CN113449002A - Vehicle recommendation method and device, electronic equipment and storage medium - Google Patents
Vehicle recommendation method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention relates to the field of intelligent decision making, and discloses a vehicle recommendation method, which comprises the following steps: the vehicle recommendation method comprises the steps of obtaining vehicle recommendation users and corresponding user information, carrying out feature extraction on the user information to obtain feature user information, and marking vehicle requirements of the vehicle recommendation users according to the feature user information; based on a vehicle demand stage, collecting a preferred vehicle type of a vehicle recommendation user by utilizing a recall layer of a vehicle recommendation model; attribute intersection is carried out on the preferred vehicle type and the vehicle recommendation user by utilizing a decision layer of a vehicle recommendation model to obtain leaf attribute nodes; preference fitting is carried out on the leaf attribute nodes by utilizing a fitting layer of the vehicle recommendation model to obtain preference probabilities of preferred vehicle types and vehicle recommendation users, and the preferred vehicle types meeting preset conditions are selected from the preferred vehicle types and recommended to the vehicle recommendation users according to the preference probabilities. In addition, the invention also relates to a block chain technology, and the characteristic user information can be stored in the block chain. The method and the device can improve the accuracy of vehicle recommendation.
Description
Technical Field
The invention relates to the field of intelligent decision making, in particular to a vehicle recommendation method and device, electronic equipment and a computer readable storage medium.
Background
With the development of information technology, the living standard of people is continuously improved, and more families have the demand of replacing cars to guarantee basic trips, improve the convenience of life. At present, vehicle replacement recommendation is generally performed based on vehicle browsing data of users online or offline, but in an actual business scene, many users may have a false data filling phenomenon in order to ensure privacy of personal data, which easily causes low accuracy of vehicle replacement recommendation.
Disclosure of Invention
The invention provides a vehicle recommendation method, a vehicle recommendation device, electronic equipment and a computer-readable storage medium, and mainly aims to improve the accuracy of vehicle recommendation.
In order to achieve the above object, the present invention provides a vehicle recommendation method, including:
the method comprises the steps of obtaining vehicle recommending users and corresponding user information, carrying out feature extraction on the user information to obtain feature user information, and marking vehicle demand stages of the vehicle recommending users according to the feature user information;
based on the vehicle demand stage, collecting the preferred vehicle type of the vehicle recommendation user by utilizing a recall layer in a vehicle recommendation model;
attribute intersection is carried out on the preferred vehicle type and the vehicle recommending user by utilizing a decision layer in the vehicle recommending model, and leaf attribute nodes of the preferred vehicle type and the vehicle recommending user are obtained;
and performing preference fitting on the leaf attribute nodes by using a fitting layer in the vehicle recommendation model to obtain preference probabilities of the preferred vehicle types and the vehicle recommendation users, and selecting the preferred vehicle types meeting preset conditions from the preferred vehicle types to recommend to the vehicle recommendation users according to the preference probabilities.
Optionally, the performing feature extraction on the user information to obtain feature user information includes:
carrying out duplicate removal processing on the user information, and calculating the information weight of each user data in the user information after the duplicate removal;
and selecting the user data with the information weight larger than a preset threshold value, and generating characteristic user information according to the selected user data.
Optionally, the marking the vehicle demand phase of the vehicle recommendation user according to the characteristic user information includes:
acquiring the user attribute of the characteristic user information, and identifying a vehicle using node of the vehicle recommending user according to the user attribute;
and determining the vehicle demand stage of the vehicle recommendation user according to the vehicle utilization node.
Optionally, the collecting, based on the vehicle demand phase, a preferred vehicle type of the vehicle recommendation user by using a recall layer in a vehicle recommendation model includes:
determining the user attribute of the vehicle recommendation user based on the vehicle demand stage, and performing characteristic branching on the user attribute by utilizing the coding module of the recall layer to obtain at least two branch attributes;
counting the historical vehicle type purchase quantity of each branch attribute, and selecting the historical vehicle type of which the historical vehicle type purchase quantity is greater than the preset quantity to obtain the historical preference vehicle type of each branch attribute;
and utilizing a sorting module of the recall layer to sort the quantity of the historical preferred vehicle types to obtain a preferred vehicle type sorting table of the user attribute, and generating the preferred vehicle type of the vehicle recommendation user according to the preferred vehicle type sorting table.
