CN111586162A - Information pushing method and system - Google Patents
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Abstract
The embodiment of the specification discloses an information pushing method and system. The method can comprise the steps of determining characteristic information of a target user, wherein the characteristic information reflects at least one of a consumption amount level of the user in a target field, a consumption activity level of the user in the target field and historical behavior habits of the user; acquiring a plurality of resource rights, wherein the resource rights come from one or more service parties and comprise user rights and service types provided by the service parties; processing the characteristic information of the target user and a plurality of resource rights and interests to determine at least one target resource right and interest matched with the target user; pushing the at least one target resource interest to a user terminal associated with a target user to cause a consumption interface of the target domain on the user terminal to expose the at least one target resource interest.
Description
Technical Field
One or more embodiments of the present disclosure relate to the field of computer technologies, and in particular, to an information pushing method and system.
Background
Currently, more and more network platforms utilize a push system to push certain user rights (e.g., red envelope, coupon, allowance, etc.) to users, so as to increase platform user usage or platform revenue. In practical application, the user rights and interests which are more matched with the user are pushed, and the purposes can be better realized on the premise of minimizing the user rights and interests cost.
Therefore, there is a need to provide an information pushing method and system more adaptive to users, so as to increase the user usage of the network platform.
Disclosure of Invention
One aspect of the present specification provides an information push method. The method comprises the following steps: determining characteristic information of a target user, wherein the characteristic information reflects at least one of a consumption amount level of the user in a target field, a consumption activity level of the user in the target field and historical behavior habits of the user; acquiring a plurality of resource rights, wherein the resource rights come from one or more service parties and comprise user rights and service types provided by the service parties; processing the characteristic information of the target user and a plurality of resource rights and interests to determine at least one target resource right and interest matched with the target user; pushing the at least one target resource interest to a user terminal associated with a target user to cause a consumption interface of the target domain on the user terminal to expose the at least one target resource interest.
Another aspect of the specification provides an information push system. The system comprises: the determining module is used for determining characteristic information of a target user, wherein the characteristic information reflects at least one of a consumption amount level of the user in a target field, a consumption activity level of the user in the target field and historical behavior habits of the user; the acquisition module is used for acquiring a plurality of resource rights, wherein the resource rights come from one or more service parties and comprise user rights and service types provided by the service parties; the processing module is used for processing the characteristic information of the target user and a plurality of resource rights and interests so as to determine at least one target resource right and interest matched with the target user; the pushing module is used for pushing the at least one target resource right to a user terminal associated with a target user so that a consumption interface of the target field on the user terminal shows the at least one target resource right.
Another aspect of the present specification provides an information pushing apparatus comprising at least one storage medium and at least one processor, the storage medium configured to store computer instructions; the processor is used for executing the information pushing method in any embodiment of the specification.
Another aspect of the present specification provides a computer-readable storage medium, where the storage medium stores computer instructions, and when the computer reads the computer instructions in the storage medium, the computer executes an information pushing method according to any one of the embodiments of the present specification.
Drawings
FIG. 1 is a diagram of an application scenario of an information push system shown in accordance with some embodiments of the present description;
FIG. 2 is an exemplary flow diagram of an information push method shown in accordance with some embodiments of the present description;
FIG. 3 is a schematic diagram of a structure of a matching model shown in accordance with some embodiments of the present description;
fig. 4 is a block diagram of an information push system shown in accordance with some embodiments of the present description.
Detailed Description
In order to more clearly illustrate the technical solution of one or more embodiments of the present invention, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, one or more embodiments of the present description can also be applied to other similar scenarios according to these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.
It should be understood that "system", "device", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.
As used in one or more embodiments of the present specification and in the claims, the terms "a," "an," and/or "the" are not intended to be inclusive of the plural form as long as the context clearly indicates the exception. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.
Flow diagrams are used in one or more embodiments of the present description to illustrate the operations performed by a system in accordance with one or more embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.
Fig. 1 is an application scenario diagram of an information push system shown in accordance with some embodiments of the present description.
As shown in fig. 1, the information push system 100 may include a server 110, a network 120, a user terminal 130, and a storage device 140.
