CN111460286A - Information recommendation method and device, electronic equipment and medium - Google Patents

Information recommendation method and device, electronic equipment and medium Download PDF

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CN111460286A
CN111460286A CN202010223996.8A CN202010223996A CN111460286A CN 111460286 A CN111460286 A CN 111460286A CN 202010223996 A CN202010223996 A CN 202010223996A CN 111460286 A CN111460286 A CN 111460286A
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recommendation
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徐杰
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Ping An Technology Shenzhen Co Ltd
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Abstract

The application provides an information recommendation method and device. The method comprises the steps of obtaining a user database, obtaining first characteristics of each user in the user database, and determining pre-recommended users in the user database through a neural network model according to the first characteristics of each user in the user database, wherein the pre-recommended users are one or more. For each pre-recommended user, obtaining second characteristics of the pre-recommended user, inputting the second characteristics of the pre-recommended user into one or more decision tree models, and obtaining a pre-recommended result output by each decision tree model, wherein each decision tree model corresponds to one type of pre-recommended information; and selecting pre-recommendation information according to the pre-recommendation result and the second characteristics of the pre-recommendation user to combine to obtain recommendation information, so that appropriate information can be accurately recommended to the user.

Description

Information recommendation method and device, electronic equipment and medium
Technical Field
The present application relates to the field of computer and communication technologies, and in particular, to an information recommendation method and apparatus, an electronic device, and a medium.
Background
With the improvement of living standard, people have higher and higher requirements on various applications, and a recommendation system is generated at the same time. The recommendation system is a product of the big data age and already exists in the aspects of people's life.
In the existing recommendation systems, the most representative one is similar recommendation, and similar information is recommended to users according to information selected by user history. The recommendation is not personalized enough, the diversity of the recommendation information is not enough, and the potential requirements of the user on other information cannot be found. Therefore, it is an urgent problem to recommend appropriate information to a user.
Disclosure of Invention
The application aims to provide an information recommendation method, an information recommendation device, electronic equipment and a medium, which can accurately recommend proper information to a user.
According to an aspect of an embodiment of the present application, there is provided an information recommendation method, including: acquiring a user database, acquiring first characteristics of each user in the user database, and determining pre-recommended users in the user database through a neural network model according to the first characteristics of each user in the user database, wherein the pre-recommended users are one or more; for each pre-recommended user, obtaining second characteristics of the pre-recommended user, inputting the second characteristics of the pre-recommended user into one or more decision tree models, and obtaining a pre-recommended result output by each decision tree model, wherein each decision tree model corresponds to one type of pre-recommended information; and selecting the pre-recommendation information to be combined according to the pre-recommendation result and the second characteristics of the pre-recommendation users to obtain recommendation information.
According to an aspect of an embodiment of the present application, there is provided an information recommendation apparatus including: the acquisition module is used for acquiring a user database, acquiring first characteristics of each user in the user database, and determining pre-recommended users in the user database through a neural network model according to the first characteristics of each user in the user database, wherein the pre-recommended users are one or more; the pre-recommendation module is used for acquiring second characteristics of each pre-recommended user, inputting the second characteristics of the pre-recommended user into one or more decision tree models, and obtaining a pre-recommendation result output by each decision tree model, wherein each decision tree model corresponds to one type of pre-recommendation information; and the recommending module selects the pre-recommending information to combine to obtain recommending information according to the pre-recommending result and the second characteristics of the pre-recommending users.
In some embodiments of the present application, based on the foregoing solution, the pre-recommendation module is configured to: acquiring second characteristics of the pre-recommendation information, wherein the second characteristics of the pre-recommendation information comprise one or more factors; and establishing a decision tree model corresponding to the pre-recommendation information according to the weight of one or more factors of the second characteristic of the pre-recommendation information in the pre-recommendation information.
