CN111598643B - Information processing method and device - Google Patents
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- 230000010365 information processing Effects 0.000 title claims abstract description 40
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- 238000012545 processing Methods 0.000 claims abstract description 108
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Abstract
The application provides an information processing method and device, wherein the method comprises the following steps: acquiring historical order information, wherein the historical order information comprises at least one order information; processing the historical order information to determine processing elements; acquiring first order information of a first user; and determining a target recommendation mode according to the processing element and the first order information. The method is used for improving the accuracy of determining the target recommendation mode, so that the target recommendation mode meets the requirements of users.
Description
Technical Field
The present disclosure relates to the field of computers, and in particular, to an information processing method and apparatus.
Background
At present, an order bubbling conversion adjustment method can be adopted in transportation or logistics to recommend a recommendation mode (such as red packages, discount coupons, membership integration and the like) for a user, and the success rate of ordering of the user is improved through the recommendation mode.
In the related art, the method for improving the ordering success rate of the user comprises the following steps: and the operation and maintenance personnel manually recommend a recommendation mode to the user according to own experience. In the method, a recommendation mode is manually recommended to the user according to own experience, and the recommendation mode recommended to the user possibly does not meet the user requirement, so that the ordering success rate of the user is low.
Disclosure of Invention
The application provides an information processing method and device, which improve the accuracy of determining a target recommendation mode, enable the target recommendation mode to meet the requirement of a user, and further improve the success probability of the ordering of the user.
In a first aspect, the present application provides an information processing method, including:
acquiring historical order information, wherein the historical order information comprises at least one order information;
processing the historical order information to determine processing elements;
acquiring first order information of a first user;
and determining a target recommendation mode according to the processing element and the first order information.
In one possible implementation manner, the determining a target recommendation mode according to the processing element and the first order information includes:
acquiring at least one preset recommendation mode;
under the at least one preset recommendation mode, processing the first order information through an enhanced learning model with the processing elements to obtain the corresponding order placing probability of each preset recommendation mode;
and determining a target recommendation mode in the at least one preset recommendation mode according to the ordering probability.
In one possible implementation, the processing the historical order information to determine a processing element includes:
determining sample order information in at least one order information included in the historical order information;
performing feature extraction processing on the sample order information to obtain at least one sample feature;
the processing element is determined based on the at least one sample characteristic.
In one possible embodiment, the at least one sample feature comprises a sample time feature, a sample user feature, and a sample area feature; determining the processing element from the at least one sample feature comprises:
determining an environmental state element from the sample time feature, the sample user feature, and the sample area feature;
and determining the processing element according to the environment state element, the preset individual action element and the preset environment rewarding element.
In one possible embodiment, determining the processing element according to the environmental status element, a preset individual action element, and a preset environmental reward element includes:
and carrying out combination processing on the environment state element, the preset individual action element and the preset environment rewarding element to obtain the processing element.
In one possible implementation, determining sample order information in at least one order information included in the historical order information includes:
acquiring effective order information from at least one order information included in the historical order information;
and determining the effective order information as sample order information.
In a possible implementation manner, the order information includes an order type; the step of obtaining valid order information from at least one order information included in the historical order information includes:
and removing order information of which the order types are a brush order and a wrong order from at least one piece of order information included in the historical order information to obtain the effective order information.
In a second aspect, the present application provides an information processing apparatus including: the device comprises an acquisition module and a determination module, wherein,
the acquisition module is used for acquiring historical order information, wherein the historical order information comprises at least one order information;
the determining module is used for processing the historical order information and determining processing elements;
the acquisition module is also used for acquiring first order information of a first user;
the determining module is further configured to determine a target recommendation mode according to the processing element and the first order information.
In a possible implementation manner, the determining module is further specifically configured to:
acquiring at least one preset recommendation mode;
under the at least one preset recommendation mode, processing the first order information through an enhanced learning model with the processing elements to obtain the corresponding order placing probability of each preset recommendation mode;
and determining a target recommendation mode in the at least one preset recommendation mode according to the ordering probability.