The server 110 may be used to process information and/or data associated with a push target resource entitlement to perform one or more of the functions disclosed in this specification. In some embodiments, the server 110 may be configured to determine characteristic information of the target user, the characteristic information reflecting at least one of a consumption amount level of the user in the target domain, a consumption activity level in the target domain, and a historical behavior habit of the user. In some embodiments, the server 110 may obtain a plurality of resource benefits from one or more business parties, including user benefits and business types provided by the business parties. In some embodiments, the server 110 may process the characteristic information of the target user and the plurality of resource benefits to determine at least one target resource benefit that matches the target user. In some embodiments, the server 110 may push the at least one targeted resource interest to a user terminal associated with the targeted user such that a consumption interface of the targeted domain on the user terminal exposes the at least one targeted resource interest.
In some embodiments, the server 110 may be a single server or a group of servers. The set of servers can be centralized or distributed (e.g., the servers 110 can be a distributed system), can be dedicated, or can be serviced by other devices or systems at the same time. In some embodiments, the server 110 may be regional or remote. In some embodiments, the server 110 may be implemented on a cloud platform, or provided in a virtual manner. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-tiered cloud, and the like, or any combination thereof.
In some embodiments, the server 110 may contain one or more sub-processing devices (e.g., single-core processing devices or multi-core processing devices). By way of example only, the server 110 may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction Processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a programmable logic circuit (PLD), a controller, a micro-controller unit, a Reduced Instruction Set Computer (RISC), a microprocessor, or the like, or any combination thereof.
The network 120 may connect the components of the information push system 100 and/or connect the system to external resource components. The network 120 enables communication between the components of the information push system 100 and with other components outside the information push system 100 to facilitate the exchange of data and/or information. In some embodiments, the network 120 may be any one or more of a wired network or a wireless network. For example, network 120 may include a cable network, a fiber optic network, a telecommunications network, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Public Switched Telephone Network (PSTN), a bluetooth network, a ZigBee network (ZigBee), Near Field Communication (NFC), an in-device bus, an in-device line, a cable connection, and the like, or any combination thereof. The network connection between the parts of the information push system 100 may be in one manner or in multiple manners. In some embodiments, the network may be a point-to-point, shared, centralized, etc. variety of topologies or a combination of topologies. In some embodiments, network 120 may include one or more network access points. For example, the network 120 may include wired or wireless network access points, such as base stations and/or network switching points 120-1, 120-2, …, through which one or more components of the information push system 100 may connect to the network 120 to exchange data and/or information.
The technical scheme disclosed by the embodiment of the specification can be applied to the target resource rights and interests pushing scenes in various application platforms. The application platform can be a platform for providing specific services for vast consumers, such as online shopping, telephone charge, financial services, data query services and the like. In some embodiments, the application platform or the goods domain and the service domain provided by the application platform may be referred to as a target domain. By way of example only, in some scenarios, an application platform (or referred to as a target domain) may issue certain user benefits (including but not limited to cash redpacks, coupons, benefits) to users consuming within its platform, thereby pulling consumption. For example, an application platform with an online recharging service pushes a cash red envelope to a user, so that the user can deduct a part of mobile phone charges by clicking the obtained cash red envelope. For another example, the application platform with financial services pushes a coupon to the user, so that the user can purchase a financial product by clicking the obtained coupon. For another example, the application platform with mobile payment service pushes allowance to the user, so that the user can make online or offline shopping by using the obtained shopping allowance. Issuing (or pushing) appropriate user rights to a particular user can lead to a greater contribution to the overall revenue of the platform. How to determine the user rights and interests pushed to the user becomes a problem to be solved urgently.
In some embodiments, when the application platform pushes the target user's rights to the target user, it may acquire the feature information of the target user (e.g., the consumption amount level, the consumption activity level of the target user, and the historical behavior habit of the target user), and then match the corresponding amount of user's rights to the target user (including but not limited to cash red envelope, coupon, allowance) by using the matching model and push the target user's rights to the target user. However, the following characteristics may exist in this pushing manner: the application platform occasionally issues user interests to the target users, and cannot stably maintain the target users for a long time, and the user interests are issued by funds in the application platform for a long time, so that a large cost is consumed. In some embodiments, the application platform can obtain resource rights from other business parties, and push the resource rights to users consuming in the application platform, so that on one hand, the overhead in the platform can be saved, and on the other hand, the exposure rate of business contents of the business parties can be increased through the issuance of the resource rights, thereby helping the business parties to complete certain business goals. The business party here may be a subject that provides other services or goods independent of the application platform. For example, the business party may be an insurance company, the business content may be various types of insurance, and the business goal may be to increase the spread of various types of insurance. In the embodiments, if the resource right which is more matched with the service content and the right amount can be recommended to the user based on the personal characteristics of the user, more benefits can be brought to the platform and the service party. Therefore, a matching algorithm is needed to be provided, when resource interest recommendation is performed, in addition to considering the matching degree between the interest amount and the user, the matching algorithm also matches the service types related to the resource interest, and further matches a more appropriate resource interest for the user.