Optionally, the performing attribute intersection on the preferred vehicle type and the vehicle recommendation user by using a decision layer in the vehicle recommendation model to obtain leaf attribute nodes of the preferred vehicle type and the vehicle recommendation user includes:
inquiring all branch attributes in the vehicle recommending user, and constructing a decision tree of the branch attributes and the preferred vehicle type by using a decision tree algorithm in the decision layer;
and determining the leaf index positions of the branch attributes and the preferred vehicle type in the decision tree by using a linear regression function in the decision layer, and generating leaf attribute nodes of the preferred vehicle type and the vehicle recommending user.
Optionally, the performing preference fitting on the leaf attribute nodes by using a fitting layer in the vehicle recommendation model to obtain a preference probability of the preferred vehicle type and the vehicle recommendation user includes:
calculating branch preference probability of branch attributes and preferred vehicle types in the leaf attribute nodes by using the activation function in the fitting layer;
and fitting the branch preference probability by using a fitting function in the fitting layer to obtain the preference probability of the preferred vehicle type and the vehicle recommended user.
Optionally, the fitting function comprises:
wherein r isimRepresenting the preference probability of the vehicle recommending user for the preferred vehicle type,
denotes the learning rate, L (y)i,f(xi) Represents the loss function, yiPreference probability, f (x), of preferred vehicle type representing ith branch attributei) Representing the ith region function in the decision tree, f (x) representing the region function in the decision tree, fm-1()Representing the region fit function in the decision tree.
In order to solve the above problem, the present invention also provides a vehicle recommendation apparatus including:
the demand phase marking module is used for acquiring vehicle recommendation users and corresponding user information, performing feature extraction on the user information to obtain feature user information, and marking vehicle demand phases of the vehicle recommendation users according to the feature user information;
the preferred vehicle type acquisition module is used for acquiring the preferred vehicle type of the vehicle recommendation user by utilizing a recall layer in a vehicle recommendation model based on the vehicle demand stage;
the attribute crossing module is used for performing attribute crossing on the preferred vehicle type and the vehicle recommending user by utilizing a decision layer in the vehicle recommending model to obtain leaf attribute nodes of the preferred vehicle type and the vehicle recommending user;
and the vehicle recommendation module is used for performing preference fitting on the leaf attribute nodes by using a fitting layer in the vehicle recommendation model to obtain preference probabilities of the preferred vehicle types and the vehicle recommendation users, and selecting the preferred vehicle types meeting preset conditions from the preferred vehicle types to recommend to the vehicle recommendation users according to the preference probabilities.
In order to solve the above problem, the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to implement the vehicle recommendation method described above.
In order to solve the above problem, the present invention also provides a computer-readable storage medium having at least one computer program stored therein, the at least one computer program being executed by a processor in an electronic device to implement the vehicle recommendation method described above.
According to the embodiment of the invention, firstly, a vehicle recommending user and corresponding user information are obtained, the user information is subjected to feature extraction to obtain feature user information, some useless data in the user information can be screened out, the subsequent data processing speed is increased, the vehicle requirement stage of the vehicle recommending user is marked according to the feature user information, the user stage of the vehicle recommending user can be identified, and the vehicle using range of the vehicle recommending user is locked, so that accurate vehicle recommending is realized; secondly, based on the vehicle demand stage, the preference vehicle type of the vehicle recommendation user is collected by using a recall layer in a vehicle recommendation model to guarantee the premise of subsequent vehicle recommendation and avoid the phenomenon of inaccurate vehicle recommendation caused by false information constructed by the vehicle recommendation user, and the decision layer in the vehicle recommendation model is used for performing attribute intersection on the preference vehicle type and the vehicle recommendation user to obtain leaf attribute nodes of the preference vehicle type and the vehicle recommendation user, so that the corresponding relation can be established between the preference vehicle type and the branch attribute of each user attribute of the vehicle recommendation user, and the accuracy of subsequent vehicle recommendation is improved; furthermore, in the embodiment of the invention, the leaf variable nodes are subjected to preference fitting by using a fitting layer in the vehicle recommendation model to obtain preference probabilities of the preferred vehicle type and the vehicle recommendation user, a vehicle type meeting the preset preference is selected from the preferred vehicle types and recommended to the vehicle recommendation user according to the preference probabilities, and the preference probabilities of each branch attribute in the leaf variable nodes and the preferred vehicle type can be calculated, so that the preference probabilities of the vehicle recommendation user and the preferred vehicle type are fitted, and the accuracy of vehicle recommendation is further improved. Therefore, the vehicle recommendation method, the vehicle recommendation device, the electronic equipment and the computer readable storage medium can improve the accuracy of vehicle recommendation.
