CN113538108A - Resource information determination method and device, electronic equipment and storage medium - Google Patents

Resource information determination method and device, electronic equipment and storage medium Download PDF

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CN113538108A
CN113538108A CN202110853137.1A CN202110853137A CN113538108A CN 113538108 A CN113538108 A CN 113538108A CN 202110853137 A CN202110853137 A CN 202110853137A CN 113538108 A CN113538108 A CN 113538108A
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user
information
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feature
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肖圣龙
马恩驰
肖军波
宋烈金
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Beijing Jingdong Century Trading Co Ltd
Beijing Wodong Tianjun Information Technology Co Ltd
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Beijing Jingdong Century Trading Co Ltd
Beijing Wodong Tianjun Information Technology Co Ltd
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Abstract

The embodiment of the invention is suitable for the technical field of computers, and provides a resource information determination method, a resource information determination device, electronic equipment and a storage medium, wherein the resource information determination method comprises the following steps: determining a first characteristic corresponding to a first user; the first characteristic represents user information of a corresponding user; inputting the first characteristic and the at least two second characteristics into a set model to obtain a first similarity of the first characteristic and each second characteristic of the at least two second characteristics; the second feature represents a user set of a second user interacting with the corresponding resource; and determining resource information of the first resource pushed to the first user based on the determined first similarity.

Description

Resource information determination method and device, electronic equipment and storage medium
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method and an apparatus for determining resource information, an electronic device, and a storage medium.
Background
In the E-market scene, resources are pushed to appropriate users, and higher resource click rate and order conversion amount can be obtained. In the related art, an operator selects a user interested in each resource through understanding each resource as a user set of the resource. And for the new user, calculating the distance between the new user and the user set of each resource, and recommending the resource corresponding to the user set with the closest distance to the new user. In the related technology, the similarity between the user and each user set is directly calculated through the distance, the relationship between the user and the resource cannot be accurately measured, and the resource recommended to the user is not accurate enough.
Disclosure of Invention
In order to solve the above problem, embodiments of the present invention provide a method and an apparatus for determining resource information, an electronic device, and a storage medium, so as to solve at least the problem that in the related art, similarity between a user and each user set is directly calculated through a distance, and a resource recommended to the user is not accurate enough.
The technical scheme of the invention is realized as follows:
in a first aspect, an embodiment of the present invention provides a method for determining resource information, where the method includes:
determining a first characteristic corresponding to a first user; the first characteristic represents user information of a corresponding user;
inputting the first feature and the at least two second features into a set model to obtain a first similarity of the first feature and each of the at least two second features; the second feature characterizes a set of users of a second user interacting with the corresponding resource;
determining resource information of a first resource pushed to the first user based on the determined first similarity.
In the foregoing solution, the determining the first characteristic corresponding to the first user includes:
vectorizing the first information and/or the second information of the first user to obtain the first characteristic; the first information represents attribute information of the first user; the second information represents resource interaction information of the first user.
In the above scheme, the method further comprises:
determining a first characteristic of each second user in a set of users of second users interacting with the corresponding resource;
and determining second characteristics of the user set of the second users participating in the corresponding resource based on the determined first characteristics of the second users.
In the above scheme, the method further comprises:
under the condition that a second resource is added into a set resource list, determining a second similarity between the resource information of the second resource and the resource information of each resource in the set resource list; each resource in the set resource list corresponds to one second feature of the at least two second features;
and determining a second feature corresponding to the resource with the maximum second similarity to the resource information of the second resource in the set resource list as a second feature corresponding to the second resource.
In the foregoing solution, the determining, based on the determined first similarity, resource information of a first resource pushed to the first user includes:
and determining the resource information of the resource corresponding to the second feature of which the first similarity of the first feature is greater than a set value in the at least two second features as the resource information of the first resource pushed to the first user.
In the above scheme, the method further comprises:
and determining the user set of the second user corresponding to each resource according to the access time of the second user to access the resource page of the corresponding resource.
In a second aspect, an embodiment of the present invention provides a model training method, where the method includes:
splicing the first characteristics corresponding to a third user, the second characteristics of the user set corresponding to a third resource and the label of the third user to obtain first training data; the label represents whether the third user accesses a resource page of the third resource;
inputting the first training data into the set model to obtain a probability value output by the set model; the probability value characterizes a similarity of a first feature of the third user to a second feature of the set of users of the third resource;
adjusting model parameters of the set model based on the probability values.