Fig. 2 is an exemplary flow diagram of an information push method shown in accordance with some embodiments of the present description.
In some embodiments, flow 200 may be performed by processing logic that may comprise hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (instructions run on a processing device to perform hardware simulation), etc., or any combination thereof. One or more operations of the information pushing method 200 shown in fig. 2 may be implemented by the information pushing system 100 shown in fig. 1. For example, the process 200 may be stored in the storage device 140 in the form of instructions and executed and/or invoked by the server 110.
In some embodiments, the target user may be a user that is about to consume in the target domain. The characteristic information may reflect at least one of a consumption amount level of the user in the target field, a consumption activity level in the target field, and a historical behavior habit of the user. In some embodiments, the target domain may be a commodity domain or a service domain purchased by the target user, or an application platform providing such commodities or services. For example, an XX flagship store of an online charging fee and an e-commerce.
In some embodiments, the spending amount hierarchy may be a spending amount level divided by a spending amount, for example, a spending amount for a charging fee may be divided into 5 levels: less than 10 yuan, 10 yuan to 30 yuan, 30 yuan to 100 yuan, 100 yuan to 300 yuan, 300 yuan above. In some embodiments, the consumption activity level may include a level divided by the number of consumption of the user over a period of time (e.g., 3 months, 6 months, or 1 year), for example, may be divided into 6 levels: loyalty (e.g., 15 times consumed in 3 months), active (e.g., 10 times consumed in 3 months), low activity (e.g., 5 times consumed in 3 months), easy to lose (e.g., 3 times consumed in 3 months), lost (e.g., 1 time consumed in the first month of 3 months, not consumed in the last two months), new users (e.g., 0 times consumed in 3 months). The consumption activity level may be further divided into other levels, such as level a, level B, level C, level D, level E, and level F, which is not limited in this application. In some embodiments, historical behavioral habits may include a user's preference for certain goods or services over a past period of time (e.g., 3 months, 6 months, or 1 year). In some embodiments, the preferences may be divided into categories, the preferences or behavior habit categories of which are determined based on the user's historical behavior. For example, the user often buys fresh food products in the past 1 year, and the historical behavior habit may be hobby cooking.
In some embodiments, the spending amount tier of the target user may be determined from the historical spending amounts. Specifically, historical spending amounts of a plurality of users in the target field can be obtained, and the historical spending amounts of the plurality of users are clustered to obtain a plurality of spending amount levels. The plurality of users may be other plurality of users outside the target user. The historical spending amount may be the amount of each spending over a historical period of time, e.g., a top-up of 50 dollars for the past month and a purchase of xx items of 100 dollars. In some embodiments, the clustering methods may include K-Means clustering algorithms, mean shift clustering algorithms, noisy density-based spatial clustering algorithms, expectation maximization clustering algorithms for Gaussian mixture models, and agglomerative hierarchical clustering algorithms. For example, the consumption amount levels obtained by clustering are 10 yuan or less, 10 yuan to 30 yuan, 30 yuan to 100 yuan, 100 yuan to 150 yuan, and 150 yuan or more. In some embodiments, the historical spending amount of the target user in the target area can be obtained, so that the spending amount level where the historical spending amount of the target user is located in the plurality of spending amount levels is determined. Specifically, for the distribution quantity of the historical expense amounts of the target user in the target field in the past period (e.g., 3 months, 6 months, or 1 year), the multiple expense amount levels where the historical expense amounts with the largest quantity are located may be selected as the expense amount levels of the target user. For example, if the target user has consumed 20 yuan 3 times, 80 yuan 7 times, and 150 yuan 1 time in the target field in the past 3 months, the consumption amount level of the target user is 30 to 100 yuan. As another example, the average spending amount is obtained by averaging the spending amounts of the target users in a historical period, and the spending amount level of the average spending amount in the plurality of spending amount levels is determined.