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Fig. 1 is a schematic flow chart of a vehicle recommendation method according to an embodiment of the present invention;
FIG. 2 is a detailed flowchart illustrating one step of the vehicle recommendation method of FIG. 1 according to an embodiment of the present invention;
FIG. 3 is a partial schematic diagram of a leaf attribute node generation according to an embodiment of the present invention;
FIG. 4 is a block diagram of a vehicle recommendation device according to an embodiment of the present invention;
fig. 5 is a schematic internal structural diagram of an electronic device for implementing a vehicle recommendation method according to an embodiment of the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the application provides a vehicle recommendation method. The execution subject of the vehicle recommendation method includes, but is not limited to, at least one of electronic devices such as a server and a terminal, which can be configured to execute the method provided by the embodiment of the present application. In other words, the vehicle recommendation method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Referring to fig. 1, a schematic flow chart of a vehicle recommendation method according to an embodiment of the present invention is shown. In an embodiment of the present invention, the vehicle recommendation method includes:
s1, obtaining vehicle recommending users and corresponding user information, performing feature extraction on the user information to obtain feature user information, and marking the vehicle demand stages of the vehicle recommending users according to the feature user information.
In the embodiment of the present invention, the vehicle recommendation user refers to a user who has a vehicle demand, such as a user who frequently performs a taxi taking service, and a user who performs vehicle browsing online and/or offline, where the user information includes basic information and vehicle transaction information, where the basic information includes: name, age, gender, personal assets, etc., and the vehicle transaction information includes vehicle usage data, online vehicle browsing records, and offline vehicle advisory data, etc. Furthermore, the embodiment of the invention screens out some useless data in the user information by extracting the characteristics of the user information, thereby improving the subsequent data processing speed.
As an embodiment of the present invention, the performing feature extraction on the user information to obtain feature user information includes: and carrying out duplicate removal processing on the user information, calculating the information weight of each user data in the user information after the duplicate removal, selecting the user data of which the information weight is greater than a preset threshold value, and generating characteristic user information according to the selected user data.
In an optional embodiment, the performing deduplication processing on the user information to obtain deduplication user information includes: and calculating the similarity of any two user data in the user information, if the similarity is not greater than the preset similarity, simultaneously retaining the two user data, and if the similarity is greater than the preset similarity, deleting any one user data in the two user data.
It should be noted that, before calculating the similarity of the similarities of any two user data in the user information, the embodiment of the present invention further includes: and converting the user data in the user information into a corresponding hash value by using a hash algorithm so as to realize the calculation of the subsequent data similarity.
In an alternative embodiment, the information weight of each user data in the user information after the weight removal is calculated by using the following formula:
wherein, CiInformation weight representing user data, EiRepresents the ith user data in the user information,
represents the eigenvector covariance of the ith user data in the user information, and trace () represents the spatial filter function.
In an optional embodiment, the preset threshold is set to 0.65, and may also be set according to an actual service scenario.
It should be understood that the user data included in the characteristic user information can know the user attributes of the vehicle recommending user, such as a favorite vehicle type, a brand, a gender, an age, and the like, but cannot know the vehicle demand phase of the vehicle recommending user, that is, cannot clearly know the vehicle using phase (such as a vehicle purchasing phase, a vehicle changing phase, a marriage phase, a house purchasing phase, a child rearing phase, and the like) of the vehicle recommending user, and therefore, according to the characteristic user information, the embodiment of the invention marks the vehicle demand phase of the vehicle recommending user to identify the vehicle using phase of the vehicle recommending user, and locks the vehicle using range of the vehicle recommending user, so that accurate vehicle recommendation is achieved.