In some embodiments of the present application, based on the foregoing, the second characteristic of the pre-recommended user includes one or more factors; the pre-recommendation module is configured to: and inputting one or more factors in the second characteristics of the pre-recommended user into each decision tree model, and obtaining the matching probability between the pre-recommended user and the pre-recommended information corresponding to the decision tree model, which is output by each decision tree model, based on the second characteristics of the pre-recommended user and the second characteristics of the pre-recommended information in each decision tree model.
In some embodiments of the present application, based on the foregoing, the recommendation module is configured to: and taking the pre-recommendation information as the recommendation information.
In some embodiments of the present application, based on the foregoing, the recommendation module is configured to: the second characteristic of the pre-recommended user comprises total resources W; recording the pre-recommendation information preset weight corresponding to the Mth decision tree model as thetaM(M ═ 1,2,3 … M), the matching probability of the mth decision tree model output is noted as PM(ii) a Selecting N pre-recommendation information from pre-recommendation information corresponding to M model trees, and recording the matching probability output by a decision tree model corresponding to the Nth pre-recommendation information as PMN(N ═ 1,2,3 … …); by the formula: wN＝PMNθMW×β/(PM1+PM2+...+PMN) Obtaining the N-th pre-recommended information suggestion allocation resource WMWherein β is a preset coefficient.
In some embodiments of the present application, based on the foregoing, the recommendation module is configured such that the value of β is 10%.
According to an aspect of embodiments of the present application, there is provided a computer-readable program medium storing computer program instructions which, when executed by a computer, cause the computer to perform the method of any one of the above.
According to an aspect of an embodiment of the present application, there is provided an electronic apparatus including: a processor; a memory having computer readable instructions stored thereon which, when executed by the processor, implement the method of any of the above.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
in the technical solutions provided by some embodiments of the present application, a first feature of each user in a user database is obtained by obtaining the user database, and pre-recommended users in the user database are determined through a neural network model according to the first feature of each user in the user database, where the pre-recommended users are one or more pre-recommended users; for each pre-recommended user, obtaining second characteristics of the pre-recommended user, inputting the second characteristics of the pre-recommended user into one or more decision tree models, and obtaining a pre-recommended result output by each decision tree model, wherein each decision tree model corresponds to one type of pre-recommended information; and selecting pre-recommendation information according to the pre-recommendation result and the second characteristics of the pre-recommendation user to combine to obtain recommendation information, so that proper information is accurately recommended to the user.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
FIG. 1 shows a schematic diagram of an exemplary system architecture to which aspects of embodiments of the present application may be applied;
FIG. 2 schematically shows a flow diagram of an information recommendation method according to an embodiment of the present application;
FIG. 3 schematically shows a block diagram of an information recommendation device according to an embodiment of the present application;
FIG. 4 is a hardware diagram of an electronic device shown in accordance with an exemplary embodiment;
fig. 5 is a computer-readable storage medium for implementing the above-described information recommendation method according to an exemplary embodiment.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the subject matter of the present application can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the application.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
Fig. 1 shows a schematic diagram of an exemplary system architecture 100 to which the technical solutions of the embodiments of the present application can be applied.
As shown in fig. 1, the system architecture 100 may include a terminal device 101 (which may be one or more of a smartphone, a tablet, a laptop, a desktop computer), a network 102, and a server 103. Network 102 is the medium used to provide communication links between terminal devices 101 and server 103. Network 102 may include various connection types, such as wired communication links, wireless communication links, and so forth.
It should be understood that the number of terminal devices 101, networks 102, and servers 103 in fig. 1 is merely illustrative. There may be any number of terminal devices 101, networks 102, and servers 103, as desired for implementation. For example, the server 103 may be a server cluster composed of a plurality of servers.