In some embodiments, the consumption activity level of the target user may be determined according to historical consumption frequency. Specifically, the historical consumption frequency of a plurality of users in the target field can be obtained, and then the historical consumption frequency of the plurality of users can be clustered to obtain a plurality of consumption activity levels. The historical consumption frequency may be the number of consumption over a historical period of time, e.g., 4 charges in the past month, 1 purchase of xx items in the past week. For the content of the clustering method, reference may be made to the above description, which is not repeated herein. For example, the consumption amount tiers obtained by clustering are loyalty (e.g., 12-15 times consumed in the past 3 months), active (e.g., 7-11 times consumed in the past 3 months), low-activity (e.g., 4-6 times consumed in the past 3 months), easily-churned (e.g., 2-3 times consumed in the past 3 months), churned (e.g., 1 time consumed in the past 3 months), new users (e.g., 0 times consumed in the past 3 months). In some embodiments, the historical consumption frequency of the target user in the target domain may also be obtained, so as to determine the consumption activity level where the historical consumption frequency of the target user is located in the plurality of consumption activity levels. For example, if the historical consumption frequency of the target user in the target domain is 5 times in the last 3 months, the consumption activity level of the target user is "low activity".
In some embodiments, historical behavior habits of the target user may be determined from historical behavior data. Specifically, historical behavior data of the target user may be obtained, where the historical behavior data at least reflects online consumption behavior information of the target user. The historical behavior data of the target user may not be limited to the historical behavior data of the target user on the current application platform, and may also be obtained from other application platforms through the data transmission port, for example, a certain e-commerce platform obtains the historical behavior data of the target user on a certain takeout platform through the data transmission port. The historical behavior data may be the type of goods or services the user has frequently purchased over a period of time (e.g., 3 months, 6 months, or 1 year), the amount of money consumed, the time of purchase, the manner of payment, etc.
In some embodiments, the trained behavior recognition model may be utilized to process historical behavior data to determine historical behavior habits of the target user. Specifically, the historical behavior data of the target user may be input into the trained behavior recognition model, and the historical behavior habit of the target user may be output. For example, the trained behavior recognition model outputs the historical behavior habits of the target user as "favoring financing" or "favoring tourism".
In some embodiments, the behavior recognition model may be derived by training an initial model. Specifically, the method may include obtaining a training sample, and then training an initial model based on the training sample and a labeling result to obtain a behavior recognition model. In some embodiments, the training samples may include sample historical behavior data and their corresponding historical behavior habits. The training samples may be historical behavior data and historical behavior habits of a plurality of users. In some embodiments, the initial model may comprise a classification model. Such as decision trees, bayesian classification, random forests, support vector machines, neural networks, and the like.
In some embodiments, the determining module 402 may determine the characteristic information of the target user according to the historical consumption of the target user.
By determining the characteristic information of the target user, more detailed personalized characteristic information of the target user can be obtained, so that more suitable target resource rights are pushed to the target user in the subsequent steps.
Multiple resource rights may be provided by one or more of the business parties. The resource rights may include user rights provided by the business party as well as the type of business. In some embodiments, a business party may include an enterprise or merchant that provides goods or services, such as a bank, a farm and sideline, a hotel, a theater, and so forth. In some embodiments, the user benefits may include, but are not limited to, cash redpacks, coupons, benefits, discounts. In some embodiments, the traffic type may be determined based on the traffic class of the traffic party. For example, the business type of the bank can be financial, the business type of the agricultural and sideline enterprises can be life, and the business type of the hotel can be travel. In some embodiments, the resource interests may also include business tasks, such as opening XX members, opening bank cards online, paying attention to the number of lives of agricultural and sideline enterprises.
In order to increase the promotion force of the product or service of the business party, the business party can choose to carry out propaganda on the application platform and provide corresponding cost or rights and interests for the application platform. In some embodiments, the obtaining module 404 may obtain a plurality of resource benefits of one or more business parties collaborating with the application platform.