In a third aspect, an embodiment of the present invention provides a resource information determining apparatus, where the apparatus includes:
the first determining module is used for determining a first characteristic corresponding to a first user; the first characteristic represents user information of a corresponding user;
the first input module is used for inputting the first characteristic and the at least two second characteristics into a set model to obtain a first similarity of the first characteristic and each second characteristic of the at least two second characteristics; the second feature characterizes a set of users of a second user interacting with the corresponding resource;
and the second determining module is used for determining the resource information of the first resource pushed to the first user based on the determined first similarity.
In a fourth aspect, an embodiment of the present invention provides a model training apparatus, including:
the splicing module is used for splicing the first characteristics corresponding to a third user, the second characteristics of the user set corresponding to a third resource and the label of the third user to obtain first training data; the label represents whether the third user accesses a resource page of the third resource;
the second input module is used for inputting the first training data into the set model to obtain a probability value output by the set model; the probability value characterizes a similarity of a first feature of the third user to a second feature of the set of users of the third resource;
and the adjusting module is used for adjusting the model parameters of the set model based on the probability value.
In a fifth aspect, an embodiment of the present invention provides an electronic device, including a processor and a memory, where the processor and the memory are connected to each other, where the memory is used to store a computer program, and the computer program includes program instructions, and the processor is configured to call the program instructions to execute the steps of the resource information determination method provided in the first aspect or the steps of the model training method provided in the second aspect of the embodiment of the present invention.
In a sixth aspect, an embodiment of the present invention provides a computer-readable storage medium, including: the computer-readable storage medium stores a computer program. The computer program, when executed by a processor, implements the steps of the resource information determination method as provided in the first aspect or the steps of the model training method as provided in the second aspect of an embodiment of the invention.
According to the method and the device for pushing the resource information, the first characteristic corresponding to the first user is determined, the first characteristic and the at least two second characteristics are input into the setting model, the first similarity between the first characteristic and each of the at least two second characteristics is obtained, and the resource information of the first resource pushed to the first user is determined based on the first similarity. The first characteristic represents user information of a corresponding user, and the second characteristic represents a user set of a second user interacting with a corresponding resource. According to the embodiment of the invention, the cross relationship between the first user and the resource is obtained by setting the model, so that the similarity between the first user and the user set of the resource can be more accurately obtained, and the resource information pushed to the user is more accurate.
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Fig. 1 is a schematic flow chart illustrating an implementation of a resource information determining method according to an embodiment of the present invention;
fig. 2 is a schematic flow chart illustrating an implementation of another resource information determining method according to an embodiment of the present invention;
fig. 3 is a schematic flow chart illustrating an implementation of another resource information determining method according to an embodiment of the present invention;
fig. 4 is a schematic flow chart illustrating an implementation of another resource information determining method according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a resource information pushing flow provided by an application embodiment of the present invention;
fig. 6 is a schematic diagram of a resource information determining apparatus according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a model training apparatus according to an embodiment of the present invention;
fig. 8 is a schematic diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the related art, the operator needs to have strong business experience to manually select the pushing user of the resource information. However, the resources change frequently, the manual selection efficiency is low, the participating users of the resources change in real time, and the real-time performance of the user set is not strong. The similarity between the user and each seed crowd is directly calculated through the distance, the relation between the user and the resource cannot be accurately measured, and the resource recommended to the user is not accurate enough.
In view of the above disadvantages of the related art, embodiments of the present invention provide a method for determining resource information, which can at least improve the accuracy of resources recommended to a user. In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
Fig. 1 is a schematic flow chart illustrating an implementation process of a resource information determining method according to an embodiment of the present invention, where an execution main body of the resource information determining method may be an electronic device, and the electronic device may be a desktop computer, a notebook computer, a server, and the like. Referring to fig. 1, the resource information determining method includes:
s101, determining a first characteristic corresponding to a first user; the first feature represents user information of a corresponding user.
Here, the first user refers to a push user of the resource information. Here, the resource refers to an activity, and in the e-market scene, the resource may be a full-reduction activity of a platform, a coupon issuing activity, a cash-back payment activity, and the like.
And vectorizing the user information of the first user to obtain a first characteristic corresponding to the first user. The user information of the first user includes sex, age, interaction information with the resource, and the like of the user.
In an embodiment, the determining the first characteristic corresponding to the first user includes:
vectorizing the first information and/or the second information of the first user to obtain the first characteristic; the first information represents attribute information of the first user; the second information represents resource interaction information of the first user.
