resource submissions via the target resource submission method to the total size of resource submissions.

[0021] Optionally, the determining unit is configured to: perform a weighted summation of each feature included in the submission method preference feature to obtain the target user's preference degree for the target resource submission method.

[0022] Optionally, the first preset condition includes: greater than a first threshold and less than a second threshold; the second preset condition includes: less than a third threshold.

[0023] Optionally, the recommendation unit is configured to display information about the target submission activity to the target user in an interface where the target user selects the current resource submission method from a variety of available resource submission methods.

[0024] Optionally, the determining unit is configured to: input the acquired resource submission features into a pre-trained machine learning model to obtain a classification result output by the machine learning model indicating whether the target user meets the conditions for participating in the target submission activity.

[0025] According to a third aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and at least one memory storing computer-executable instructions, wherein the computer-executable instructions, when executed by the at least one processor, cause the at least one processor to perform the recommended method as described above.

[0026] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided that, when instructions in the computer-readable storage medium are executed by at least one processor, causes the at least one processor to perform the recommended method as described above.

[0027] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising computer instructions, wherein the computer instructions, when executed by at least one processor, implement the recommended method as described above.

[0028] The recommendation method, apparatus, electronic device, and storage medium according to exemplary embodiments of this disclosure can determine whether a user meets the conditions for participating in a resource submission activity based on historical statistics of the user's withdrawal of submitted resources. Only when the user meets the conditions is the submission activity recommended to them, thereby promoting the user to submit resources through the resource submission method, improving the utilization rate of the submission activity's budget, and maximizing the promotion effect of the submission activity.

[0029] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0030] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0031] Figure 1 A flowchart illustrating a recommended method according to exemplary embodiments of the present disclosure;

[0032] Figure 2 A flowchart illustrating a method for determining whether a target user meets the conditions for participating in a target submission activity of a target resource submission method, according to an exemplary embodiment of the present disclosure;

[0033] Figure 3 This illustration shows an example of recommending a target delivery activity to a target user according to an exemplary embodiment of this disclosure;

[0034] Figure 4 A structural block diagram of a recommended apparatus according to exemplary embodiments of the present disclosure is shown;

[0035] Figure 5 A structural block diagram of an electronic device according to an exemplary embodiment of the present disclosure is shown. Detailed Implementation

[0036] To enable those skilled in the art to better understand the technical solutions of this disclosure, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0037] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0038] It should be noted that the phrase "at least one of several items" in this disclosure refers to three parallel cases: "any one of the several items", "a combination of any number of the several items", and "all of the several items". For example, "including at least one of A and B" includes the following three parallel cases: (1) including A; (2) including B; (3) including A and B. As another example, "performing at least one of step one and step two" indicates the following three parallel cases: (1) performing step one; (2) performing step two; (3) performing both step one and step two.

[0039] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties.

[0040] Figure 1 A flowchart illustrating a recommended method according to an exemplary embodiment of this disclosure is provided.

[0041] Reference Figure 1 In step S101, the resource submission characteristics of the target user are obtained.

[0042] As an example, step S101 can be executed when the target user needs to submit resources.

[0043] In step S102, based on the acquired resource submission characteristics, it is determined whether the target user meets the conditions for participating in the target submission activity of the target resource submission method.

[0044] The target submission activity is an activity designed to encourage users to submit resources using the target resource submission method. For example, the submission activity could be a promotional activity.

[0045] The resource submission features include: resource withdrawal features used to characterize the historical statistics of the target user withdrawing submitted resources.

[0046] As an example, the resource delivery feature may further include: a delivery method preference feature used to characterize the target user's preference for the resource delivery method.

[0047] It should be understood that the resource delivery features may also include other types of features used to characterize resource delivery information of the target user, and this disclosure does not limit them.

[0048] As an example, the resource withdrawal feature may include at least one of the following features A1 to A8. It should be understood that the resource withdrawal feature may also include other types of features used to characterize the historical statistics of a target user withdrawing submitted resources, and this disclosure is not limiting in this regard.

[0049] Feature A1: The proportion of the target user's total frequency of resource withdrawals to the total frequency of resource submissions throughout history. Here, the total frequency of resource submissions includes the total frequency of resource submissions through all resource submission methods.