By obtaining multiple resource benefits of one or more business parties, more appropriate target resource benefits can be matched for target users in subsequent steps.
In some embodiments, the target resource equity may be a resource equity that has a higher degree of match (e.g., match score) with the target user.
In some embodiments, the feature information of the target user may be respectively matched with the service type and the user interest in the resource interest, so as to obtain a first matching score and a second matching score, and a final matching score of the target user and the resource interest is determined based on the first matching score and the second matching score.
In some embodiments, the feature information of the target user and the plurality of resource benefits may be processed using a matching model to determine at least one target resource benefit that matches the target user. Specifically, the feature information of the target user may be combined with each of the plurality of resource benefits, respectively, to obtain a plurality of input feature pairs. Because there are multiple resource benefits, the feature pair of the target user can be a permutation combination of the feature information of the target user and the multiple resource benefits. For example, there are 5 resource rights, and 5 input feature pairs can be obtained by combining the feature information of the target user and the 5 resource rights, respectively.
In some embodiments, multiple input feature pairs may be processed in sequence using a trained matching model to arrive at a matching score for a target user with multiple resource benefits. Specifically, a plurality of input feature pairs may be input into the trained matching model, and matching scores of the target user and a plurality of resource rights may be output. In some embodiments, the match score may be a percentile, e.g., 0-100 points. In some embodiments, the match score may be a tenth score, e.g., 0-10. In some embodiments, the match score may be a ranking system, such as 0-10. The training process of the matching model can be referred to fig. 3 and the description thereof, which are not described herein.
In some embodiments, the resource right whose matching score satisfies a preset condition may be determined as the target resource right. In some embodiments, the preset conditions may include the match score being the largest, the match score being sorted by size into the first three, or being greater than the match score threshold. For example, when the preset condition is that the matching scores are sorted in the top three according to the size, the matching scores can be sorted from large to small, and the top three resource rights can be determined as the three target resource rights.
In some embodiments, the processing module 406 may process the characteristic information of the target user and the plurality of resource benefits to determine at least one target resource benefit that matches the target user.
By matching the characteristic information of the target user and the plurality of resource rights and interests, the interested resource rights and interests of the target user can be matched for the target user according to the characteristic information of the target user, so that the target user can use the user rights and interests in the resource rights and interests, and the service party of the resource rights and interests can realize the popularization effect.
In some embodiments, the user terminal associated with the target user may be the user terminal currently being used by the target user. In some embodiments, the consumption interface of the target domain may be a settlement interface for the target user to purchase goods or services on the user terminal. In some embodiments, there are multiple ways to present at least one target resource interest, and the target user may select among the presented at least one resource interest. For example, the target resource interests may be selected by popping up a small window on the settlement interface to reveal at least one target resource interest, which the target user can slide left or right or up or down. As another example, the at least one target resource interest may also be presented by displaying an "offer" word on the settlement interface, which the target user may click to pop up a list of the at least one target resource interest, from which the user target may select the target resource interest. In some embodiments, the consumption interface of the target domain may be a presentation interface of goods or services. For example, by displaying the target resource interest corresponding to each commodity or service on the display interface of the commodity or service, the user target can select the commodity or service of interest from the target resource interests, that is, select the corresponding at least one target resource interest.
In some embodiments, the resource interests may also include business tasks. After the user selects the target resource right, the user also needs to complete the service task of the service party corresponding to the target resource right. And after the business task of the business party is completed, the corresponding user rights and interests can be obtained, and the user rights and interests in the target resource rights and interests can be used for deducting at least part of consumption money generated by the target user consuming in the consumption interface of the target field. For more contents of the service party and the service task, reference may be made to the description of step 204, which is not described herein again.
The process of exposing and using at least one target resource interest is set forth below in one specific example: for example, the target user charges the telephone fee at a charging center of a certain e-commerce platform, the charging interface of the charging center on the user terminal charges the telephone fee shows the charging amount (e.g. 50 yuan), the preferential price (e.g. 41.10 yuan) and the service task of the preferential price (e.g. opening XX insurance member, namely, sharing the preferential price), a 'getting' control is displayed below the service task of the preferential price, the target user clicks the 'getting' control page to jump to the XX insurance life number and opens the member, and the page returns to the charging interface and automatically deducts part of the telephone fee.