As an embodiment of the present invention, the marking a vehicle demand phase of the vehicle recommendation user according to the characteristic user information includes: and acquiring the user attribute of the characteristic user information, identifying a vehicle using node of the vehicle recommending user according to the user attribute, and determining a vehicle demand stage of the vehicle recommending user according to the vehicle using node.
Illustratively, the user attributes of the characteristic user information are: male, 30 years old, no car, newcastle, then the vehicle recommending user who can recognize this characteristic user information is: the vehicle-just-needed node can determine that the vehicle-needed stage of the vehicle recommendation user of the characteristic user information is as follows: and a high-potential vehicle purchasing stage.
Further, in order to ensure the privacy and security of the characteristic user information, the characteristic user information may also be stored in a blockchain node.
And S2, collecting the preferred vehicle type of the vehicle recommendation user by utilizing a recall layer in a vehicle recommendation model based on the vehicle demand stage.
In the embodiment of the invention, the vehicle recommendation model is used for collecting preferred vehicle types of the vehicle recommendation users and calculating preference probabilities of the preferred vehicle types and the vehicle recommendation users, is constructed Through a Click-Through-Rate (CTR) prediction network, and comprises a recall layer, a decision layer and a fitting layer, wherein the recall layer is used for collecting the preferred vehicle types of the vehicle recommendation users, the decision layer is used for constructing a decision tree of the preferred vehicle types and the vehicle recommendation users to identify attribute nodes of the preferred vehicle types and the vehicle recommendation users, and the fitting layer is used for performing residual error fitting on the preferred vehicle types and the attribute nodes of the vehicle recommendation users to calculate the preference probabilities of the preferred vehicle types and the vehicle recommendation users.
As an embodiment of the present invention, referring to fig. 2, the collecting, by using a recall layer in a vehicle recommendation model, a preferred vehicle type of the vehicle recommendation user based on the vehicle demand phase includes:
s201, determining the user attribute of the vehicle recommendation user based on the vehicle demand stage, and performing characteristic branching on the user attribute by using the coding module of the recall layer to obtain at least two branch attributes;
s202, counting the historical vehicle type purchase quantity of each branch attribute, and selecting the historical vehicle type of which the historical vehicle type purchase quantity is larger than a preset quantity to obtain the historical preference vehicle type of each branch attribute;
s203, utilizing a sorting module of the recall layer to sort the quantity of the historical preferred vehicle types to obtain a preferred vehicle type sorting table of the user attribute, and generating the preferred vehicle type of the vehicle recommending user according to the preferred vehicle type sorting table.
In an optional embodiment, the determining the user attribute of the vehicle recommendation user based on the vehicle demand phase includes: obtaining the vehicle using node in the vehicle demand stage, determining the user attribute of the vehicle recommending user according to the vehicle using node, and if the vehicle using node is a pre-purchased vehicle node, the user attribute of the vehicle recommending user may be: gender, age, vehicle brand, and vehicle color, etc.
In an optional embodiment, the feature branching of the user attribute is to split each user attribute into at least two feature attributes to increase the number of recalls of the preferred vehicle type, for example, if the user attribute is gender, the feature branching of the user attribute is as follows: male and female, should understand, said user attribute is off-line characteristic (such as age) or continuity characteristic (such as click rate), because the computer can only process the digital code, the invention realizes the characteristic branch of said user attribute through said code, in order to guarantee the normal loading process of the subsequent data.
In an optional embodiment, the historical vehicle type purchase quantity of the branch attribute may be generated by querying vehicle purchase data of a vehicle website and/or an offline vehicle sales site, and the preset quantity is set to 200, and may also be set according to an actual service scenario.
It should be further understood that the same historical preferred vehicle type may exist in the historical preferred vehicle type of each branch attribute, for example, the branch attribute is historical preferred vehicle type "a" for men, and the branch attribute is historical preferred vehicle type "B" for women, so in the embodiment of the present invention, the historical preferred vehicle types are subjected to quantity sorting by the sorting module to establish the historical preferred vehicle type sorting table of the branch attribute corresponding to the user attribute, so as to generate the historical preferred vehicle type sorting table of the user recommended by the vehicle corresponding to the user attribute.