[0050] As an example, feature A1 may further include multiple sub-features based on the time window. For instance, feature A1 may further include: the proportion of the total frequency of resource withdrawals by the target user in the first time window to the total frequency of resource submissions; the proportion of the total frequency of resource withdrawals by the target user in the second time window to the total frequency of resource submissions; and the proportion of the total frequency of resource withdrawals by the target user in the third time window to the total frequency of resource submissions. For example, the first time window may be the most recent month, the second time window may be the most recent three months, and the third time window may be the most recent year. It should be understood that the specific size of each time window and the total number of time windows can be set according to the actual situation and specific needs, and this disclosure does not impose any restrictions on this.

[0051] Feature A2: The proportion of the total size of resources withdrawn by the target user in history to the total size of resources submitted. Here, the total size of resources submitted includes the total size of resources submitted through all resource submission methods.

[0052] Feature A3: The proportion of the total frequency of resource submissions followed by resource withdrawals by the target user throughout history, relative to the total frequency of resource submissions through submission activities. It should be understood that "submission activities" here can include all submission activities using all resource submission methods.

[0053] Feature A4: The proportion of the total amount of resources submitted and withdrawn by the target user through submission activities in history to the total amount of resources submitted through submission activities.

[0054] Feature A5: The proportion of the total frequency of the target user submitting resources and then withdrawing them through the target resource submission method in history, relative to the total frequency of resources submitted through the target resource submission method.

[0055] Feature A6: The proportion of the total size of resources submitted and withdrawn by the target user through the target resource submission method in the history of the target user to the total size of resources submitted through the target resource submission method.

[0056] Feature A7: The proportion of the total frequency of resource submissions and subsequent withdrawals by the target user through the target resource submission method in history, relative to the total frequency of resource submissions through the target resource submission method. It should be understood that the target resource submission method here may include all submissions using the target resource submission method.

[0057] Feature A8: The proportion of the total size of resources submitted and withdrawn by the target user in the past through the target resource submission method to the total size of resources submitted through the target resource submission method.

[0058] It should be understood that features A2 through A8, as in the example of feature A1, can be further divided into multiple sub-features based on the time window, which will not be elaborated further here. It should be understood that the specific size of each time window corresponding to each feature from A1 to A8, as well as the total number of time windows, can be set according to the actual situation and specific needs, and this disclosure does not impose any restrictions on this.

[0059] As an example, the submission method preference feature may include at least one of the following features B1 to B2. It should be understood that the submission method preference feature may also include other types of features used to characterize a target user's preference for a resource submission method, and this disclosure is not limiting in this regard.

[0060] Feature B1: The proportion of the total frequency of resource submissions by the target user in history through the target resource submission method to the total frequency of resource submissions. Here, the total frequency of resource submissions includes the total frequency of resource submissions through all resource submission methods.

[0061] Feature B2: The proportion of the total size of resources submitted by the target user historically through the target resource submission method to the total size of submitted resources. Here, the total size of submitted resources includes the total size of resources submitted through all resource submission methods.

[0062] It should be understood that features B1 to B2, as in the example of feature A1, can be further divided into multiple sub-features based on the time window, which will not be elaborated further here. It should be understood that the specific size of each time window corresponding to each feature in features B1 to B2 and the total number of time windows can be set according to the actual situation and specific needs, and this disclosure does not impose any restrictions on this.

[0063] In one embodiment, the probability that a target user will withdraw the submitted resources after submitting them through a target submission activity can be determined based on the resource withdrawal characteristics, and whether the target user meets the conditions for participating in the target submission activity can be determined based on the probability. Further, as an example, the target user's preference for the target resource submission method can be determined based on the submission method preference characteristics, and the probability that the target user will withdraw the submitted resources after submitting them through a target submission activity can be determined based on the resource withdrawal characteristics; then, based on the preference level and the probability, it can be determined whether the target user meets the conditions for participating in the target submission activity. The following will combine... Figure 2 The exemplary embodiment will now be described in detail.

[0064] In another embodiment, the acquired resource submission features can be input into a pre-trained machine learning model to obtain a classification result output by the machine learning model indicating whether the target user meets the conditions for participating in the target submission activity.

[0065] It should be understood that the machine learning model can be trained in advance using training samples.

[0066] In step S103, if it is determined that the target user meets the conditions, the target submission activity is recommended to the target user to encourage the target user to submit the resource through the target resource submission method.

[0067] As an example, information about the target submission activity can be displayed to the target user on the interface where the target user selects from multiple available resource submission methods. For instance, the beneficial results that can be achieved by submitting the resource through the target submission activity can be shown to the target user. For example, such as... Figure 3 As shown, the first resource delivery method is the target resource delivery method, which can display promotional copy information of the current delivery activity of the first resource delivery method (i.e., the target delivery activity) to the target users.