In some embodiments, the push module 408 may push the at least one targeted resource interest to a user terminal associated with the targeted user such that a consumption interface of the targeted domain on the user terminal exposes the at least one targeted resource interest.
By pushing at least one target resource right to a user terminal associated with a target user, the target user can quickly select the user right corresponding to the interested service type, and immediately use the user right when consuming on a consumption interface in the target field, and the service party can successfully complete a service task.
It should be noted that the above description related to the flow 200 is only for illustration and description, and does not limit the applicable scope of the present specification. Various modifications and alterations to flow 200 will be apparent to those skilled in the art in light of this description. However, such modifications and variations are intended to be within the scope of the present description. For example, the feature information of the target user in step 202 may further include feature attributes (e.g., age, gender, occupation, etc.) of the target user, and by further determining the feature attributes of the target user, the resource interests may be subsequently more accurately matched for the target user.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
FIG. 3 is a schematic diagram of a structure of a matching model shown in accordance with some embodiments of the present description.
In some embodiments, a trained matching model may be utilized to calculate a first matching score for the feature information of the target user in the input feature pair with the business category in the resource equity. Specifically, the matching model may be used to perform first weighted fusion on the consumption amount hierarchy, the consumption activity hierarchy, and the historical behavior habit in the feature information of the target user to obtain a first fusion feature. Optionally, corresponding weighting coefficients may be respectively given to the consumption amount hierarchy, the consumption activity hierarchy and the historical behavior habit, and then the first weighted fusion is performed to obtain the first fusion characteristic. Alternatively, the weighting factor may be set by system default or manually. Alternatively, the weighting coefficients may be parameters to be trained in the model. In some embodiments, the matching model may process the first fused feature with the business category in the resource interest to obtain a first matching score.
In some embodiments, a second match score of the feature information of the target user in the input feature pair to the user's in the resource equity may be calculated using the trained matching model. Specifically, the matching model may be used to perform second weighted fusion on the consumption amount hierarchy, the consumption activity hierarchy, and the historical behavior habit in the feature information of the target user to obtain a second fusion feature. Optionally, corresponding weighting coefficients may be respectively given to the consumption amount hierarchy, the consumption activity hierarchy and the historical behavior habit in the feature information of the target user, and then second weighting fusion is performed to obtain a second fusion feature. Alternatively, the weighting coefficients may be set by system default or manually or the model parameters to be trained. Alternatively, the weighting coefficients in the first weighted fusion may be different from the weighting coefficients in the second weighted fusion. In some embodiments, the matching model may process the second fused feature with the user interests in the resource interests to obtain a second matching score.
In some embodiments, the matching model may be further configured to fuse the first matching score with the second matching score to obtain a matching score. Specifically, the matching model may assign corresponding weighting coefficients to the first matching score and the second matching score, and then perform weighted summation to obtain the matching score. Alternatively, the weighting coefficients may be set by system default or manually or the model parameters to be trained.
In some embodiments, the matching model training method may include obtaining a training sample, and then training an initial model based on the training sample and the labeling result to obtain a matching model.
In some embodiments, the training sample may include a sample input feature pair and its label. The sample input feature pairs may include feature information of the sample user and corresponding resource rights. For more description of the sample input feature pairs, reference may be made to the associated description of fig. 2. When the resource interest in the sample input feature pair is accepted by the sample user history, the label may be "1" (for the case of a match score of a percentile, the label value may be set to 100); and conversely "0" (for a match score of a percentile, the label value may be set to 0). The training samples may be extracted from historical consumption orders, e.g., orders for historical purchases of goods or services by other users.
In some embodiments, the acquired training samples may be pre-processed to meet the requirements of model training. Preprocessing methods may include format conversion, normalization, identification, and the like.
In some embodiments, the training samples may also be partitioned into a training set and a validation set. Specifically, the training samples may be divided according to a certain ratio. For example, the partition ratio may be 80% for the training set and 20% for the validation set.
In some embodiments, the initial model may comprise a decision model. In some embodiments, the Decision model may include a Boosting Tree (Adaptive Boosting Tree), an Adaptive Boosting Tree (AdaBoost), a Gradient Boosting Decision Tree (GBDT), an extreme Gradient Boosting Tree (boost g), and the like. In some embodiments, the initial model may adjust internal parameters according to the training situation.