Here, the first information refers to attribute information of the first user, and the attribute information includes information of the user's age, sex, height, and the like. For example, one _ hot transformation is performed for gender, male is (1, 0), and female is (0, 1). The second information refers to resource interaction information of the first user, such as the number of times that the user clicks a resource page in one month, the number of commodities purchased by the user in one month, and the like. Here, the first feature of the user is denoted by userV.
S102, inputting the first feature and the at least two second features into a set model to obtain a first similarity of the first feature and each of the at least two second features; the second feature characterizes a set of users of a second user interacting with the corresponding resource.
The embodiment of the invention has a resource list, each resource in the resource list corresponds to one of at least two second characteristics, each resource has a user set, and the user set is composed of second users interacting with the corresponding resource.
In an embodiment, the user set of the second user corresponding to each resource is determined according to the access time of the second user to access the resource page of the corresponding resource.
For each user set of resources, the embodiment of the present invention sets an upper limit on the number of people, for example, the maximum number of people in the user set is 100. And if the number of the second users interacting with the corresponding resources is more than 100, sorting the second users according to the sequence of the access time of the second users accessing the resource pages of the corresponding resources, and placing the second users most recently accessing the resource pages of the corresponding resources at the forefront and sorting in sequence. The second user who takes the top 100 rank forms the set of users for the corresponding resource. In this way, the real-time performance of the user set can be ensured.
Referring to fig. 2, in an embodiment, the method further comprises:
s201, determining the first characteristics of each second user in the user set of the second users interacting with the corresponding resources.
And vectorizing the user information of each second user in the user set to obtain the first characteristic of each second user in the user set.
S202, determining second characteristics of the user set of the second users participating in the corresponding resources based on the determined first characteristics of the second users.
Here, the first feature of each second user in the user set may be averaged to obtain the second feature of the user set.
For example, the first feature of the second user in the user set is represented by userV, and the second feature is represented by seedV.
seedV＝average_pooling(seedn(actid))＝average_pooling(userVN)＝average_pooling(userV1，userV2，…，userVN)＝(average(userV11+userV21+…+userVN1)，average(userV12+userV22+…+userVN2)，…，average(userV1K+userV2K+…+userVNK)). Wherein, userVNKIs shown asAnd the K-th bit characteristic value corresponding to the vector of the N-bit users.
And inputting the first characteristic and the at least two second characteristics of the first user into the setting model to obtain the first similarity of the first characteristic output by the setting model and each second characteristic of the at least two second characteristics. Here, the set model is used to determine the similarity between the features, and the first similarity output by the set model is a probability value indicating the degree of similarity between the first feature and the second feature.
For example, if a resource is a preferential resource with respect to a razor, the first user frequently searches for razor-related products, and the first user is a male, the first user's first characteristic has a greater first similarity to the second characteristic of the resource.
Before this, the setting model needs to be trained in advance by training data.
Fig. 3 is a schematic flow chart of an implementation process of a model training method according to an embodiment of the present invention, where the model training method is used to train a set model in the above embodiment, an execution subject of the model training method may be an electronic device, and the electronic device may be a desktop computer, a notebook computer, a server, and the like. Referring to fig. 3, the model training method includes:
s301, splicing a first feature corresponding to a third user, a second feature of a user set corresponding to a third resource and a label of the third user to obtain first training data; the tag characterizes whether the third user accesses a resource page of the third resource.
Here, the third resource may or may not be a resource in the resource list. Whether the third user accesses the resource page of the third resource is known, the first feature of the third user and the second feature corresponding to the user set of the third resource are spliced, for example, the first feature of the third user is userV, the second feature corresponding to the third resource is seedV, the behavior of whether the third user accesses the resource page of the third resource is represented by Y, the accessed Y is marked as 1, and the non-accessed Y is marked as 0. The first training data is denoted by X, then X ═ userV, seedV, Y.
The embodiment of the invention only obtains one piece of training data of the set model through splicing, and the embodiment only obtains the example of the training data through splicing, and does not represent that only one piece of training data is needed for setting the model. The set model requires a plurality of training data to carry out iterative training during training so as to ensure the accuracy of the output result of the set model.
S302, inputting the first training data into the set model to obtain a probability value output by the set model; the probability value characterizes a similarity of a first feature of the third user to a second feature of the set of users of the third resource.