In an embodiment of the present application, the server 103 obtains the first characteristic of each user in the user database by obtaining the user database, and determines, according to the first characteristic of each user in the user database, one or more pre-recommended users in the user database through a neural network model; for each pre-recommended user, obtaining second characteristics of the pre-recommended user, inputting the second characteristics of the pre-recommended user into one or more decision tree models, and obtaining a pre-recommended result output by each decision tree model, wherein each decision tree model corresponds to one type of pre-recommended information; and selecting pre-recommendation information according to the pre-recommendation result and the second characteristics of the pre-recommendation user to combine to obtain recommendation information, so that proper information is accurately recommended to the user.
It should be noted that the information recommendation method provided in the embodiment of the present application is generally executed by the server 103, and accordingly, the information recommendation apparatus is generally disposed in the server 103. However, in other embodiments of the present application, the terminal device 101 may also have a similar function to the server 103, so as to execute the information recommendation method provided in the embodiments of the present application.
The implementation details of the technical solution of the embodiment of the present application are set forth in detail below:
fig. 2 schematically shows a flowchart of an information recommendation method according to an embodiment of the present application, where an execution subject of the information recommendation method may be a server, such as the server 103 shown in fig. 1.
Referring to fig. 2, the information recommendation method at least includes steps S210 to S230, which are described in detail as follows:
in step S210, a user database is obtained, a first characteristic of each user in the user database is obtained, and according to the first characteristic of each user in the user database, pre-recommended users in the user database are determined through a neural network model, where the pre-recommended users are one or more.
In an embodiment of the application, the first feature of each user in the user database may be input into the neural network model to obtain an output result of the neural network model, and the pre-recommended user in the user database may be determined according to the output result of the neural network model.
In one embodiment of the present application, the output result of the neural network model may be a pre-recommended user list.
In one embodiment of the present application, the neural network model may be trained by: the method includes the steps of obtaining a first feature sample set, wherein an output result corresponding to each first feature sample in the first feature sample set is known, and the output result corresponding to the first feature sample can be whether a user corresponding to each first feature sample is a pre-recommended user or not. Inputting the first characteristic of each user in the user database into the neural network model, acquiring the result of whether the user output by the neural network model is a pre-recommended user, comparing the output result with the known output result corresponding to the same first characteristic, and if not, adjusting the neural network model to enable the output result to be consistent with the known output result corresponding to the same first characteristic.
In one embodiment of the application, the first characteristic may include a demographic attribute, an enterprise attribute, a geographic attribute, an insurance awareness, a life cycle, an underwriting claim settlement record and the like, and whether the pre-recommended user is a user who may purchase insurance or not may be judged according to the first characteristic, and only marketing is performed on the target user.
In an embodiment of the application, the first characteristic may include a memory capacity, a data occupation space, and the like of the user, and the pre-recommended user is determined as a user that may need to cache according to the first characteristic.
Continuing to refer to fig. 2, in step S220, for each pre-recommended user, obtaining a second feature of the pre-recommended user, and inputting the second feature of the pre-recommended user into one or more decision tree models to obtain a pre-recommendation result output by each decision tree model, where each decision tree model corresponds to one type of pre-recommendation information.
In one embodiment of the present application, the second characteristic of the pre-recommended users may be a family income level, whether married, whether there is a car, whether there is a child, etc., and each decision tree model corresponds to an insurance.
In an embodiment of the application, the second characteristic of the pre-recommended user may be a data type frequently accessed by the user, a data size frequently accessed by the user, a duration of internet surfing of the user, and the like, and each decision tree model corresponds to one cache information.
In an embodiment of the application, a second characteristic of the pre-recommendation information may be obtained, where the second characteristic of the pre-recommendation information includes one or more factors; and according to the weight of one or more factors of the second characteristic of the pre-recommendation information in the pre-recommendation information, establishing a decision tree model corresponding to the pre-recommendation information, so that the decision tree model corresponding to each piece of pre-recommendation information conforms to the characteristics of the pre-recommendation information.
In one embodiment of the present application, since the factors at the root of the decision tree have the greatest influence on the result of the decision tree, the factors having the greater influence may be set closer to the root of the decision tree.