In one possible embodiment, the determining module is specifically configured to;
determining sample order information in at least one order information included in the historical order information;
performing feature extraction processing on the sample order information to obtain at least one sample feature;
the processing element is determined based on the at least one sample characteristic.
In one possible embodiment, the at least one sample feature comprises a sample time feature, a sample user feature, and a sample area feature; the determining module is specifically configured to:
determining an environmental state element from the sample time feature, the sample user feature, and the sample area feature;
and determining the processing element according to the environment state element, the preset individual action element and the preset environment rewarding element.
In one possible implementation manner, the determining module is specifically configured to:
and carrying out combination processing on the environment state element, the preset individual action element and the preset environment rewarding element to obtain the processing element.
In one possible implementation manner, the determining module is specifically configured to:
acquiring effective order information from at least one order information included in the historical order information;
and determining the effective order information as sample order information.
In one possible implementation manner, the determining module is specifically configured to:
and removing order information of which the order types are a brush order and a wrong order from at least one piece of order information included in the historical order information to obtain the effective order information.
In a third aspect, the present application provides an information processing apparatus including: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executing computer-executable instructions stored in the memory causes the at least one processor to perform the information processing method of any one of the first aspects.
In a fourth aspect, the present application provides an information processing apparatus including: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executing computer-executable instructions stored in the memory causes the at least one processor to perform the information processing method of any of the second aspects.
The information processing method and device provided by the application comprise the following steps: acquiring historical order information, wherein the historical order information comprises at least one order information; processing the historical order information to determine processing elements; acquiring first order information of a first user; and determining a target recommendation mode according to the processing element and the first order information. According to the method, the target recommendation mode is determined according to the processing element and the first order information, so that accuracy of determining the target recommendation mode can be improved, the target recommendation mode meets requirements of users, and further the successful probability of ordering of the users and the income of an application platform are improved.
Drawings
For a clearer description of the technical solutions of the present application or of the prior art, the drawings that are used in the description of the embodiments or of the prior art will be briefly described, it being obvious that the drawings in the description below are some embodiments of the invention, and that other drawings can be obtained from them without inventive effort for a person skilled in the art.
Fig. 1 is a schematic view of an application scenario of an information processing method provided in the present application;
FIG. 2 is a schematic flow chart of an information processing method provided in the present application;
FIG. 3 is a second flow chart of the information processing method provided in the present application;
FIG. 4 is a schematic diagram of an information processing apparatus according to the present application;
fig. 5 is a schematic hardware structure of the information processing apparatus provided in the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the present application more apparent, the technical solutions in the present application will be clearly and completely described below with reference to the drawings in the present application, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Fig. 1 is a schematic application scenario diagram of an information processing method provided in the present application. As shown in fig. 1, includes: a server 101 and a client 102. Alternatively, the server 101 and the client 102 may be corresponding servers and clients in a certain application platform. Alternatively, the application platform may be a shopping platform, a transportation platform, or the like.
Specifically, the client 102 may be running on a terminal device held by a user. For example, the terminal device may be a computer device, a tablet computer, a mobile phone (or "cellular" phone), etc., and the terminal device may also be a portable, pocket, hand-held, computer-built-in mobile device or apparatus, without any particular limitation. It should be noted that, in fig. 1, 4 clients 102 are shown by way of example, and in practical application, the number of clients 102 is not particularly limited in the present application.
Alternatively, the client 102 may interact with the server 101 through a wired network, or a wireless network. For example, the wired network may include coaxial cable, twisted pair, optical fiber, etc., and the wireless network may be a 2G network, a 3G network, a 4G network, or a 5G network, a wireless fidelity (Wireless Fidelity, abbreviated as WIFI) network, etc. The specific type or specific form of the interaction is not limited in this application, as long as it can implement the interaction function between the server and the terminal device.
