CN112702179A - Package recommendation method and device - Google Patents

Package recommendation method and device Download PDF

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Publication number
CN112702179A
CN112702179A CN202011510279.XA CN202011510279A CN112702179A CN 112702179 A CN112702179 A CN 112702179A CN 202011510279 A CN202011510279 A CN 202011510279A CN 112702179 A CN112702179 A CN 112702179A
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China
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target
package
user
packages
users
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CN202011510279.XA
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Chinese (zh)
Inventor
王蕾
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China United Network Communications Group Co Ltd
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China United Network Communications Group Co Ltd
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Priority to CN202011510279.XA priority Critical patent/CN112702179A/en
Publication of CN112702179A publication Critical patent/CN112702179A/en
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00—Data switching networks
    • H04L12/02—Details
    • H04L12/14—Charging, metering or billing arrangements for data wireline or wireless communications
    • H04L12/1485—Tariff-related aspects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0631—Item recommendations
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M15/00—Arrangements for metering, time-control or time indication ; Metering, charging or billing arrangements for voice wireline or wireless communications, e.g. VoIP
    • H04M15/70—Administration or customization aspects; Counter-checking correct charges
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/24—Accounting or billing

Abstract

The embodiment of the invention provides a package recommendation method and device, relates to the technical field of communication, and aims to improve package recommendation reasonability and save package recommendation resources of operators. The method comprises the following steps: determining the probability of a target user purchasing a target package based on a preset model; and under the condition that the probability that the target user purchases the target package is greater than or equal to the purchase threshold corresponding to the target package, determining to recommend the target package to the target user.

Description

Package recommendation method and device
Technical Field
The embodiment of the invention relates to the technical field of communication, in particular to a package recommendation method and device.
Background
Currently, an operator may recommend a communication package to a user by a method of acquiring a user demand or counting historical data of the user. For example, the operator determines that the user 1 needs at least 1000 minutes per month of call duration by acquiring the user demand, and assumes that the call duration included in the package a is 1000 minutes per month, so that the package a can be recommended to the user 1.
However, in the above method, there may be a risk of package recommendation failure, that is, after the package a is recommended to the user 1, the user 1 does not purchase the package a but purchases other packages (for example, package B).
Disclosure of Invention
The embodiment of the invention provides a package recommendation method and device, which can improve the package recommendation rationality and save package recommendation resources of operators.
In a first aspect, an embodiment of the present invention provides a package recommendation method, including: determining the probability of a target user purchasing a target package based on a preset model; and under the condition that the probability that the target user purchases the target package is greater than or equal to the purchase threshold corresponding to the target package, determining to recommend the target package to the target user.
Optionally, the package recommendation method further includes: acquiring user characteristics of a plurality of users and purchase results of target packages corresponding to the users, wherein the user characteristics of one user comprise the age of the user, the package types purchased by the user and the network access duration of the user, and the purchase results of the target packages comprise two results of purchasing the target packages and not purchasing the target packages; and training the user characteristics of the users and the purchase results of the target packages corresponding to the users to generate the preset model.
Optionally, the target package is a 5G package, and the package recommendation method further includes: and determining to recommend the target package to the target user under the condition that the terminal corresponding to the target user is the 5G terminal.
Optionally, the package recommendation method further includes: and sending alarm information under the condition that the probability of the target user purchasing the target package is smaller than the purchase threshold, wherein the alarm information is used for recommending other packages to the target user, and the other packages are packages except the target package in the plurality of packages.
In a second aspect, an embodiment of the present invention provides a package recommendation apparatus, including: a determination module; the determining module is used for determining the probability of the target user purchasing the target package based on a preset model; the determining module is further configured to determine to recommend the target package to the target user when the probability that the target user purchases the target package is greater than or equal to a purchase threshold corresponding to the target package.