In one embodiment of the present application, the bottom-up factoring of the decision tree may be family income level, whether married, whether children are present, whether cars are present.
In an embodiment of the present application, the factor arrangement of the decision tree from bottom to top may be a user's internet surfing time, a type of data frequently accessed by the user, and a size of data frequently accessed by the user.
In an embodiment of the application, the second feature of the pre-recommended user includes one or more factors, the one or more factors in the second feature of the pre-recommended user may be input into each decision tree model, and the matching probability between the pre-recommended user output by each decision tree model and the pre-recommended information corresponding to the decision tree model is obtained based on the second feature of the pre-recommended user and the second feature of the pre-recommended information in each decision tree model.
With continued reference to fig. 2, in step S230, the pre-recommendation information is selected and combined according to the pre-recommendation result and the second characteristic of the pre-recommendation user to obtain the recommendation information.
In an embodiment of the present application, if there is only one type of pre-recommendation information, the pre-recommendation information may be used as the recommendation information.
In one embodiment of the present application, the second characteristic of the pre-recommended user may include total resources W; selecting N pieces of pre-recommendation information from the pre-recommendation information corresponding to the M model trees, and recording the matching probability output by the decision tree model corresponding to the selected Nth piece of pre-recommendation information as PMN(N ═ 1,2,3 … …); recording the pre-recommendation information preset weight corresponding to the Nth decision tree model as thetaM(M ═ 1,2,3 … M), by the formula: wN＝PMNθMW×β/(PM1+PM2+...+PMN) Obtaining the N-th pre-recommended information suggestion allocation resource WMWherein β is a preset coefficient.
In one embodiment of the present application, the total resource W may be a family annual income of the user, the insurance types suitable for the family are determined according to the second characteristics of the user, such as whether there is a child, whether there is a car, whether there is an old person, N insurance suitability is selected from M insurance suitability, and recommended to the user, the user's the firstMatching the two characteristics with the N insurances to obtain the matching probability of each insurance in the N insurances suitable for the user, and marking as PMN(N ═ 1,2,3 … …). Wherein the N insurance types have their own weights, e.g. theta for severe insuranceMNCan be 50%, and has anticancer effectMNWhich may be 20% … … the user has a reasonable amount to buy the nth insurance cost: wN＝PMNθMNW×β/(PM1+PM2+...+PMN)。
In an embodiment of the present application, the total resource W may be a total number of the cache information, and may find, for the user, N appropriate cache ways from M cache ways according to a type of the cache information, where a matching probability of the N cache ways and the cache information is PMNThe Nth buffer mode has a weight of θMNThen, the amount of information that the user allocates the cache information to the nth caching mode is: wN＝PMNθMNW×β/(PM1+PM2+...+PMN)。
In one embodiment of the present application, the value of β may be 10%.
In one embodiment of the present application, the amount of money used to purchase insurance in a household may be 10% of the annual income, while maintaining family health and not affecting family life.
The following describes embodiments of an apparatus of the present application, which may be used to perform the information recommendation method in the above embodiments of the present application. For details that are not disclosed in the embodiments of the apparatus of the present application, please refer to the embodiments of the information recommendation method described above in the present application.
Fig. 3 schematically shows a block diagram of an information recommendation device according to an embodiment of the present application.
Referring to fig. 3, an information recommendation apparatus 300 according to an embodiment of the present application includes an obtaining module 301, a pre-recommending module 302, and a recommending module 303.
In some embodiments of the present application, based on the foregoing solution, the obtaining module 301 is configured to obtain a user database, obtain a first feature of each user in the user database, and determine, according to the first feature of each user in the user database, one or more pre-recommended users in the user database through a neural network model; the pre-recommendation module 302 is configured to, for each pre-recommendation user, obtain a second feature of the pre-recommendation user, input the second feature of the pre-recommendation user into one or more decision tree models, and obtain a pre-recommendation result output by each decision tree model, where each decision tree model corresponds to one type of pre-recommendation information; the recommending module 303 is configured to select pre-recommending information according to the pre-recommending result and the second characteristic of the pre-recommending user and combine the pre-recommending information to obtain recommending information.