When the client 102 interacts with the server 101, the following procedure may be implemented: the client 102 may send the bubbling order information to the server 101, and the server 101 may process the bubbling order information through the reinforcement learning model with the processing element to obtain a target recommendation method, and send the target recommendation method to the client 102, where the bubbling order information is order information that the user has a purchase desire for the commodity. In the method, the bubbling order information is processed through the reinforcement learning model with the processing elements to obtain the target recommendation mode, so that the accuracy of determining the target recommendation mode can be provided, and the target recommendation mode meets the requirements of users.
The technical scheme shown in the application is described in detail through specific embodiments. It should be noted that the following embodiments may be combined with each other, and for the same or similar matters, the description will not be repeated in different embodiments.
Fig. 2 is a schematic flow chart of an information processing method provided in the present application. As shown in fig. 2, the information processing method provided in this embodiment includes:
s201, acquiring historical order information, wherein the historical order information comprises at least one order information.
Alternatively, the execution subject of the information processing method provided in the present application may be a server, and may be an information processing state set in the server, and the information processing apparatus may be implemented by a combination of software and/or hardware.
The historical order information is order information in a preset duration stored in the server. Alternatively, the preset duration may be 1 month, 2 months, 6 months, or the like, which is not limited in this application.
Specifically, the at least one order information includes valid order information and invalid order information, wherein the invalid order information includes dirty data such as order information generated by a user ordering error or order information caused by a network error, and the valid order information is order information except for the invalid order information in the at least one order information.
S202, processing the historical order information, and determining processing elements.
Optionally, processing the valid order information in the historical order information to obtain a processing element.
Wherein the processing elements are learning elements required for enhancing the learning model. Alternatively, the processing elements may include an environmental status element, an individual action element, an environmental reward element.
Specifically, processing valid order information in the historical order information can obtain an environmental status element. Wherein the individual action elements and the environmental reward elements are preset elements.
S203, acquiring first order information of a first user.
Specifically, the first order information is information included in the first order after bubbling by the order scheduling system. The order scheduling system is arranged in the server, and the first order information comprises time characteristic information, area characteristic information, passenger characteristic information and the like of the first user.
For example, when the order scheduling system is a traffic order scheduling system, the time feature information may include a riding time of the first user, a time interval from bubbling to issuing of the first order, a time period from bubbling to issuing of the first order, and the like, the area feature information may include whether a current position of the first user is a hot business turn, the number of surrounding vehicles, the total number of orders in an area where the first user is currently located, and the like, and the passenger feature information may include a probability of each preset recommendation mode in which the first user participates before, a probability of successful issuing of the first user in each preset recommendation mode before, a time interval from bubbling to issuing, a probability of conversion success, and the like.
In practical applications, the order scheduling system generally performs bubbling processing on orders according to the purchasing desire of the user, and the bubbled orders are usually orders with larger purchasing desire of the user. Alternatively, the server may determine the user's desire to purchase based on the time the user browses the order, the information query action performed on the order, and the like.
S204, determining a target recommendation mode according to the processing elements and the first order information.
In one possible implementation, at least one preset recommendation mode is obtained;
under at least one preset recommendation mode, processing the first order information through an enhanced learning model with processing elements to obtain the ordering probability corresponding to each preset recommendation mode;
and determining a target recommendation mode in at least one preset recommendation mode according to the ordering probability.
Specifically, the at least one preset recommendation mode is a recommendation mode pre-stored in the server, wherein the at least one preset recommendation mode can include a red envelope, a discount coupon, a membership point and the like.
Alternatively, the reinforcement learning model may be any one of a Q-learning model, a Multi-band model, and a Sara algorithm model.
Specifically, under at least one preset recommendation mode, the time feature information, the area feature information and the passenger feature information of the first user in the first order information are processed through an enhanced learning model with processing elements, so that the ordering probability corresponding to each preset recommendation mode is obtained.