And inputting the spliced first training data into the set model for model training by taking the spliced first training data as training data of the set model. And outputting a probability value by the set model in the training process, wherein the probability value represents the similarity of the first characteristic of the third user and the second characteristic of the user set of the third resource.
S303, adjusting the model parameters of the set model based on the probability value.
Adjusting model parameters of the set model according to the probability value, such as adjusting the number of residual iterations; adjusting the batch size, wherein the batch size refers to the number of samples sent into the model each time the neural network is trained; and adjusting the learning rate, wherein the learning rate refers to the magnitude of updating the network weight in the optimization algorithm.
And training the set model after the model parameters are adjusted by using the training data again until the loss function of the set model is converged. The trained setting model can be used for acquiring the similarity between the first feature of the user and the second feature corresponding to the resource.
S103, determining resource information of the first resource pushed to the first user based on the determined first similarity.
In an embodiment, the determining resource information of a first resource pushed to the first user based on the determined first similarity includes:
and determining the resource information of the resource corresponding to the second feature of which the first similarity of the first feature is greater than a set value in the at least two second features as the resource information of the first resource pushed to the first user.
Here, the resource information of a plurality of first resources may be pushed to the first user at a time, or only the resource information of the resource with the largest first similarity may be pushed to the first user. The relevance between the user and the user set of the resources is captured through the set model, the resources with the maximum relevance are pushed to the user, and the accuracy of pushing the resources is guaranteed.
Referring to fig. 4, in an embodiment, the method further comprises:
s401, under the condition that a second resource is added into a set resource list, determining a second similarity between the resource information of the second resource and the resource information of each resource in the set resource list; each resource in the set resource list corresponds to one of the at least two second characteristics.
If a resource is newly developed, the new resource needs to be added into the set resource list, and the new resource is not interacted with the user. Here, the second resource is a new resource that is accurately added to the set resource list.
And determining a second similarity between the resource information of the second resource and the resource information of each resource in the set resource list, wherein the second similarity is the similarity between the resource information. The resource information includes the gender of the resource, the commodity attribute of the resource, etc., for example, if one resource is a preferential resource about the brand of the sanitary napkin, the user facing the sanitary napkin is female, and if the other resource is a preferential resource about the shaver, the user facing the shaver is male, the second similarity of the resource information of the two resources is very low.
S402, determining a second feature corresponding to the resource with the largest second similarity to the resource information of the second resource in the set resource list as a second feature corresponding to the second resource.
And determining a second similarity between the resource information of the second resource and the resource information of each resource in the set resource list, and taking a second feature corresponding to the resource with the maximum second similarity between the resource information of the second resource and the set resource list as a second feature corresponding to the second resource. That is, the user set corresponding to the resource with the largest second similarity is used as the user set of the second resource.
According to the method and the device for pushing the resource information, the first characteristic corresponding to the first user is determined, the first characteristic and the at least two second characteristics are input into the setting model, the first similarity between the first characteristic and each of the at least two second characteristics is obtained, and the resource information of the first resource pushed to the first user is determined based on the first similarity. The first characteristic represents user information of a corresponding user, and the second characteristic represents a user set of a second user interacting with a corresponding resource. According to the embodiment of the invention, the cross relationship between the first user and the resource is obtained by setting the model, so that the similarity between the first user and the user set of the resource can be more accurately obtained, and the resource information pushed to the user is more accurate.
Referring to fig. 5, fig. 5 is a schematic diagram of a resource information pushing flow provided by an application embodiment of the present invention. And generating resources in the effective resource list according to the real-time resource data of the releasing side, and generating a user set of the resources in the effective resource list according to the real-time click data of the users on the resources in the effective resource list. Here, the user set of resources in the list of active resources may be updated every 10 minutes based on the real-time click data. And determining the first characteristic of the user according to the user information, and determining the second characteristic of the user set of each resource in the effective resource list according to the first characteristic of each user in the user set.
When preparing to push resources to a user, calculating the similarity between the first characteristic of the user and the second characteristic of the resources through a set model, and pushing the resources corresponding to the second characteristic with the similarity larger than a set value to the user. In the figure, a top resource, that is, a resource with a similarity greater than a set value is pushed to a user.
The application embodiment of the invention clicks the real-time resource on the user to determine the user set of the resource, thereby not only ensuring the real-time characteristic of the resource, but also improving the efficiency of determining the user set. The cross relationship between the user and the resource is obtained by setting the model, so that the similarity between the user and the user set of the resource can be more accurately obtained, and the resource information pushed to the user is more accurate.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The technical means described in the embodiments of the present invention may be arbitrarily combined without conflict.