In some embodiments of the present application, based on the foregoing, the pre-recommendation module 302 is configured to: acquiring second characteristics of the pre-recommendation information, wherein the second characteristics of the pre-recommendation information comprise one or more factors; and establishing a decision tree model corresponding to the pre-recommendation information according to the weight of one or more factors of the second characteristic of the pre-recommendation information in the pre-recommendation information.
In some embodiments of the present application, based on the foregoing, the second characteristic of the pre-recommended user includes one or more factors; pre-recommendation module 302 is configured to: inputting one or more factors in the second characteristics of the pre-recommended user into each decision tree model; and obtaining the matching probability between the pre-recommended user output by each decision tree model and the pre-recommended information corresponding to the decision tree model based on the second characteristics of the pre-recommended user and the second characteristics of the pre-recommended information in each decision tree model.
In some embodiments of the present application, based on the foregoing, the recommendation module 303 is configured to: and using the pre-recommendation information as recommendation information.
In some embodiments of the present application, based on the foregoing, the recommendation module 303 is configured to: the second characteristic of the pre-recommended user comprises total resources W; recording the pre-recommendation information preset weight corresponding to the Mth decision tree model as thetaM(M ═ 1,2,3 … M), the matching probability of the mth decision tree model output is noted as PM(ii) a Selecting N pieces of pre-recommendation information from the pre-recommendation information corresponding to the M model trees, and determining the N piece of pre-recommendation information corresponding to the selected N piece of pre-recommendation informationThe matching probability output by the policy tree model is recorded as PMN(N ═ 1,2,3 … …); by the formula: wN＝PMNθMW×β/(PM1+PM2+...+PMN) Obtaining the N-th pre-recommended information suggestion allocation resource WMWherein β is a preset coefficient.
In some embodiments of the present application, the recommendation module 303 is configured to β having a value of 10% based on the foregoing.
As will be appreciated by one skilled in the art, aspects of the present application may be embodied as a system, method or program product. Accordingly, various aspects of the present application may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
An electronic device 40 according to this embodiment of the present application is described below with reference to fig. 4. The electronic device 40 shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 4, electronic device 40 is embodied in the form of a general purpose computing device. The components of electronic device 40 may include, but are not limited to: the at least one processing unit 41, the at least one memory unit 42, a bus 43 connecting different system components (including the memory unit 42 and the processing unit 41), and a display unit 44.
Wherein the storage unit stores program code executable by the processing unit 41 to cause the processing unit 41 to perform the steps according to various exemplary embodiments of the present application described in the section "example methods" above in this specification.
The storage unit 42 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)421 and/or a cache memory unit 422, and may further include a read only memory unit (ROM) 423.
The storage unit 42 may also include a program/utility 424 having a set (at least one) of program modules 425, such program modules 425 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
Bus 43 may be one or more of any of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
Electronic device 40 may also communicate with one or more external devices (e.g., keyboard, pointing device, Bluetooth device, etc.), and also with one or more devices that enable a user to interact with electronic device 40, and/or with any device (e.g., router, modem, etc.) that enables electronic device 40 to communicate with one or more other computing devices.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to make a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) execute the method according to the embodiments of the present application.
There is also provided, in accordance with an embodiment of the present application, a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, various aspects of the present application may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps according to various exemplary embodiments of the present application described in the "exemplary methods" section above of this specification, when the program product is run on the terminal device.
Referring to fig. 5, a program product 50 for implementing the above method according to an embodiment of the present application is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present application is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including AN object oriented programming language such as Java, C + +, or the like, as well as conventional procedural programming languages, such as the "C" language or similar programming languages.