And S3, performing attribute intersection on the preferred vehicle type and the vehicle recommending user by using a decision layer in the vehicle recommending model to obtain leaf attribute nodes of the preferred vehicle type and the vehicle recommending user.
It should be understood that the preferred vehicle type is generated based on the branch attributes of the user attributes in the vehicle recommending user, and since a plurality of user attributes may exist in the vehicle recommending user and each user attribute includes a plurality of branch attributes, if the vehicle recommending user is directly recommended to the vehicle recommending user according to the historical preferred vehicle type sorting table established in S2, the vehicle recommending user may be affected by the single user attribute or the branch attribute in the vehicle recommending user, and thus the accuracy of vehicle recommendation may be affected, therefore, in the embodiment of the present invention, the decision layer in the vehicle recommending model is used to perform attribute intersection on the preferred vehicle type and the vehicle recommending user, so as to establish a corresponding relationship between the preferred vehicle type and the branch attribute of each user attribute of the vehicle recommending user, and improve the accuracy of vehicle recommendation.
As an embodiment of the present invention, the performing attribute intersection on the preferred vehicle type and the vehicle recommendation user by using a decision layer in the vehicle recommendation model to obtain leaf attribute nodes of the preferred vehicle type and the vehicle recommendation user includes: inquiring all branch attributes in the vehicle recommending users, constructing a decision tree of the branch attributes and the preferred vehicle types by using a decision tree algorithm in the decision layer, determining leaf index positions of the branch attributes and the preferred vehicle types in the decision tree by using a linear regression function in the decision layer, and generating leaf attribute nodes of the preferred vehicle types and the vehicle recommending users.
The decision tree algorithm may be an XGBoost algorithm, and the regression function may be a relu linear function.
Fig. 3 is a partial schematic diagram of the generation of a leaf attribute node according to an embodiment of the present invention. In the embodiment of the present invention, the fig. 3 includes a branch attribute A, B, C and preferred vehicle types I, II, and III, where the branch attribute a is connected to the preferred vehicle types I, II, and II, the preferred vehicle type I is connected to the branch attribute B and the branch attribute C, the preferred vehicle type II is connected to the branch attribute B and the branch attribute C, and the preferred vehicle type III is connected to the branch attribute B and the branch attribute C.
S4, performing preference fitting on the leaf attribute nodes by using a fitting layer in the vehicle recommendation model to obtain preference probabilities of the preferred vehicle type and the vehicle recommendation user, and selecting the preferred vehicle type meeting preset conditions from the preferred vehicle type according to the preference probabilities to recommend to the vehicle recommendation user.
In the embodiment of the invention, preference fitting is carried out on the leaf attribute nodes by utilizing a fitting layer in the vehicle recommendation model so as to calculate the preference probability of each branch attribute in the leaf attribute nodes and the preferred vehicle type, thereby fitting the preference probability of the vehicle recommendation user and the preferred vehicle type and improving the accuracy of vehicle recommendation.
As an embodiment of the present invention, the performing preference fitting on the leaf attribute node by using a fitting layer in the vehicle recommendation model to obtain a preference probability between the preferred vehicle type and the vehicle recommendation user includes: calculating branch preference probability of the branch attribute and the preferred vehicle type in the leaf attribute node by using the activation function in the matching layer, and fitting the branch preference probability by using the matching function in the matching layer to obtain preference probability of the preferred vehicle type and the vehicle recommendation user.
In an optional embodiment, the activation function comprises:
wherein y (x) represents the branch preference probability of the branch attribute to the preferred vehicle type, x represents the preferred vehicle type, and e represents the infinite acyclic decimal number
In an alternative embodiment, the fitting function comprises:
wherein r isimRepresenting the preference probability of the vehicle recommending user for the preferred vehicle type,
denotes the learning rate, L (y)i,f(xi) Represents the loss function, yiPreference probability, f (x), of preferred vehicle type representing ith branch attributei) Representing the ith region function in the decision tree, f (x) representing the region function in the decision tree, fm-1()Representing the region fit function in the decision tree.