In addition, in the embodiments of the present invention, "first", "second", and the like are used for distinguishing similar objects, and are not necessarily used for describing a specific order or a sequential order.
Referring to fig. 6, fig. 6 is a schematic diagram of a resource information determining apparatus according to an embodiment of the present invention, as shown in fig. 6, the apparatus includes: the device comprises a first determination module, a first input module and a second determination module.
The first determining module is used for determining a first characteristic corresponding to a first user; the first characteristic represents user information of a corresponding user;
the first input module is used for inputting the first characteristic and the at least two second characteristics into a set model to obtain a first similarity of the first characteristic and each second characteristic of the at least two second characteristics; the second feature characterizes a set of users of a second user interacting with the corresponding resource;
and the second determining module is used for determining the resource information of the first resource pushed to the first user based on the determined first similarity.
In an embodiment, the first determining module, when determining the first feature corresponding to the first user, is configured to:
vectorizing the first information and/or the second information of the first user to obtain the first characteristic; the first information represents attribute information of the first user; the second information represents resource interaction information of the first user.
The device further comprises:
a third determining module, configured to determine a first feature of each second user in the user set of second users interacting with the corresponding resource;
and determining second characteristics of the user set of the second users participating in the corresponding resource based on the determined first characteristics of the second users.
The device further comprises:
a fourth determining module, configured to determine, when a second resource is added to a set resource list, a second similarity between resource information of the second resource and resource information of each resource in the set resource list; each resource in the set resource list corresponds to one second feature of the at least two second features;
and determining a second feature corresponding to the resource with the maximum second similarity to the resource information of the second resource in the set resource list as a second feature corresponding to the second resource.
In an embodiment, the second determining module, when determining resource information of the first resource pushed to the first user based on the determined first similarity, is configured to:
and determining the resource information of the resource corresponding to the second feature of which the first similarity of the first feature is greater than a set value in the at least two second features as the resource information of the first resource pushed to the first user.
The device further comprises:
and the fifth determining module is used for determining the user set of the second user corresponding to each resource according to the access time of the second user to access the resource page of the corresponding resource.
Referring to fig. 7, fig. 7 is a schematic diagram of a model training apparatus according to an embodiment of the present invention, as shown in fig. 7, the apparatus includes: the device comprises a splicing module, a second input module and an adjusting module.
The splicing module is used for splicing the first characteristics corresponding to a third user, the second characteristics of the user set corresponding to a third resource and the label of the third user to obtain first training data; the label represents whether the third user accesses a resource page of the third resource;
the second input module is used for inputting the first training data into the set model to obtain a probability value output by the set model; the probability value characterizes a similarity of a first feature of the third user to a second feature of the set of users of the third resource;
and the adjusting module is used for adjusting the model parameters of the set model based on the probability value.
In practical applications, the first determining module, the first input module and the second determining module may be implemented by a Processor in an electronic device, such as a Central Processing Unit (CPU), a Digital Signal Processor (DSP), a Micro Control Unit (MCU), or a Programmable gate array (FPGA).
It should be noted that: the resource information determining apparatus provided in the foregoing embodiment is only illustrated by dividing the modules when determining the resource information, and in practical applications, the processing allocation may be completed by different modules according to needs, that is, the internal structure of the apparatus may be divided into different modules to complete all or part of the processing described above. In addition, the resource information determining apparatus and the resource information determining method provided in the above embodiments belong to the same concept, and specific implementation processes thereof are detailed in the method embodiments and are not described herein again.
Based on the hardware implementation of the program module, in order to implement the method of the embodiment of the present application, an embodiment of the present application further provides an electronic device. Fig. 8 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present application, and as shown in fig. 8, the electronic device includes:
the communication interface can carry out information interaction with other equipment such as network equipment and the like;
and the processor is connected with the communication interface to realize information interaction with other equipment, and is used for executing the method provided by one or more technical schemes on the electronic equipment side when running a computer program. And the computer program is stored on the memory.
Of course, in practice, the various components in an electronic device are coupled together by a bus system. It will be appreciated that a bus system is used to enable communications among the components. The bus system includes a power bus, a control bus, and a status signal bus in addition to a data bus. For clarity of illustration, however, the various buses are labeled as a bus system in fig. 8.