Optionally, the package recommending apparatus further includes an obtaining module, configured to obtain user characteristics of each of the multiple users and a purchase result of a target package corresponding to each of the multiple users, where the user characteristics of one user include an age of the user, a type of package that the user has purchased, and a duration of network entry of the user, and the purchase result of the target package includes two results of purchasing the target package and not purchasing the target package; the determining module is further configured to train user characteristics of the plurality of users and purchase results of target packages corresponding to the plurality of users, and generate the preset model.
Optionally, the target package is a 5G package, and the determining module is further configured to determine to recommend the target package to the target user when the terminal corresponding to the target user is a 5G terminal.
Optionally, the package recommending apparatus further includes an alarm module, where the alarm module is configured to send alarm information when a probability that the target user purchases the target package is smaller than the purchase threshold, where the alarm information is used to recommend another package to the target user, and the another package is a package other than the target package in the plurality of packages.
In a third aspect, an embodiment of the present invention provides another package recommendation apparatus, including: a processor, a memory, a bus, and a communication interface; the memory is used for storing computer execution instructions, the processor is connected with the memory through a bus, and when the package recommendation device runs, the processor executes the computer execution instructions stored in the memory, so that the package recommendation device executes the package recommendation method provided by the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which includes a computer program, and when the computer program runs on a computer, the computer is caused to execute a package recommendation method provided in the first aspect.
In a fifth aspect, an embodiment of the present invention provides a computer program product containing instructions, which, when run on a computer, causes the computer to execute the package recommendation method of the first aspect and any implementation manner thereof.
According to the package recommendation method and device provided by the embodiment of the invention, the package recommendation device firstly determines the probability of a target user purchasing a target package based on a preset model, and then determines to recommend the target package to the target user under the condition that the probability of the target user purchasing the target package is greater than or equal to a purchase threshold corresponding to the target package. In the embodiment of the invention, the package recommending device can accurately and effectively determine the possibility of the target user for purchasing the target package based on the preset model, so that when the purchase possibility is high, namely the purchase probability is greater than or equal to the purchase threshold, the target package can be determined to be recommended to the target user, the package recommending rationality can be improved, and the package recommending resources of an operator can be saved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below.
Fig. 1 is a hardware schematic diagram of a server according to an embodiment of the present invention;
fig. 2 is a first schematic diagram of a package recommendation method according to an embodiment of the present invention;
fig. 3 is a second schematic diagram of a package recommendation method according to an embodiment of the present invention;
fig. 4 is a third schematic diagram of a package recommendation method according to an embodiment of the present invention;
fig. 5 is a first schematic structural diagram of a package recommendation device according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a package recommendation device according to an embodiment of the present invention.
Detailed Description
The package recommendation method and device provided by the embodiment of the invention will be described in detail below with reference to the accompanying drawings.
Furthermore, the terms "including" and "having," and any variations thereof, as referred to in the description of the present application, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements but may alternatively include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that, in the embodiments of the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "e.g.," an embodiment of the present invention is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts related in a concrete fashion.
The term "and/or" as used herein includes the use of either or both of the two methods.
In the description of the present application, the meaning of "a plurality" means two or more unless otherwise specified.
Based on the problems in the background art, the embodiments of the present invention provide a package recommendation method and apparatus, where a package recommendation apparatus first determines, based on a preset model, a probability that a target user purchases a target package, and then determines to recommend the target package to the target user when the probability that the target user purchases the target package is greater than or equal to a purchase threshold corresponding to the target package. In the embodiment of the invention, the package recommending device can accurately and effectively determine the possibility of the target user for purchasing the target package based on the preset model, so that when the purchase possibility is high, namely the purchase probability is greater than or equal to the purchase threshold, the target package can be determined to be recommended to the target user, the package recommending rationality can be improved, and the package recommending resources of an operator can be saved.
An embodiment of the present invention provides a package recommendation apparatus, which may be a server, and fig. 1 is a hardware schematic diagram of a server for executing the package recommendation method provided in the embodiment of the present invention, as shown in fig. 1, the server 10 may include a processor 101, a memory 102, a network interface 103, and the like.