In some embodiments, a loss function may also be constructed based on the prediction results (e.g., match scores) of the model and the sample label values, and parameters in the model may be adjusted based on the back propagation of the gradient values of the loss function to optimize the model (e.g., minimize the loss function). In some embodiments, during the training process, the sample data in the verification set may be input into the trained model for calculation, an output value (i.e., a verification result) is obtained, and the model parameters are adjusted according to the verification result (e.g., the model is under-fit and/or over-fit) to optimize the model. And the data in the verification set and the training data of the initial model are independently and identically distributed and have no intersection. And comparing the verification result of the sample data with the identification of the corresponding sample data, and judging whether the training result meets the requirement. And if the training result does not meet the requirement, re-preparing the sample data or re-dividing the training set and the verification set for continuous training. If the training result meets the requirement, the model training can be stopped, and the final model is output as the required accuracy evaluation model.
Fig. 4 is a block diagram of an information push system shown in accordance with some embodiments of the present description.
As shown in fig. 4, the information push system 400 may include a determination module 402, an acquisition module 404, a processing module 406, and a push module 408.
The determining module 402 may be configured to determine characteristic information of the target user, where the characteristic information may reflect at least one of a consumption amount level of the user in the target domain, a consumption activity level in the target domain, and a historical behavior habit of the user. For more details on determining the characteristic information of the target user, reference may be made to the description of step 202, which is not described herein in detail.
The obtaining module 404 may be configured to obtain a plurality of resource rights, which may be from one or more service parties and include user rights and service types provided by the service parties. For more details on obtaining the multiple resource rights, reference may be made to the description of step 204, which is not described herein again.
The processing module 406 may be configured to process the characteristic information of the target user and the plurality of resource benefits to determine at least one target resource benefit matching the target user. For more details on determining at least one target resource right matching the target user, reference may be made to the description of step 206, which is not repeated herein.
The pushing module 408 may be configured to push the at least one target resource interest to a user terminal associated with the target user such that a consumption interface of the target domain on the user terminal exposes the at least one target resource interest. For more details on the right to push the at least one target resource, reference may be made to the description of step 208, which is not described herein in detail.
It should be appreciated that the system or modules illustrated in FIG. 4 may be implemented in a variety of ways. For example, in some embodiments, a system or module may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system or module of the present specification may be implemented not only by hardware circuits such as a very large scale integrated circuit or gate array, a semiconductor such as a logic chip, a transistor, or the like, or a programmable hardware device such as a field programmable gate array, a programmable logic device, or the like, but also by software executed by various types of processors, for example, and by a combination of the hardware circuits and the software (for example, firmware).
It should be noted that the above description of the information push system 400 or the modules is only for convenience of description, and the description is not limited to the scope of the embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of modules or sub-system configurations may be used to connect to other modules without departing from such teachings. For example, the determining module 402, the obtaining module 404, the processing module 406, and the pushing module 408 disclosed in fig. 4 may be different modules in a system, or may be a module that implements the two functions described above. Such variations are within the scope of the present disclosure.
The beneficial effects that may be brought by the embodiments of the present description include, but are not limited to: by matching the characteristic information of the target user with a plurality of resource rights and interests, the target user can be more suitable for the target resource rights and interests, the requirements of the application platform for stably maintaining the target user and developing new users for a long time are met, the business party of the resource rights and interests is guaranteed to complete business tasks, and the business party is attracted to provide the resource rights and interests for the application platform for a long time. It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.
Also, the description uses specific words to describe embodiments of the description. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.
Moreover, those skilled in the art will appreciate that aspects of the present description may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereof. Accordingly, aspects of this description may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present description may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.
The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.
Computer program code required for the operation of various portions of this specification may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).
Additionally, the order in which the elements and sequences of the process are recited in the specification, the use of alphanumeric characters, or other designations, is not intended to limit the order in which the processes and methods of the specification occur, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.
For each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited in this specification, the entire contents of each are hereby incorporated by reference into this specification. Except where the application is filed in a manner inconsistent or contrary to the present specification, and except where a claim is filed in a manner limited to the broadest scope of the application (whether present or later appended to the specification). It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.