The memory in the embodiments of the present application is used to store various types of data to support the operation of the electronic device. Examples of such data include: any computer program for operating on an electronic device.
It will be appreciated that the memory can be either volatile memory or nonvolatile memory, and can include both volatile and nonvolatile memory. Among them, the nonvolatile Memory may be a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a magnetic random access Memory (FRAM), a flash Memory (flash Memory), a magnetic surface Memory, an optical disk, or a Compact Disc Read-Only Memory (CD-ROM); the magnetic surface storage may be disk storage or tape storage. The volatile Memory may be a Random Access Memory (RAM) which serves as an external cache. By way of illustration and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM, Double Data Synchronous Random Access Memory), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM, Enhanced Synchronous Dynamic Random Access Memory), Synchronous link Dynamic Random Access Memory (SLDRAM, Synchronous Dynamic Random Access Memory), Direct Memory (DRmb Random Access Memory, Random Access Memory). The memories described in the embodiments of the present application are intended to comprise, without being limited to, these and any other suitable types of memory.
The method disclosed in the embodiments of the present application may be applied to a processor, or may be implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The processor described above may be a general purpose processor, a DSP, or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. The processor may implement or perform the methods, steps, and logic blocks disclosed in the embodiments of the present application. A general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the method disclosed in the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules may be located in a storage medium located in a memory where a processor reads the programs in the memory and in combination with its hardware performs the steps of the method as previously described.
Optionally, when the processor executes the program, the corresponding process implemented by the electronic device in each method of the embodiment of the present application is implemented, and for brevity, no further description is given here.
In an exemplary embodiment, the present application further provides a storage medium, specifically a computer storage medium, for example, a first memory storing a computer program, where the computer program is executable by a processor of an electronic device to perform the steps of the foregoing method. The computer readable storage medium may be Memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface Memory, optical disk, or CD-ROM.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus, electronic device and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
Alternatively, the integrated units described above in the present application may be stored in a computer-readable storage medium if they are implemented in the form of software functional modules and sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially implemented or portions thereof contributing to the prior art may be embodied in the form of a software product stored in a storage medium, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
The technical means described in the embodiments of the present application may be arbitrarily combined without conflict.
In addition, in the examples of the present application, "first", "second", and the like are used for distinguishing similar objects, and are not necessarily used for describing a specific order or a sequential order.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (11)

1. A method for determining resource information, the method comprising:
determining a first characteristic corresponding to a first user; the first characteristic represents user information of a corresponding user;
inputting the first feature and the at least two second features into a set model to obtain a first similarity of the first feature and each of the at least two second features; the second feature characterizes a set of users of a second user interacting with the corresponding resource;
determining resource information of a first resource pushed to the first user based on the determined first similarity.
2. The method of claim 1, wherein determining the first characteristic corresponding to the first user comprises:
vectorizing the first information and/or the second information of the first user to obtain the first characteristic; the first information represents attribute information of the first user; the second information represents resource interaction information of the first user.
3. The method of claim 1, further comprising:
determining a first characteristic of each second user in a set of users of second users interacting with the corresponding resource;
and determining second characteristics of the user set of the second users participating in the corresponding resource based on the determined first characteristics of the second users.
4. The method of claim 1, further comprising:
under the condition that a second resource is added into a set resource list, determining a second similarity between the resource information of the second resource and the resource information of each resource in the set resource list; each resource in the set resource list corresponds to one second feature of the at least two second features;
and determining a second feature corresponding to the resource with the maximum second similarity to the resource information of the second resource in the set resource list as a second feature corresponding to the second resource.
5. The method of claim 1, wherein the determining resource information of the first resource pushed to the first user based on the determined first similarity comprises:
and determining the resource information of the resource corresponding to the second feature of which the first similarity of the first feature is greater than a set value in the at least two second features as the resource information of the first resource pushed to the first user.
6. The method of claim 1, further comprising:
and determining the user set of the second user corresponding to each resource according to the access time of the second user to access the resource page of the corresponding resource.
7. A model training method for training a set model according to claim 1, the method comprising:
splicing the first characteristics corresponding to a third user, the second characteristics of the user set corresponding to a third resource and the label of the third user to obtain first training data; the label represents whether the third user accesses a resource page of the third resource;
inputting the first training data into the set model to obtain a probability value output by the set model; the probability value characterizes a similarity of a first feature of the third user to a second feature of the set of users of the third resource;
adjusting model parameters of the set model based on the probability values.