The processor 101 is a core component of the server 10, and the processor 101 is configured to run an operating system of the server 10 and application programs (including a system application program and a third-party application program) on the server 10, so as to implement the package recommendation method performed by the server 10.
In this embodiment, the processor 101 may be a Central Processing Unit (CPU), a microprocessor, a Digital Signal Processor (DSP), an application-specific integrated circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof, which is capable of implementing or executing various exemplary logic blocks, modules, and circuits described in connection with the disclosure of the embodiment of the present invention; a processor may also be a combination of computing functions, e.g., comprising one or more microprocessors, a DSP and a microprocessor, or the like.
Optionally, the processor 101 of the server 10 includes one or more CPUs, which are single-core CPUs (single-CPUs) or multi-core CPUs (multi-CPUs).
The memory 102 includes, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical memory, or the like. The memory 102 holds the code of the operating system.
Optionally, the processor 101 reads an instruction stored in the memory 102 to implement the package recommendation method in the embodiment of the present invention, or the processor 101 implements the package recommendation method provided in the embodiment of the present invention by using an instruction stored inside. When the processor 101 reads the instructions stored in the memory to execute the package recommendation method provided by the embodiment of the present invention, the memory stores the instructions for implementing the package recommendation method provided by the embodiment of the present invention.
The network interface 103 is a wired interface, such as a Fiber Distributed Data Interface (FDDI) interface or a Gigabit Ethernet (GE) interface. Alternatively, the network interface 103 is a wireless interface. The network interface 103 is used for the server 10 to communicate with other devices.
The memory 102 is used for storing user characteristics of each of a plurality of users and purchase results of target packages corresponding to each of the plurality of users. The at least one processor 101 further executes the method according to the embodiment of the present invention according to the user characteristics of each of the plurality of users and the purchase result of the target package corresponding to each of the plurality of users stored in the memory 102. For more details of the above functions implemented by the processor 101, reference is made to the following description of various method embodiments.
Optionally, the server 10 further includes a bus, and the processor 101 and the memory 102 are connected to each other through the bus 104, or in other manners.
Optionally, the server 10 further comprises an input/output interface 105, wherein the input/output interface 105 is configured to connect to an input device, and receive a package recommendation request input by a user through the input device. Input devices include, but are not limited to, a keyboard, a touch screen, a microphone, and the like. The input/output interface 105 is also used for connecting with an output device, and outputting a package recommendation result of the processor 101 (i.e., determining whether to recommend a target package to a target user). Output devices include, but are not limited to, a display, a printer, and the like.
The package recommendation method and device provided by the embodiment of the invention are applied to an application scene that operator equipment (namely a package recommendation device) recommends packages (such as communication packages including traffic, conversation and the like) for users. When the package recommending device obtains the user characteristics of a certain user (for example, a target user), the probability that the target user purchases a target package may be determined based on a preset model, and then whether to recommend the target package to the target user is determined.
As shown in fig. 2, the package recommendation method provided by the embodiment of the present invention may include S101-S102.
S101, determining the probability of the target user purchasing the target package based on a preset model.
It should be understood that the target user may be one of at least one user (specifically, at least one user who has not recommended the target package), the target package may be one of a plurality of packages provided by the operator, and the probability that the target user purchases the target package may be understood as the probability that the target user purchases the target package, for example, assuming that the probability that the target user purchases the target package is 0.5, it indicates that the target user has a 50% probability of purchasing the target package.
In an implementation manner of the embodiment of the present invention, in the above S101, the package recommending apparatus may be directly used when the preset model is trained, that is, the probability that the target user purchases the target package is determined based on the preset model.
In another implementation manner of the embodiment of the present invention, the package recommendation device may further train related data to obtain a preset model. Specifically, as shown in fig. 3, the package recommendation method provided by the embodiment of the present invention further includes S201-S202.
S201, obtaining user characteristics of a plurality of users and purchase results of target packages corresponding to the users.