Furthermore, the above-described figures are merely schematic illustrations of processes involved in methods according to exemplary embodiments of the present application, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims (9)

1. An information recommendation method, comprising:
acquiring a user database, acquiring first characteristics of each user in the user database, and determining pre-recommended users in the user database through a neural network model according to the first characteristics of each user in the user database, wherein the pre-recommended users are one or more;
for each pre-recommended user, obtaining second characteristics of the pre-recommended user, inputting the second characteristics of the pre-recommended user into one or more decision tree models, and obtaining a pre-recommended result output by each decision tree model, wherein each decision tree model corresponds to one type of pre-recommended information;
and selecting the pre-recommendation information to be combined according to the pre-recommendation result and the second characteristics of the pre-recommendation users to obtain recommendation information.
2. The information recommendation method according to claim 1, wherein before inputting the second feature of the pre-recommended user into one or more decision tree models, the method comprises:
acquiring second characteristics of the pre-recommendation information, wherein the second characteristics of the pre-recommendation information comprise one or more factors;
and establishing a decision tree model corresponding to the pre-recommendation information according to the weight of one or more factors of the second characteristic of the pre-recommendation information in the pre-recommendation information.
3. The information recommendation method according to claim 2, wherein the second characteristic of the pre-recommended user includes one or more factors;
inputting the second characteristics of the pre-recommendation user into one or more decision tree models to obtain a pre-recommendation result output by each decision tree model, wherein the pre-recommendation result comprises:
inputting one or more factors in the second characteristics of the pre-recommended user into each decision tree model;
and obtaining the matching probability between the pre-recommended user output by each decision tree model and the pre-recommended information corresponding to the decision tree model based on the second characteristics of the pre-recommended user and the second characteristics of the pre-recommended information in each decision tree model.
4. The information recommendation method according to claim 1, wherein the selecting the pre-recommendation information according to the pre-recommendation result and the second characteristic of the pre-recommendation user to combine to obtain recommendation information comprises:
and taking the pre-recommendation information as the recommendation information.
5. The information recommendation method according to claim 4, wherein the combining the selected pre-recommendation information according to the pre-recommendation result and the second characteristic of the pre-recommendation user to obtain recommendation information comprises:
the second characteristic of the pre-recommended user comprises total resources W;
recording the pre-recommendation information preset weight corresponding to the Mth decision tree model as thetaM(M ═ 1,2,3 … M), the matching probability of the mth decision tree model output is noted as PM；
Selecting N pre-recommendation information from pre-recommendation information corresponding to M model trees, and recording the matching probability output by a decision tree model corresponding to the Nth pre-recommendation information as PMN(N＝1,2,3……)；
By the formula: wN＝PMNθMW×β/(PM1+PM2+...+PMN) Obtaining the N-th pre-recommended information suggestion allocation resource WMWherein β is a preset coefficient.
6. The information recommendation method according to claim 5, wherein said value of β is 10%.
7. An information recommendation apparatus, comprising:
the acquisition module is used for acquiring a user database, acquiring first characteristics of each user in the user database, and determining pre-recommended users in the user database through a neural network model according to the first characteristics of each user in the user database, wherein the pre-recommended users are one or more pre-recommended users;
the pre-recommendation module is used for acquiring second characteristics of each pre-recommended user, inputting the second characteristics of the pre-recommended users into one or more decision tree models, and obtaining pre-recommendation results output by each decision tree model, wherein each decision tree model corresponds to one type of pre-recommendation information;
and the recommending module selects the pre-recommending information to combine to obtain recommending information according to the pre-recommending result and the second characteristics of the pre-recommending users.
8. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out the method of any one of claims 1 to 6.
9. A computer program medium having computer readable instructions stored thereon which, when executed by a processor of a computer, cause the computer to perform the method of any one of claims 1-6.
CN202010223996.8A 2020-03-26 2020-03-26 Information recommendation method and device, electronic equipment and medium Pending CN111460286A (en)

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