Further, in the embodiment of the present invention, a preferred vehicle type with a preference probability greater than a preset preference probability is selected from the preferred vehicle types and pushed to the vehicle recommendation user as a recommended vehicle, optionally, the preset preference probability is set to 0.7, and may also be set according to an actual service scene.
According to the embodiment of the invention, firstly, a vehicle recommending user and corresponding user information are obtained, the user information is subjected to feature extraction to obtain feature user information, some useless data in the user information can be screened out, the subsequent data processing speed is increased, the vehicle requirement stage of the vehicle recommending user is marked according to the feature user information, the user stage of the vehicle recommending user can be identified, and the vehicle using range of the vehicle recommending user is locked, so that accurate vehicle recommending is realized; secondly, based on the vehicle demand stage, the preference vehicle type of the vehicle recommendation user is collected by using a recall layer in a vehicle recommendation model to guarantee the premise of subsequent vehicle recommendation and avoid the phenomenon of inaccurate vehicle recommendation caused by false information constructed by the vehicle recommendation user, and the decision layer in the vehicle recommendation model is used for performing attribute intersection on the preference vehicle type and the vehicle recommendation user to obtain leaf attribute nodes of the preference vehicle type and the vehicle recommendation user, so that the corresponding relation can be established between the preference vehicle type and the branch attribute of each user attribute of the vehicle recommendation user, and the accuracy of subsequent vehicle recommendation is improved; furthermore, in the embodiment of the invention, the leaf variable nodes are subjected to preference fitting by using a fitting layer in the vehicle recommendation model to obtain preference probabilities of the preferred vehicle type and the vehicle recommendation user, a vehicle type meeting the preset preference is selected from the preferred vehicle types and recommended to the vehicle recommendation user according to the preference probabilities, and the preference probabilities of each branch attribute in the leaf variable nodes and the preferred vehicle type can be calculated, so that the preference probabilities of the vehicle recommendation user and the preferred vehicle type are fitted, and the accuracy of vehicle recommendation is further improved. Therefore, the vehicle recommendation method provided by the invention can improve the accuracy of vehicle recommendation.
Fig. 4 is a functional block diagram of the vehicle recommendation apparatus according to the present invention.
The vehicle recommendation apparatus 100 according to the present invention may be installed in an electronic device. According to the realized functions, the vehicle recommendation device can comprise a demand phase marking module 101, a preferred vehicle type collecting module 102, an attribute crossing module 103 and a vehicle recommendation module 104. The module, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of the electronic device 1 and that can perform a fixed function, and that are stored in a memory of the electronic device 1.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the demand phase marking module 101 is configured to obtain a vehicle recommendation user and corresponding user information, perform feature extraction on the user information to obtain feature user information, and mark a vehicle demand phase of the vehicle recommendation user according to the feature user information;
the preferred vehicle type acquisition module 102 is configured to acquire a preferred vehicle type of the vehicle recommendation user by using a recall layer in a vehicle recommendation model based on the vehicle demand phase;
the attribute crossing module 103 is configured to perform attribute crossing on the preferred vehicle type and the vehicle recommendation user by using a decision layer in the vehicle recommendation model to obtain leaf attribute nodes of the preferred vehicle type and the vehicle recommendation user;
the vehicle recommendation module 104 is configured to perform preference fitting on the leaf attribute nodes by using a fitting layer in the vehicle recommendation model to obtain preference probabilities of the preferred vehicle types and the vehicle recommendation users, and select a preferred vehicle type meeting a preset condition from the preferred vehicle types to recommend to the vehicle recommendation users according to the preference probabilities.
In detail, when the vehicle recommendation apparatus 100 in the embodiment of the present invention is used, the same technical means as the vehicle recommendation method described in fig. 1 to 3 are adopted, and the same technical effects can be produced, and details are not repeated herein.
Fig. 5 is a schematic structural diagram of an electronic device 1 for implementing a vehicle recommendation method according to the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program, such as a vehicle recommendation program, stored in the memory 11 and executable on the processor 10.