The user characteristics of a user comprise the age of the user, the type of packages purchased by the user and the network access duration of the user, and the purchase result of the target package comprises two results of purchasing the target package and not purchasing the target package.
It should be understood that the plurality of users are users who have already recommended the target package, and therefore, the plurality of users may correspond to the above two purchase results, that is, some users purchase the target package after being recommended, and some users choose to forgo purchasing the target package after being recommended.
Optionally, the user characteristics of the user may further include the gender of the user, the city where the user is located, the fee of the package purchased by the user, and the like.
S202, training the user characteristics of the users and the purchase results of the target packages corresponding to the users to generate a preset model.
In the embodiment of the present invention, the package recommendation apparatus may determine, according to the purchase result of the target package corresponding to each of the plurality of users, a (a > 0) users who purchase the target package and B (B > 0) users who do not purchase the target package, then select a certain number of users from the a users by the package recommendation apparatus, and train through a machine learning algorithm by using features of the certain number of users as positive samples, and select users of the same number as the certain number of users from the B users and by using features of the same number of users as negative samples, so as to obtain the preset model.
Alternatively, the machine learning algorithm may be a logistic regression algorithm, a Support Vector Machine (SVM) algorithm, an XGboost algorithm, a Gradient Boosting Decision Tree (GBDT) GBDT algorithm, and the like.
It should be noted that, in S101, the process of determining, by the package recommending apparatus, the probability of the target user purchasing the target package based on the preset model may be specifically understood that, after the package recommending apparatus obtains the user characteristics of the target user, that is, the age, the purchased package type, and the duration of network access of the target user, the probability of the target user purchasing the target package may be determined based on the user characteristics of the target user and the preset model.
S102, under the condition that the probability that the target user purchases the target package is larger than or equal to the purchase threshold corresponding to the target package, the target package is determined to be recommended to the target user.
It can be understood that the purchase threshold corresponding to the target package is a threshold used by the package recommendation device for determining whether to recommend the target package to the target user, and when the probability that the target user purchases the target package is greater than or equal to the purchase threshold, it indicates that the target user has a high possibility of purchasing the target package, and the package recommendation device can determine to recommend the target package to the target user; when the probability that the target user purchases the target package is smaller than the purchase threshold, which indicates that the target user has a low possibility of purchasing the target package, the package recommendation device may determine to perform other operations.
According to the package recommendation method and device provided by the embodiment of the invention, the package recommendation device firstly determines the probability of a target user purchasing a target package based on a preset model, and then determines to recommend the target package to the target user under the condition that the probability of the target user purchasing the target package is greater than or equal to a purchase threshold corresponding to the target package. In the embodiment of the invention, the package recommending device can accurately and effectively determine the possibility of the target user for purchasing the target package based on the preset model, so that when the purchase possibility is high, namely the purchase probability is greater than or equal to the purchase threshold, the target package can be determined to be recommended to the target user, the package recommending rationality can be improved, and the package recommending resources of an operator can be saved.
In an implementation manner of the embodiment of the present invention, the target package may be a 5G package, and before the step S101, the package recommendation method provided in the embodiment of the present invention may further include a step a.
And step A, under the condition that the terminal corresponding to the target user is the 5G terminal, determining to recommend the target package to the target user.
It can be understood that, when the terminal corresponding to (or used by) the target user is a 5G terminal, it indicates that the target user has a need for the 5G package, and at this time, when the target package is the 5G package, the target package may be directly recommended to the target user.
Otherwise, in the case that the terminal corresponding to the target user is the 4G terminal, the package recommending apparatus may determine whether to recommend the target package (or the 5G package) to the target user based on steps S101-S102.
Based on fig. 2, as shown in fig. 4, after S101, the package recommendation method provided by the embodiment of the present invention further includes S103.
S103, sending out alarm information under the condition that the probability of the target user purchasing the target package is smaller than a purchase threshold value.