[0068] Furthermore, as an example, if it is determined that the target user does not meet the aforementioned conditions, the target submission activity will not be recommended or provided to the target user. In other words, the target user can submit resources through the target resource submission method, but cannot participate in the target submission activity.

[0069] Figure 2 A flowchart illustrating a method for determining whether a target user meets the conditions for participating in a target resource submission activity according to an exemplary embodiment of the present disclosure.

[0070] Reference Figure 2 In step S201, the degree of preference of the target user for the target resource delivery method is determined based on the delivery method preference features, and it is determined whether the degree of preference meets the first preset condition.

[0071] In step S202, based on the resource withdrawal characteristics, the probability that the target user will withdraw the resource after submitting it through the target submission activity is determined, and it is determined whether the probability satisfies the second preset condition.

[0072] It should be understood that this disclosure does not limit the execution order of steps S201 and S202. As an example, step S202 can be executed when it is determined in step S201 that the preference level meets a first preset condition. As another example, steps S201 and S202 can be executed in parallel. As yet another example, step S201 can be executed when it is determined in step S202 that the probability meets a second preset condition.

[0073] In step S203, if the preference level meets the first preset condition and the probability meets the second preset condition, it is determined that the target user meets the conditions for participating in the target submission activity.

[0074] As an example, the weighted sum of the various features included in the resource withdrawal feature can be used to obtain the probability that the target user will withdraw the resource after submitting it through the target submission activity. It should be understood that the weights of each feature can be set according to the actual situation and specific needs.

[0075] As an example, when the resource withdrawal feature includes multiple sub-features based on a time window, the weighted sum of each sub-feature can be performed to obtain the probability that the target user will withdraw the resource after submitting it through the target submission activity. It should be understood that the weight of each sub-feature can be set according to the actual situation and specific needs; for example, the smaller the time window corresponding to a sub-feature, the greater its weight.

[0076] As an example, the weighted sum of the various features included in the submission method preference feature can be used to obtain the target user's preference for the target resource submission method. It should be understood that the weights of each feature can be set according to actual circumstances and specific needs.

[0077] As an example, when the features included in the submission method preference feature are further divided into multiple sub-features based on a time window, the weighted sum of each sub-feature of the features included in the submission method preference feature can be performed to obtain the target user's preference for the target resource submission method. It should be understood that the weight of each sub-feature can be set according to the actual situation and specific needs; for example, the smaller the size of the time window corresponding to a sub-feature, the greater the weight of that sub-feature.

[0078] As an example, the first preset condition may include: greater than a first threshold and less than a second threshold. As an example, the second preset condition may include: less than a third threshold. As an example, if the preference level is greater than the first threshold and less than the second threshold, and the probability is less than the third threshold, it can be determined that the target user meets the conditions for participating in the target submission activity.

[0079] It should be understood that the specific values ​​of the first, second, and third thresholds can be set according to the actual situation and specific needs. For example, by reasonably setting the specific values ​​of the first and second thresholds, users who are not heavily biased towards the target resource submission method and are not heavily biased towards other submission methods can be selected as willing to participate in the target submission activity. For example, by reasonably setting the specific value of the third threshold, users who do not frequently withdraw submitted resources can be selected.

[0080] As an example, as shown in Table 1, if there are only two resource submission methods, namely the first resource submission method and the second resource submission method, the target user's preference for the second resource submission method can be determined based on the frequency of resource submissions made by the target user through the second resource submission method.

[0081] Table 1 Resource Submission Method Preferences

[0082]

[0083] As an example, the first threshold can be set to 0.3 and the second threshold to 0.7. In other words, the target user must be at least a "swing user" before a submission activity can be recommended to them. Specifically, when the second resource submission method is the target resource submission method, the probability of users who are "loyal users of the first resource submission method" and "heavy users of the first resource submission method" using the second resource submission method is relatively low. Users in the "heavy users of the second resource submission method" and "loyal users of the second resource submission method" will use the second resource submission method to submit resources even if they are not offered submission activities using the second resource submission method.