In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), a microprocessor, a digital Processing chip, a graphics processor, a combination of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device 1, connects various components of the electronic device 1 by using various interfaces and lines, and executes various functions and processes data of the electronic device 1 by operating or executing programs or modules (for example, executing a vehicle recommendation program, etc.) stored in the memory 11 and calling data stored in the memory 11.
The memory 11 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only to store application software installed in the electronic device 1 and various types of data, such as codes of a vehicle recommendation program, etc., but also to temporarily store data that has been output or is to be output.
The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
The communication interface 13 is used for communication between the electronic device 1 and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are generally used for establishing a communication connection between the electronic device 1 and other electronic devices 1. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the electronic device 1 and for displaying a visualized user interface, among other things.
Fig. 5 shows only the electronic device 1 with components, and it will be understood by those skilled in the art that the structure shown in fig. 5 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device 1 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so as to implement functions of charge management, discharge management, power consumption management, and the like through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 1 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The vehicle recommendation program stored in the memory 11 of the electronic device 1 is a combination of a plurality of computer programs, which when executed in the processor 10, can implement:
the method comprises the steps of obtaining vehicle recommending users and corresponding user information, carrying out feature extraction on the user information to obtain feature user information, and marking vehicle demand stages of the vehicle recommending users according to the feature user information;
based on the vehicle demand stage, collecting the preferred vehicle type of the vehicle recommendation user by utilizing a recall layer in a vehicle recommendation model;
attribute intersection is carried out on the preferred vehicle type and the vehicle recommending user by utilizing a decision layer in the vehicle recommending model, and leaf attribute nodes of the preferred vehicle type and the vehicle recommending user are obtained;
and performing preference fitting on the leaf attribute nodes by using a fitting layer in the vehicle recommendation model to obtain preference probabilities of the preferred vehicle types and the vehicle recommendation users, and selecting the preferred vehicle types meeting preset conditions from the preferred vehicle types to recommend to the vehicle recommendation users according to the preference probabilities.
Specifically, the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the computer program, which is not described herein again.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a non-volatile computer-readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
The present invention also provides a computer-readable storage medium, storing a computer program which, when executed by a processor of an electronic device 1, may implement:
the method comprises the steps of obtaining vehicle recommending users and corresponding user information, carrying out feature extraction on the user information to obtain feature user information, and marking vehicle demand stages of the vehicle recommending users according to the feature user information;
based on the vehicle demand stage, collecting the preferred vehicle type of the vehicle recommendation user by utilizing a recall layer in a vehicle recommendation model;
attribute intersection is carried out on the preferred vehicle type and the vehicle recommending user by utilizing a decision layer in the vehicle recommending model, and leaf attribute nodes of the preferred vehicle type and the vehicle recommending user are obtained;
and performing preference fitting on the leaf attribute nodes by using a fitting layer in the vehicle recommendation model to obtain preference probabilities of the preferred vehicle types and the vehicle recommendation users, and selecting the preferred vehicle types meeting preset conditions from the preferred vehicle types to recommend to the vehicle recommendation users according to the preference probabilities.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules 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 modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A vehicle recommendation method, characterized in that the method comprises:
the method comprises the steps of obtaining vehicle recommending users and corresponding user information, carrying out feature extraction on the user information to obtain feature user information, and marking vehicle demand stages of the vehicle recommending users according to the feature user information;
based on the vehicle demand stage, collecting the preferred vehicle type of the vehicle recommendation user by utilizing a recall layer in a vehicle recommendation model;
attribute intersection is carried out on the preferred vehicle type and the vehicle recommending user by utilizing a decision layer in the vehicle recommending model, and leaf attribute nodes of the preferred vehicle type and the vehicle recommending user are obtained;
and performing preference fitting on the leaf attribute nodes by using a fitting layer in the vehicle recommendation model to obtain preference probabilities of the preferred vehicle types and the vehicle recommendation users, and selecting the preferred vehicle types meeting preset conditions from the preferred vehicle types to recommend to the vehicle recommendation users according to the preference probabilities.
2. The vehicle recommendation method of claim 1, wherein the performing feature extraction on the user information to obtain feature user information comprises:
carrying out duplicate removal processing on the user information, and calculating the information weight of each user data in the user information after the duplicate removal;
and selecting the user data with the information weight larger than a preset threshold value, and generating characteristic user information according to the selected user data.