The warning information is used for recommending other packages to the target user, and the other packages are packages except the target package in the plurality of packages.
With reference to the above description of the embodiments, it should be understood that when the probability that the target user purchases the target package is smaller than the purchase threshold, it indicates that the target user has a lower possibility of purchasing the target package, so that the package recommendation apparatus may issue an alarm message, i.e., recommend another package to the target user.
It should be noted that, for the process of recommending other packages to the target user by the package recommending apparatus, reference may be made to the process of recommending the target package to the target user in the foregoing embodiment, which is the same or of the same type in principle, and details are not described here.
In the embodiment of the present invention, the package recommendation apparatus and the like may be divided into functional modules according to the method example, for example, each functional module may be divided for each function, or two or more functions may be integrated into one processing module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. It should be noted that, the division of the modules in the embodiment of the present invention is schematic, and is only a logic function division, and there may be another division manner in actual implementation.
In the case of dividing the function modules according to the respective functions, fig. 5 shows a schematic diagram of a possible structure of the package recommendation device in the above embodiment, and as shown in fig. 5, the package recommendation device 20 may include: a determination module 201.
The determining module 201 is configured to determine, based on a preset model, a probability that a target user purchases a target package.
And the determining module is further used for determining to recommend the target package to the target user under the condition that the probability that the target user purchases the target package is greater than or equal to the purchase threshold corresponding to the target package.
Optionally, the package recommending apparatus 20 further includes an obtaining module 202.
An obtaining module 202, configured to obtain user characteristics of each of a plurality of users and a purchase result of a target package corresponding to each of the plurality of users, where the user characteristics of one user include an age of the user, a package type purchased by the user, and a duration of network entry of the user, and the purchase result of the target package includes two results of purchasing the target package and not purchasing the target package.
The determining module 201 is further configured to train user characteristics of the multiple users and purchase results of target packages corresponding to the multiple users, and generate the preset model.
Optionally, the target package is a 5G package.
The determining module 201 is further configured to determine to recommend the target package to the target user when the terminal corresponding to the target user is a 5G terminal.
Optionally, package recommendation device 20 further comprises an alert module 203.
And an alarm module 203, configured to send alarm information when the probability that the target user purchases the target package is smaller than the purchase threshold, where the alarm information is used to recommend other packages to the target user, and the other packages are packages other than the target package in the multiple packages.
Fig. 6 shows a schematic diagram of a possible configuration of the package recommendation device according to the above-described embodiment, in the case of an integrated unit. As shown in fig. 6, the package recommendation apparatus 30 may include: a processing module 301 and a communication module 302. The processing module 301 may be configured to control and manage actions of the package recommendation apparatus 30, for example, the processing module 301 may be configured to support the package recommendation apparatus 30 to execute S101 in the above method embodiment. Communication module 302 may be used to support communication of package recommendation device 30 with other entities. Optionally, as shown in fig. 6, the package recommendation apparatus 30 may further include a storage module 303 for storing program codes and data of the package recommendation apparatus 30.
The processing module 301 may be a processor or a controller (e.g., the processor 101 shown in fig. 1). The communication module 302 may be a transceiver, a transceiver circuit, or a communication interface, etc. (e.g., may be the network interface 103 shown in fig. 1 described above). The storage module 303 may be a memory (e.g., may be the memory 102 described above with reference to fig. 1).
When the processing module 301 is a processor, the communication module 302 is a transceiver, and the storage module 503 is a memory, the processor, the transceiver, and the memory may be connected by a bus. The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc.
It should be understood that, in various embodiments of the present invention, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
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, 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 the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented using a software program, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The procedures or functions described in accordance with the embodiments of the invention are all or partially effected when the computer program instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website, computer, server, or data center to another website, computer, server, or data center via wire (e.g., coaxial cable, fiber optics, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or can comprise one or more data storage devices, such as a server, a data center, etc., that can be integrated with the medium. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention 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 invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (10)

1. A package recommendation method, the method comprising:
determining the probability of a target user purchasing a target package based on a preset model;
and under the condition that the probability that the target user purchases the target package is greater than or equal to the purchase threshold corresponding to the target package, determining to recommend the target package to the target user.