Claims (11)
1. An information pushing method, the method comprising:
determining characteristic information of a target user, wherein the characteristic information reflects at least one of a consumption amount level of the user in a target field, a consumption activity level of the user in the target field and historical behavior habits of the user;
acquiring a plurality of resource rights, wherein the resource rights come from one or more service parties and comprise user rights and service types provided by the service parties;
processing the characteristic information of the target user and a plurality of resource rights and interests to determine at least one target resource right and interest matched with the target user;
pushing the at least one target resource interest to a user terminal associated with a target user to cause a consumption interface of the target domain on the user terminal to expose the at least one target resource interest.
2. The method of claim 1, the determining characteristic information of the target user comprising:
acquiring historical consumption amounts of a plurality of users in the target field;
clustering the historical consumption amounts of the users to obtain a plurality of consumption amount levels;
acquiring the historical consumption amount of the target user in the target field;
determining a spending amount tier at which the historical spending amount of the target user is located in the plurality of spending amount tiers.
3. The method of claim 1, the determining characteristic information of the target user comprising:
acquiring historical consumption frequency of a plurality of users in the target field;
clustering the historical consumption frequency of the users to obtain a plurality of consumption activity levels;
acquiring historical consumption frequency of the target user in the target field;
determining a consumption activity level at which the historical consumption frequency of the target user is located in the plurality of consumption activity levels.
4. The method of claim 1, the determining characteristic information of the target user comprising:
acquiring historical behavior data of the target user; the historical behavior data at least reflects the online consumption behavior information of the target user;
and processing the historical behavior data by using the trained behavior recognition model to determine the historical behavior habit of the target user.
5. The method of claim 1, the processing the characteristic information of the target user and the plurality of resource benefits to determine at least one target resource benefit matching the target user comprising:
combining the feature information of the target user with each of a plurality of resource rights to obtain a plurality of input feature pairs;
sequentially processing the plurality of input feature pairs by using a trained matching model to obtain matching scores of the target user and the plurality of resource rights;
and determining the resource right of which the matching score meets the preset condition as the target resource right.
6. The method of claim 5, the matching model for any input feature pair being used to:
calculating a first matching score of the feature information of the target user in the input feature pair and the business category in the resource right;
calculating a second matching score of the feature information of the target user in the input feature pair and the user interest in the resource interest;
and fusing the first matching score and the second matching score to obtain the matching score.
7. The method of claim 6, wherein to calculate a first match score between the feature information of the target user in the input feature pair and the business category in the resource interest, the matching model is used to:
carrying out first weighted fusion on the consumption amount level, the consumption activity level and the historical behavior habit in the feature information of the target user to obtain first fusion features;
determining the first match score based on the first fused feature and a business category in the resource interest;
to calculate a second match score between the feature information of the target user in the input feature pair and the business category in the resource interest, the matching model is configured to:
performing second weighted fusion on the consumption amount level, the consumption activity level and the historical behavior habit in the feature information of the target user to obtain second fusion features;
determining the second match score based on a second fused feature with a user of the resource interests;
wherein the weighting coefficients in the first weighted fusion are different from the weighting coefficients in the second weighted fusion.
8. The method of claim 1, the resource equity further comprising a business task, a user equity in the target resource equity for crediting at least a portion of an amount of money generated by the target user for consumption in a consumption interface of the target domain upon completion of the business task.
9. An information push system, the system comprising:
the determining module is used for determining characteristic information of a target user, wherein the characteristic information reflects at least one of a consumption amount level of the user in a target field, a consumption activity level of the user in the target field and historical behavior habits of the user;
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring a plurality of resource rights, the resource rights come from one or more service parties and comprise user rights and service types provided by the service parties;
the processing module is used for processing the characteristic information of the target user and a plurality of resource rights and interests so as to determine at least one target resource right and interest matched with the target user;
the pushing module is used for pushing the at least one target resource right to a user terminal associated with a target user so that a consumption interface of the target field on the user terminal shows the at least one target resource right.
10. An information pushing apparatus, the apparatus comprising at least one processor and at least one storage device, the storage device being configured to store instructions that, when executed by the at least one processor, implement the method according to any one of claims 1 to 8.
11. A computer readable storage medium storing computer instructions which, when read by a computer, cause the computer to perform the method of any one of claims 1 to 8.
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