Further, determining a corresponding preset recommendation mode when the ordering probability is maximum as a target recommendation mode.
In practice, after determining the target recommendation mode, the target recommendation mode may also be sent to the client, so that the terminal device with the client may display the target recommendation mode to the first user.
The information processing method provided in the embodiment includes: acquiring historical order information, wherein the historical order information comprises at least one order information; processing the historical order information to determine processing elements; acquiring first order information of a first user; and determining a target recommendation mode according to the processing element and the first order information. According to the method, the target recommendation mode is determined according to the processing element and the first order information, so that accuracy of determining the target recommendation mode can be improved, the target recommendation mode meets requirements of users, and further the successful probability of ordering of the users and the income of an application platform are improved.
Based on the foregoing embodiments, the information processing method provided in the present application is described in further detail below with reference to fig. 3, and specifically, please refer to fig. 3.
Fig. 3 is a flow chart of an information processing method provided in the present application. As shown in fig. 3, the information processing method provided in this embodiment includes:
s301, acquiring historical order information, wherein the historical order information comprises at least one order information.
Specifically, the execution method of S301 is the same as that of S201, and here, the execution process of S301 is not described again.
S302, determining sample order information in at least one order information included in the historical order information.
In one possible implementation, the valid order information is obtained from at least one order information included in the historical order information; valid order information is determined as specimen order information.
Specifically, the explanation of the valid order information can be referred to S201, and will not be repeated here.
In one possible implementation, the order information includes an order type; the step of acquiring valid order information from at least one order information included in the historical order information includes: and removing order information with the order types of the brush order and the error order from at least one piece of order information included in the historical order information to obtain effective order information.
Wherein, the wrong order user places order information generated by wrong order, or order information caused by network error, etc.
S303, carrying out feature extraction processing on the sample order information to obtain at least one sample feature, wherein the at least one sample feature comprises a sample time feature, a sample user feature and a sample area feature.
S304, determining the processing elements according to at least one sample characteristic.
In one possible embodiment, when the at least one sample characteristic includes a sample time characteristic, a sample user characteristic, and a sample area characteristic; determining a processing element from at least one sample feature, comprising: determining an environmental state element according to the sample time feature, the sample user feature and the sample area feature; and determining a processing element according to the environment state element, the preset individual action element and the preset environment rewarding element.
The sample time features may include a riding time of the sample user, a time interval from bubbling to issuing of an order, a time period from bubbling to issuing of the order, and the like, the sample area features may include whether a current position of the sample user is a popular business district, the number of surrounding vehicles, the total number of orders in an area where the sample user is currently located, and the like, and the sample user features may include a probability of each preset recommendation mode in which the sample user participates before, a probability of successful order placing of the sample user before in each preset recommendation mode, a time interval from bubbling to order placing, a probability of conversion success, and the like.
Specifically, the environmental state element S is obtained after processing the sample time feature, the sample user feature and the sample area feature by a preset processing algorithm. The preset individual action elements A are recommendation mode sets of { preset recommendation mode a1, preset recommendation mode a2, preset recommendation modes a3 and … … }. The preset environmental reward element R is a set including {0, 1}, where 0 indicates a failure of bubbling and 1 indicates a success of bubbling. It should be noted that the failure of bubbling means that the user has not issued an order, and the success of bubbling means that the user issued an order.
In a first possible embodiment, determining the processing element according to the environmental status element, the preset individual action element, and the preset environmental reward element includes: and carrying out combined processing on the environmental state element, the preset individual action element and the preset environmental rewarding element to obtain a processing element.
In the first possible embodiment, the processing element is a processing element set including an environmental status element, a preset individual action element, and a preset environmental reward element.