[0084] According to an exemplary embodiment of this disclosure, considering that resource submission activities have a budget, if multiple users who submitted resources through the activity withdraw their submissions, the utilization rate of the activity's budget will decrease, thereby reducing the promotion and effectiveness of the resource submission method. Therefore, an exemplary embodiment of this disclosure proposes: based on the historical statistics of users withdrawing submitted resources and their preferences for resource submission methods, determining whether a user meets the conditions for participating in a resource submission activity, and only recommending the activity to the user if the conditions are met, thereby encouraging users to submit resources through the resource submission method. This improves the utilization rate of the activity's budget and the success rate of promotion, thus maximizing the promotional effect of the activity.

[0085] Figure 4 A structural block diagram of a recommended apparatus according to an exemplary embodiment of the present disclosure is shown.

[0086] like Figure 4 As shown, the recommendation device 10 according to an exemplary embodiment of the present disclosure includes: a feature acquisition unit 101, a determination unit 102, and a recommendation unit 103.

[0087] Specifically, the feature acquisition unit 101 is configured to acquire the resource submission features of the target user.

[0088] The resource submission features include: resource withdrawal features used to characterize the historical statistics of the target user withdrawing submitted resources.

[0089] As an example, the resource delivery feature may further include: a delivery method preference feature used to characterize the target user's preference for the resource delivery method.

[0090] The determining unit 102 is configured to determine, based on the acquired resource delivery characteristics, whether the target user meets the conditions for participating in the target delivery activity of the target resource delivery method.

[0091] The target submission activity is an activity designed to encourage users to submit resources through the target resource submission method.

[0092] The recommendation unit 103 is configured to recommend the target submission activity to the target user when it is determined that the target user meets the conditions.

[0093] As an example, the determining unit 102 may be configured to: determine the target user's preference level for the target resource submission method based on the submission method preference features, and determine whether the preference level meets a first preset condition; determine the probability that the target user will withdraw the resource after submitting it through the target submission activity based on the resource withdrawal features, and determine whether the probability meets a second preset condition; and determine that the target user meets the conditions for participating in the target submission activity if the preference level meets the first preset condition and the probability meets the second preset condition.

[0094] As an example, the resource withdrawal feature may include at least one of the following features: the proportion of the target user's total frequency of resource withdrawals in history to the total frequency of resource submissions; the proportion of the target user's total size of withdrawn resources in history to the total size of submitted resources; the proportion of the target user's total frequency of withdrawing resources after submitting resources through a submission activity in history to the total frequency of resources submitted through a submission activity; the proportion of the target user's total size of withdrawn resources after submitting resources through a submission activity in history to the total size of resources submitted through a submission activity; the proportion of the target user's total frequency of withdrawing resources after submitting resources through the target resource submission method in history to the total frequency of resources submitted through the target resource submission method. The percentage of the total frequency of resource submissions via the target resource submission method; the percentage of the total size of resources submitted and withdrawn by the target user through the target resource submission method in history, relative to the total size of resources submitted through the target resource submission method; the percentage of the total frequency of resources submitted and withdrawn by the target user through the target resource submission method in history, relative to the total frequency of resources submitted through the target resource submission method; and the percentage of the total size of resources submitted and withdrawn by the target user through the target resource submission method in history, relative to the total size of resources submitted through the target resource submission method.

[0095] As an example, the determining unit 102 may be configured to: perform a weighted summation of the various features included in the resource withdrawal feature to obtain the probability that the target user withdraws the resource after submitting it through the target submission activity.

[0096] As an example, the submission method preference feature may include at least one of the following features: the proportion of the total frequency of the target user's historical resource submissions via the target resource submission method to the total frequency of resource submissions; the proportion of the total size of the target user's historical resource submissions via the target resource submission method to the total size of resource submissions.

[0097] As an example, the determining unit 102 may be configured to: perform a weighted summation of each feature included in the submission method preference feature to obtain the target user's preference degree for the target resource submission method.

[0098] As an example, the first preset condition may include: greater than a first threshold and less than a second threshold; the second preset condition may include: less than a third threshold.

[0099] As an example, the recommendation unit 103 may be configured to display information about the target submission activity to the target user in an interface for the target user to select the current resource submission method from a variety of available resource submission methods.

[0100] As an example, the determining unit 102 can be configured to: input the acquired resource submission features into a pre-trained machine learning model to obtain a classification result output by the machine learning model indicating whether the target user meets the conditions for participating in the target submission activity.

[0101] Regarding the recommended device 10 in the above embodiments, the specific manner in which each unit performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated here.

[0102] Furthermore, it should be understood that the various units in the recommended apparatus 10 according to the exemplary embodiments of this disclosure may be implemented as hardware components and / or software components. Those skilled in the art, based on the processes performed by the defined various units, may implement the various units, for example, using a field-programmable gate array (FPGA) or an application-specific integrated circuit (ASIC).