3. The vehicle recommendation method of claim 1, wherein said marking vehicle demand phases of said vehicle recommendation user based on said characteristic user information comprises:
acquiring the user attribute of the characteristic user information, and identifying a vehicle using node of the vehicle recommending user according to the user attribute;
and determining the vehicle demand stage of the vehicle recommendation user according to the vehicle utilization node.
4. The vehicle recommendation method of claim 1, wherein the collecting the vehicle recommendation user's preferred vehicle type using a recall layer in a vehicle recommendation model based on the vehicle demand phase comprises:
determining the user attribute of the vehicle recommendation user based on the vehicle demand stage, and performing characteristic branching on the user attribute by utilizing the coding module of the recall layer to obtain at least two branch attributes;
counting the historical vehicle type purchase quantity of each branch attribute, and selecting the historical vehicle type of which the historical vehicle type purchase quantity is greater than the preset quantity to obtain the historical preference vehicle type of each branch attribute;
and utilizing a sorting module of the recall layer to sort the quantity of the historical preferred vehicle types to obtain a preferred vehicle type sorting table of the user attribute, and generating the preferred vehicle type of the vehicle recommendation user according to the preferred vehicle type sorting table.
5. The vehicle recommendation method of claim 1, wherein the performing attribute intersection on the preferred vehicle type and the vehicle recommendation user by using a decision layer in the vehicle recommendation model to obtain leaf attribute nodes of the preferred vehicle type and the vehicle recommendation user comprises:
inquiring all branch attributes in the vehicle recommending user, and constructing a decision tree of the branch attributes and the preferred vehicle type by using a decision tree algorithm in the decision layer;
and determining the leaf index positions of the branch attributes and the preferred vehicle type in the decision tree by using a linear regression function in the decision layer, and generating leaf attribute nodes of the preferred vehicle type and the vehicle recommending user.
6. The vehicle recommendation method of any one of claims 1 to 5, wherein the performing preference fitting on the leaf attribute nodes by using a fitting layer in the vehicle recommendation model to obtain preference probabilities of the preferred vehicle type and the vehicle recommendation user comprises:
calculating branch preference probability of branch attributes and preferred vehicle types in the leaf attribute nodes by using the activation function in the fitting layer;
and fitting the branch preference probability by using a fitting function in the fitting layer to obtain the preference probability of the preferred vehicle type and the vehicle recommended user.
7. The vehicle recommendation method of claim 6, wherein the fitting function comprises:
wherein r isimRepresenting the preference probability of the vehicle recommending user for the preferred vehicle type,
denotes the learning rate, L (y)i,f(xi) Represents the loss function, yiPreference probability, f (x), of preferred vehicle type representing ith branch attributei) Representing the ith region function in the decision tree, f (x) representing the region function in the decision tree, fm-1(x)Representing the region fit function in the decision tree.
8. A vehicle recommendation device, characterized in that the device comprises:
the demand phase marking module is used for acquiring vehicle recommendation users and corresponding user information, performing feature extraction on the user information to obtain feature user information, and marking vehicle demand phases of the vehicle recommendation users according to the feature user information;
the preferred vehicle type acquisition module is used for acquiring the preferred vehicle type of the vehicle recommendation user by utilizing a recall layer in a vehicle recommendation model based on the vehicle demand stage;
the attribute crossing module is used for performing attribute crossing on the preferred vehicle type and the vehicle recommending user by utilizing a decision layer in the vehicle recommending model to obtain leaf attribute nodes of the preferred vehicle type and the vehicle recommending user;
and the vehicle recommendation module is used for performing preference fitting on the leaf attribute nodes by using a fitting layer in the vehicle recommendation model to obtain preference probabilities of the preferred vehicle types and the vehicle recommendation users, and selecting the preferred vehicle types meeting preset conditions from the preferred vehicle types to recommend to the vehicle recommendation users according to the preference probabilities.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the vehicle recommendation method of any one of claims 1-7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements a vehicle recommendation method as recited in any one of claims 1-7.
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