In a second possible embodiment, determining the processing element according to the environmental status element, the preset individual action element, and the preset environmental reward element includes: determining an individual policy element pi according to the environmental state element S and a preset individual action element A; determining a value element v according to the environmental state element S, the individual strategy element pi and the preset model element gamma π (S); determining the environmental state according to the environmental state element S and the preset individual action element AA state transition model element. Further, for the environmental status element S, the preset individual action element, the preset environmental reward element, the individual policy element pi and the value element v π (S) combining the preset model elements gamma and the environment state conversion model elements to obtain a processing element.
Optionally, in a second possible implementation, the exploration rate e may also be included. Further, for the environmental status element S, the preset individual action element, the preset environmental reward element, the individual policy element pi and the value element v π (S) combining a preset model element gamma, an environment state conversion model element and an exploration rate epsilon to obtain a processing element.
S305, acquiring first order information of a first user, wherein the first order information comprises time characteristic information, area characteristic information and passenger characteristic information of the first user.
Specifically, the execution method of S305 is the same as the execution method of S203, and here, the execution process of S305 is not described again.
S306, under at least one preset recommendation mode, processing the time feature information, the area feature information and the passenger feature information of the first user through an enhanced learning model with processing elements to obtain the ordering probability corresponding to each preset recommendation mode.
S307, determining a target recommendation mode in at least one preset recommendation mode according to the ordering probability.
The method provided by the embodiment comprises the following steps: acquiring historical order information, wherein the historical order information comprises at least one order information; determining sample order information in at least one order information included in the historical order information; performing feature extraction processing on the sample order information to obtain at least one sample feature, wherein the at least one sample feature comprises a sample time feature, a sample user feature and a sample region feature; determining a processing element based on the at least one sample feature; acquiring first order information of a first user, wherein the first order information comprises time feature information, area feature information and passenger feature information of the first user; under at least one preset recommendation mode, processing the time feature information, the area feature information and the passenger feature information of the first user through an enhanced learning model with processing elements to obtain the ordering probability corresponding to each preset recommendation mode; and determining a target recommendation mode in at least one preset recommendation mode according to the ordering probability. In the method, the time characteristic information, the area characteristic information and the passenger characteristic information of the first user are processed through the reinforcement learning model with the processing elements, and the time characteristic information, the area characteristic information and the passenger characteristic information of the current first user are comprehensively considered, so that the accuracy of determining the target recommending mode and the user ordering success probability can be improved, and the benefit of an application platform is further improved.
Fig. 4 is a schematic structural diagram of an information processing apparatus provided in the present application. The information processing apparatus 10 can be applied to a server. Referring to fig. 4, the information processing apparatus 10 may include: an acquisition module 11 and a determination module 12, wherein,
the acquiring module 11 is configured to acquire historical order information, where the historical order information includes at least one order information;
the determining module 12 is configured to process the historical order information and determine a processing element;
the acquiring module 11 is further configured to acquire first order information of a first user;
the determining module 12 is further configured to determine a target recommendation mode according to the processing element and the first order information.
The information processing device provided by the application can execute the technical scheme shown in the embodiment of the method, and the implementation principle and the beneficial effects are similar, and the detailed description is omitted here.
In a possible embodiment, the determining module 12 is further specifically configured to:
acquiring at least one preset recommendation mode;
under the at least one preset recommendation mode, processing the first order information through an enhanced learning model with the processing elements to obtain the corresponding order placing probability of each preset recommendation mode;
and determining a target recommendation mode in the at least one preset recommendation mode according to the ordering probability.
In one possible embodiment, the determining module 12 is specifically configured to;
determining sample order information in at least one order information included in the historical order information;
performing feature extraction processing on the sample order information to obtain at least one sample feature;
the processing element is determined based on the at least one sample characteristic.
In one possible embodiment, the at least one sample feature comprises a sample time feature, a sample user feature, and a sample area feature; the determining module 12 is specifically configured to:
determining an environmental state element from the sample time feature, the sample user feature, and the sample area feature;
and determining the processing element according to the environment state element, the preset individual action element and the preset environment rewarding element.