8. A resource information determination apparatus, comprising:
the first determining module is used for determining a first characteristic corresponding to a first user; the first characteristic represents user information of a corresponding user;
the first input module is used for inputting the first characteristic and the at least two second characteristics into a set model to obtain a first similarity of the first characteristic and each second characteristic of the at least two second characteristics; the second feature characterizes a set of users of a second user interacting with the corresponding resource;
and the second determining module is used for determining the resource information of the first resource pushed to the first user based on the determined first similarity.
9. A model training apparatus, comprising:
the splicing module is used for splicing the first characteristics corresponding to a third user, the second characteristics of the user set corresponding to a third resource and the label of the third user to obtain first training data; the label represents whether the third user accesses a resource page of the third resource;
the second input module is used for inputting the first training data into the set model to obtain a probability value output by the set model; the probability value characterizes a similarity of a first feature of the third user to a second feature of the set of users of the third resource;
and the adjusting module is used for adjusting the model parameters of the set model based on the probability value.
10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the resource information determination method of any one of claims 1 to 6 or the model training method of claim 7 when executing the computer program.
11. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program comprising program instructions that, when executed by a processor, cause the processor to perform the resource information determination method of any one of claims 1 to 6 or the model training method of claim 7.
CN202110853137.1A 2021-07-27 2021-07-27 Resource information determination method and device, electronic equipment and storage medium Pending CN113538108A (en)

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Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107103057A (en) * 2017-04-13 2017-08-29 腾讯科技（深圳）有限公司 A kind of resource supplying method and device
CN109190046A (en) * 2018-09-18 2019-01-11 北京点网聚科技有限公司 Content recommendation method, device and content recommendation service device
WO2020048084A1 (en) * 2018-09-07 2020-03-12 平安科技（深圳）有限公司 Resource recommendation method and apparatus, computer device, and computer-readable storage medium
CN111681059A (en) * 2020-08-14 2020-09-18 支付宝(杭州)信息技术有限公司 Training method and device of behavior prediction model
CN112559901A (en) * 2020-12-11 2021-03-26 百度在线网络技术（北京）有限公司 Resource recommendation method and device, electronic equipment, storage medium and computer program product
CN112650948A (en) * 2020-12-30 2021-04-13 华中师范大学 Information network construction method, system and application for education informatization evaluation
WO2021068610A1 (en) * 2019-10-12 2021-04-15 平安国际智慧城市科技股份有限公司 Resource recommendation method and apparatus, electronic device and storage medium
CN112800291A (en) * 2021-04-15 2021-05-14 武汉卓尔数字传媒科技有限公司 Similar account determination method and device, electronic equipment and storage medium
CN112905651A (en) * 2021-02-20 2021-06-04 卓尔智联(武汉)研究院有限公司 Information recommendation method and device, electronic equipment and storage medium

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107103057A (en) * 2017-04-13 2017-08-29 腾讯科技（深圳）有限公司 A kind of resource supplying method and device
US20190364123A1 (en) * 2017-04-13 2019-11-28 Tencent Technology (Shenzhen) Company Limited Resource push method and apparatus
WO2020048084A1 (en) * 2018-09-07 2020-03-12 平安科技（深圳）有限公司 Resource recommendation method and apparatus, computer device, and computer-readable storage medium
CN109190046A (en) * 2018-09-18 2019-01-11 北京点网聚科技有限公司 Content recommendation method, device and content recommendation service device
WO2021068610A1 (en) * 2019-10-12 2021-04-15 平安国际智慧城市科技股份有限公司 Resource recommendation method and apparatus, electronic device and storage medium
CN111681059A (en) * 2020-08-14 2020-09-18 支付宝(杭州)信息技术有限公司 Training method and device of behavior prediction model
CN112559901A (en) * 2020-12-11 2021-03-26 百度在线网络技术（北京）有限公司 Resource recommendation method and device, electronic equipment, storage medium and computer program product
CN112650948A (en) * 2020-12-30 2021-04-13 华中师范大学 Information network construction method, system and application for education informatization evaluation
CN112905651A (en) * 2021-02-20 2021-06-04 卓尔智联(武汉)研究院有限公司 Information recommendation method and device, electronic equipment and storage medium
CN112800291A (en) * 2021-04-15 2021-05-14 武汉卓尔数字传媒科技有限公司 Similar account determination method and device, electronic equipment and storage medium

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