[0103] Figure 5 A structural block diagram of an electronic device according to an exemplary embodiment of the present disclosure is shown. (Refer to...) Figure 5 The electronic device 20 includes at least one memory 201 and at least one processor 202, wherein the at least one memory 201 stores a set of computer-executable instructions, which, when executed by the at least one processor 202, perform the recommended method as described in the exemplary embodiments above.

[0104] As an example, electronic device 20 may be a PC, tablet, personal digital assistant, smartphone, or other device capable of executing the aforementioned set of instructions. Here, electronic device 20 is not necessarily a single electronic device; it may be any collection of devices or circuits capable of executing the aforementioned instructions (or instruction sets) individually or in combination. Electronic device 20 may also be part of an integrated control system or system manager, or may be configured to interconnect with a portable electronic device locally or remotely (e.g., via wireless transmission) through an interface.

[0105] In electronic device 20, processor 202 may include a central processing unit (CPU), a graphics processing unit (GPU), a programmable logic device, a dedicated processor system, a microcontroller, or a microprocessor. By way of example and not limitation, processor 202 may also include analog processors, digital processors, microprocessors, multi-core processors, processor arrays, network processors, etc.

[0106] The processor 202 can execute instructions or code stored in the memory 201, which can also store data. Instructions and data can also be sent and received over a network via a network interface device, which can employ any known transmission protocol.

[0107] The memory 201 may be integrated with the processor 202, for example, by placing RAM or flash memory within an integrated circuit microprocessor. Alternatively, the memory 201 may include a separate device, such as an external disk drive, a storage array, or other storage device usable by any database system. The memory 201 and the processor 202 may be operatively coupled, or may communicate with each other, for example, via I / O ports, network connections, etc., enabling the processor 202 to read files stored in the memory.

[0108] In addition, electronic device 20 may also include a video display (such as a liquid crystal display) and a user interaction interface (such as a keyboard, mouse, touch input device, etc.). All components of electronic device 20 may be interconnected via a bus and / or network.

[0109] According to exemplary embodiments of the present disclosure, a computer-readable storage medium storing instructions may also be provided, wherein when the instructions are executed by at least one processor, they cause at least one processor to perform the recommended method as described in the exemplary embodiments above. Examples of computer-readable storage media herein include: read-only memory (ROM), random access programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, non-volatile memory, CD-ROM, CD-R, CD+R, CD-RW, CD+RW, DVD-ROM, DVD-R, DVD+R, DVD-RW, DVD+RW, DVD-RAM, BD-ROM, BD-R, BD-R, BD-R The computer program can be stored in a computer-readable storage medium such as a BD-RE, Blu-ray or optical disc storage device, hard disk drive (HDD), solid-state drive (SSD), card storage (such as a multimedia card, secure digital (SD) card, or ultra-fast digital (XD) card), magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid-state drive, or any other device configured to store a computer program and any associated data, data files, and data structures in a non-transitory manner and to provide the computer program and any associated data, data files, and data structures to a processor or computer so that the processor or computer can execute the computer program. The computer program in the aforementioned computer-readable storage medium can run in an environment deployed in computer devices such as clients, hosts, agent devices, servers, etc. Furthermore, in one example, the computer program and any associated data, data files, and data structures are distributed across a networked computer system, such that the computer program and any associated data, data files, and data structures are stored, accessed, and executed in a distributed manner through one or more processors or computers.

[0110] According to exemplary embodiments of the present disclosure, a computer program product may also be provided, wherein the instructions in the computer program product are executable by at least one processor to perform the recommended method as described in the exemplary embodiments above.

[0111] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0112] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A recommendation method, characterized in that, The recommendation method includes: Obtain the resource submission characteristics of the target user, wherein the resource submission characteristics include: resource withdrawal characteristics, which characterize the historical statistics of the target user withdrawing submitted resources, and submission method preference characteristics, which characterize the target user's preference for resource submission methods; Based on the acquired resource submission characteristics, it is determined whether the target user meets the conditions for participating in the target submission activity of the target resource submission method, wherein the target submission activity is an activity used to promote users to submit resources through the target resource submission method; If the target user is determined to meet the conditions, the target submission activity is recommended to the target user. The step of determining whether the target user meets the conditions for participating in the target resource submission activity based on the acquired resource submission characteristics includes: Based on the submission method preference features, determine the target user's preference level for the target resource submission method, and determine whether the preference level meets the first preset condition; Based on the resource withdrawal characteristics, determine the probability that the target user will withdraw the resource after submitting it through the target submission activity, and determine whether the probability satisfies the second preset condition; If the preference level meets the first preset condition and the probability meets the second preset condition, it is determined that the target user meets the conditions for participating in the target submission activity.