In one possible implementation, the determining module 12 is specifically configured to:
and carrying out combination processing on the environment state element, the preset individual action element and the preset environment rewarding element to obtain the processing element.
In one possible implementation, the determining module 12 is specifically configured to:
acquiring effective order information from at least one order information included in the historical order information;
and determining the effective order information as sample order information.
In one possible implementation, the determining module 12 is specifically configured to:
and removing order information of which the order types are a brush order and a wrong order from at least one piece of order information included in the historical order information to obtain the effective order information. And (5) brushing order information of the order orders and the error order, and obtaining the effective order information.
Fig. 5 is a schematic hardware structure of the information processing apparatus provided in the present application, and as shown in fig. 5, the information processing apparatus 20 includes: at least one processor 21, a memory 22. The processor 21 and the memory 22 are connected by a bus 23.
In a specific implementation, at least one processor 21 executes computer-executable instructions stored in the memory 22, so that the at least one processor 21 performs the information processing method as described above.
The specific implementation process of the processor 21 can be referred to the above method embodiment, and its implementation principle and technical effects are similar, and this embodiment will not be described herein again.
In the embodiment shown in fig. 5, it should be understood that the processor may be a central processing unit (english: central Processing Unit, abbreviated as CPU), or may be other general purpose processors, digital signal processors (english: digital Signal Processor, abbreviated as DSP), application specific integrated circuits (english: application Specific Integrated Circuit, abbreviated as ASIC), or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in a processor for execution.
The memory may comprise high speed RAM memory or may further comprise non-volatile storage NVM, such as at least one disk memory.
The bus may be an industry standard architecture (Industry Standard Architecture, ISA) bus, an external device interconnect (Peripheral Component, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, the buses in the drawings of the present application are not limited to only one bus or one type of bus.
The present application also provides a computer-readable storage medium having stored therein computer-executable instructions which, when executed by a processor, implement the information processing method as described above.
The computer readable storage medium described above may be implemented by any type of volatile or non-volatile memory device or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk. A readable storage medium can be any available medium that can be accessed by a general purpose or special purpose computer.
An exemplary readable storage medium is coupled to the processor such the processor can read information from, and write information to, the readable storage medium. In the alternative, the readable storage medium may be integral to the processor. The processor and the readable storage medium may reside in an application specific integrated circuit (Application Specific Integrated Circuits, ASIC for short). The processor and the readable storage medium may reside as discrete components in a device.
The division of the units is merely a logic function division, and there may be another division manner when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown 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 may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the method embodiments described above may be performed by hardware associated with program instructions. The foregoing program may be stored in a computer readable storage medium. The program, when executed, performs steps including the method embodiments described above; and the aforementioned storage medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (7)
1. An information processing method, characterized by comprising:
acquiring historical order information, wherein the historical order information comprises at least one order information;
processing the historical order information to determine processing elements;
acquiring first order information of a first user;
determining a target recommendation mode according to the processing element and the first order information;
processing the historical order information to determine processing elements, including;
determining sample order information in at least one order information included in the historical order information;
performing feature extraction processing on the sample order information to obtain at least one sample feature; the at least one sample feature includes a sample time feature, a sample user feature, and a sample area feature; the sample time characteristics comprise riding time of a sample user, time intervals from bubbling to bill generation of orders and time periods from bill generation of the sample user, the sample area characteristics comprise whether the current position of the sample user is a popular business district, the number of surrounding vehicles and the total number of orders in an area where the sample user is currently located, and the sample user characteristics comprise probability of each preset recommendation mode in which the sample user participates before, probability of successful bill generation of the sample user in each preset recommendation mode before, time interval from bubbling to bill generation and conversion success probability;
determining the processing element based on the at least one sample feature; wherein the processing elements include an environmental status element, an individual action element, and an environmental rewards element;
the determining a target recommendation mode according to the processing element and the first order information includes:
acquiring at least one preset recommendation mode; wherein the at least one recommendation method comprises: red envelope, coupon, membership score;
under the at least one preset recommendation mode, processing the first order information through an enhanced learning model with the processing elements to obtain the corresponding order placing probability of each preset recommendation mode;
determining a target recommendation mode in the at least one preset recommendation mode according to the order probability;
the at least one sample feature includes a sample time feature, a sample user feature, and a sample area feature; determining the processing element from the at least one sample feature comprises:
determining an environmental state element from the sample time feature, the sample user feature, and the sample area feature;
determining an individual policy element according to the environment state element and a preset individual action element; determining a value element according to the environment state element, the individual strategy element and the preset model element; determining an environmental state transition model element according to the environmental state element and a preset individual action element; the processing element is obtained by combining an environmental state element, a preset individual action element, a preset environmental rewards element, an individual strategy element, a value element, a preset model element and an environmental state conversion model element.