2. The method of claim 1, further comprising:
acquiring user characteristics of a plurality of users and purchase results of target packages corresponding to the users, wherein the user characteristics of one user comprise the age of the user, the package types purchased by the user and the network entry duration of the user, and the purchase results of the target packages comprise two results of purchasing the target packages and not purchasing the target packages;
and training the user characteristics of the users and the purchase results of the target packages corresponding to the users to generate the preset model.
3. The method of claim 2, wherein the target package is a 5G package, and before the determining the probability of the target user purchasing the target package based on the preset model, the method further comprises:
and under the condition that the terminal corresponding to the target user is a 5G terminal, determining to recommend the target package to the target user.
4. The method according to any one of claims 1-3, further comprising:
and sending alarm information under the condition that the probability of the target user purchasing the target package is smaller than the purchase threshold, wherein the alarm information is used for recommending other packages to the target user, and the other packages are packages except the target package in the plurality of packages.
5. A package recommendation apparatus, characterized in that the apparatus comprises a determination module;
the determining module is used for determining the probability of the target user purchasing the target package based on a preset model;
the determining module is further configured to determine to recommend the target package to the target user when the probability that the target user purchases the target package is greater than or equal to a purchase threshold corresponding to the target package.
6. The package recommendation device of claim 5, further comprising an acquisition module;
the acquisition module is used for acquiring user characteristics of a plurality of users and purchase results of target packages corresponding to the users, wherein the user characteristics of one user comprise the age of the user, the package types purchased by the user and the network access duration of the user, and the purchase results of the target packages comprise two results of purchasing the target packages and not purchasing the target packages;
the determining module is further configured to train user characteristics of the users and purchase results of the target packages corresponding to the users, and generate the preset model.
7. The package recommendation device of claim 6, wherein said target package is a 5G package;
the determining module is further configured to determine to recommend the target package to the target user when the terminal corresponding to the target user is a 5G terminal.
8. The package recommendation device according to any one of claims 5-7, wherein said device further comprises an alarm module;
the warning module is configured to send warning information when the probability that the target user purchases the target package is smaller than the purchase threshold, where the warning information is used to recommend other packages to the target user, and the other packages are packages other than the target package in the plurality of packages.
9. A package recommendation device, characterized in that the package recommendation device comprises: a processor, a memory, a bus, and a communication interface; the memory is used for storing computer-executable instructions, and when the package recommendation device runs, the processor executes the computer-executable instructions stored in the memory, so that the package recommendation device executes the package recommendation method according to any one of claims 1 to 4.
10. A computer-readable storage medium comprising a computer program which, when run on a computer, causes the computer to perform the package recommendation method of any one of claims 1 to 4.
CN202011510279.XA 2020-12-18 2020-12-18 Package recommendation method and device Pending CN112702179A (en)

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WO2016180182A1 (en) * 2015-10-30 2016-11-17 中兴通讯股份有限公司 Service package recommendation method and device
CN109995837A (en) * 2018-01-02 2019-07-09 中国移动通信有限公司研究院 A kind of service package recommended method, device and server
CN111047406A (en) * 2019-12-12 2020-04-21 北京思特奇信息技术股份有限公司 Telecommunication package recommendation method, device, storage medium and equipment

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Publication number Priority date Publication date Assignee Title
WO2016180182A1 (en) * 2015-10-30 2016-11-17 中兴通讯股份有限公司 Service package recommendation method and device
CN109995837A (en) * 2018-01-02 2019-07-09 中国移动通信有限公司研究院 A kind of service package recommended method, device and server
CN111047406A (en) * 2019-12-12 2020-04-21 北京思特奇信息技术股份有限公司 Telecommunication package recommendation method, device, storage medium and equipment

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Application publication date: 20210423