2. The recommended method according to claim 1, characterized in that, The resource withdrawal feature includes at least one of the following features: The proportion of the target user's total frequency of resource withdrawals to the total frequency of resource submissions in history; The proportion of the total size of resources withdrawn by the target user in history to the total size of resources submitted. The proportion of the total frequency of resource submissions and subsequent withdrawals by the target user through submission activities in history to the total frequency of resource submissions through submission activities; The proportion of the total amount of resources submitted and then withdrawn by the target user through submission activities in history to the total amount of resources submitted through submission activities. The percentage of the total frequency of resource submissions and subsequent withdrawals by the target user through the target resource submission method in the history of the target user, relative to the total frequency of resource submissions through the target resource submission method. The total size of resources submitted and withdrawn by the target user through the target resource submission method in history, as a percentage of the total size of resources submitted through the target resource submission method; The percentage of the total frequency of resource submissions and subsequent withdrawals by the target user through the target resource submission method in the past, relative to the total frequency of resource submissions through the target resource submission method. The percentage of the total amount of resources submitted and subsequently withdrawn by the target user through the target resource submission method in the past, relative to the total amount of resources submitted through the target resource submission method.

3. The recommended method according to claim 2, characterized in that, The steps for determining the probability that a target user will withdraw resources after submitting them through the target submission activity based on the resource withdrawal characteristics include: The probability of a target user withdrawing resources after submitting them through the target submission activity is obtained by weighted summation of the various features included in the resource withdrawal feature.

4. The recommended method according to claim 1, characterized in that, The submission method preference features include at least one of the following features: The proportion of the total frequency of resource submissions by the target user in history through the target resource submission method to the total frequency of resource submissions; The proportion of the total size of resources submitted by the target user in history through the target resource submission method to the total size of submitted resources.

5. The recommended method according to claim 4, characterized in that, The steps for determining the target user's preference for the target resource delivery method based on the delivery method preference features include: The target user's preference for the target resource delivery method is obtained by weighted summation of each feature included in the delivery method preference feature.

6. The recommendation method according to claim 1, characterized in that, The first preset condition includes: greater than a first threshold and less than a second threshold; The second preset condition includes: less than the third threshold.

7. The recommended method according to claim 1, characterized in that, The steps of recommending the target delivery activity to the target user include: In the interface for the target user to select the resource submission method from a variety of available resource submission methods, information about the target submission activity is displayed to the target user.

8. The recommended method according to claim 1, characterized in that, The steps for determining whether the target user meets the conditions for participating in the target resource submission activity based on the acquired resource submission characteristics include: The acquired resource submission features are input into a pre-trained machine learning model to obtain a classification result output by the machine learning model indicating whether the target user meets the conditions for participating in the target submission activity.

9. A recommended device, characterized in that, The recommendation device includes: The feature acquisition unit is configured to acquire the resource submission features of the target user, wherein the resource submission features include: resource withdrawal features for characterizing the historical statistics of the target user withdrawing submitted resources and submission method preference features for characterizing the target user's preference for resource submission methods; The determining unit is configured to determine, based on the acquired resource submission characteristics, whether the target user meets the conditions for participating in the target submission activity of the target resource submission method, wherein the target submission activity is an activity used to promote the user to submit resources through the target resource submission method; The recommendation unit is configured to recommend the target submission activity to the target user when it is determined that the target user meets the conditions; The determining unit is further configured to: determine the target user's preference level for the target resource submission method based on the submission method preference features, and determine whether the preference level meets a first preset condition; determine the probability that the target user will withdraw the resource after submitting it through the target submission activity based on the resource withdrawal features, and determine whether the probability meets a second preset condition; and determine that the target user meets the conditions for participating in the target submission activity if the preference level meets the first preset condition and the probability meets the second preset condition.

10. An electronic device, characterized in that, include: At least one processor; At least one memory that stores computer-executable instructions. Wherein, when the computer-executable instructions are executed by the at least one processor, they cause the at least one processor to perform the recommended method as described in any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by at least one processor, the at least one processor causes the processor to perform the recommended method as described in any one of claims 1 to 8.

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