2. The method of claim 1, wherein determining the processing element based on the environmental status element, a preset individual action element, and a preset environmental reward element comprises:
and carrying out combination processing on the environment state element, the preset individual action element and the preset environment rewarding element to obtain the processing element.
3. The method of any of claims 1 to 2, wherein determining sample order information from at least one order information included in the historical order information comprises:
acquiring effective order information from at least one order information included in the historical order information;
and determining the effective order information as sample order information.
4. A method according to claim 3, wherein the order information includes an order type; the step of obtaining valid order information from at least one order information included in the historical order information includes:
and removing order information of which the order types are a brush order and a wrong order from at least one piece of order information included in the historical order information to obtain the effective order information.
5. An information processing apparatus, characterized by comprising: the device comprises an acquisition module and a determination module, wherein,
the acquisition module is used for acquiring historical order information, wherein the historical order information comprises at least one order information;
the determining module is used for processing the historical order information and determining processing elements;
the acquisition module is also used for acquiring first order information of a first user;
the determining module is further used for determining a target recommending mode according to the processing element and the first order information;
the determining module is specifically configured to determine sample order information from at least one order information included in the historical order information;
performing feature extraction processing on the sample order information to obtain at least one sample feature; the at least one sample feature includes a sample time feature, a sample user feature, and a sample area feature; the sample time characteristics comprise riding time of a sample user, time intervals from bubbling to bill generation of orders and time periods from bill generation of the sample user, the sample area characteristics comprise whether the current position of the sample user is a popular business district, the number of surrounding vehicles and the total number of orders in an area where the sample user is currently located, and the sample user characteristics comprise probability of each preset recommendation mode in which the sample user participates before, probability of successful bill generation of the sample user in each preset recommendation mode before, time interval from bubbling to bill generation and conversion success probability;
determining the processing element based on the at least one sample feature; wherein the processing elements include an environmental status element, an individual action element, and an environmental rewards element;
the determining module is specifically configured to obtain at least one preset recommendation mode; wherein the at least one recommendation method comprises: red envelope, coupon, membership score;
under the at least one preset recommendation mode, processing the first order information through an enhanced learning model with the processing elements to obtain the corresponding order placing probability of each preset recommendation mode;
determining a target recommendation mode in the at least one preset recommendation mode according to the order probability;
the determining module is specifically configured to determine an environmental state element according to the sample time feature, the sample user feature, and the sample area feature;
determining an individual policy element according to the environment state element and a preset individual action element; determining a value element according to the environment state element, the individual strategy element and the preset model element; determining an environmental state transition model element according to the environmental state element and a preset individual action element; the processing element is obtained by combining an environmental state element, a preset individual action element, a preset environmental rewards element, an individual strategy element, a value element, a preset model element and an environmental state conversion model element.
6. An information processing apparatus, characterized by comprising: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executing computer-executable instructions stored in the memory causes the at least one processor to perform the information processing method of any one of claims 1 to 4.
7. A computer-readable storage medium having stored therein computer-executable instructions which, when executed by a processor, implement the information processing method of any one of claims 1 to 4.
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