CN111667340A - Target object recommendation method and device based on big data and computer-readable storage medium - Google Patents
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Abstract
The invention relates to an artificial intelligence technology, and discloses a target object recommendation method based on big data, which comprises the following steps: acquiring a basic information data set of a user, and retrieving an original associated data set of the basic information data set from a network according to the basic information data set; performing an exception removal preprocessing operation on the original associated data set to obtain an initial associated data set, and numbering all initial associated data in the initial associated data set to obtain a standard associated data set; performing result prediction analysis on each standard associated data in the standard associated data set to generate a characteristic associated data set; and selecting a target object from a pre-constructed target object database according to the characteristic association data set to recommend the user. The invention can solve the problems that various target objects are various and a client is difficult to efficiently select the target object suitable for the client. The invention also relates to a block chain technology, and the network can be a block chain network.
Description
Technical Field
The invention relates to the technical field of artificial intelligence and big data, in particular to a target object recommendation method and device based on big data, electronic equipment and a computer-readable storage medium.
Background
With the improvement of living standard, the goods or services provided by society are more and more abundant, and it is very important for users to recommend suitable target objects for users. For example, traveling has become daily life of people at present, but traveling has some unexpected situations with a certain probability, such as the situations of delayed working, robbed and stolen personal belongings, and different-place medical treatment due to accidental injury and illness, so that people usually buy an insurance product for themselves before going out.
The current insurance product types are dazzling, and people often have little clarity of the unexpected types which can be met by people, so that people are difficult to find suitable insurance products from various types of insurance, and therefore, people pay more and more attention to how to efficiently select target products suitable for users from a large number of products and recommend the target products to customers.
Disclosure of Invention
The invention provides a target object recommendation method and device based on big data, electronic equipment and a computer readable storage medium, and mainly aims to solve the problems that various target objects are various and a client cannot efficiently select a target object suitable for the client.
In order to achieve the above object, the present invention provides a method for recommending a target object based on big data, comprising:
acquiring a basic information data set of a user, and retrieving an original associated data set of the basic information data set from a network according to the basic information data set;
performing an exception removal preprocessing operation on the original associated data set to obtain an initial associated data set, and numbering all initial associated data in the initial associated data set to obtain a standard associated data set;
performing result prediction analysis on each standard associated data in the standard associated data set to generate a characteristic associated data set;
and selecting a target object from a pre-constructed target object database according to the characteristic association data set to recommend the user.
Optionally, the performing result prediction analysis on each standard associated data in the standard associated data set to generate a feature associated data set includes:
performing result prediction analysis on the standard associated data set to obtain an analysis result set;
performing occurrence probability calculation on each analysis result in the analysis result set to obtain a probability result set;
generating a number which is the same as the standard associated data for each probability result in the probability result set to obtain a probability result number set;
and merging the items with the same number in the probability result number set and the standard associated data set to obtain the characteristic associated data set.
Optionally, the performing result prediction analysis on the standard associated data set to obtain an analysis result set includes:
step A: randomly generating a training initial association data set and a standard analysis result set corresponding to the training initial association data set;
and B: performing predictive analysis on the training initial association data set by using a convolutional neural network to obtain a training analysis result set;
and C: comparing and judging the training analysis result set and the standard analysis result set, if the training analysis result set and the standard analysis result set are different, adjusting parameters of the neural network, and returning to the step B to continue to execute conversion;
step D: if the training analysis result set and the standard analysis result set do not have difference, finishing the training;
step E: and performing predictive analysis on the standard associated data set by using the trained convolutional neural network to obtain an analysis result set.
Optionally, the comparing and determining the training analysis result set and the standard analysis result set includes:
carrying out similarity calculation on the training analysis result set and the standard analysis result set to obtain a similarity value;
comparing and judging the similarity value with a preset threshold value, and if the similarity value is smaller than or equal to the preset threshold value, standardizing the standard analysis result set and the training analysis result set to have difference;
and if the similarity value is larger than the preset threshold value, the standard analysis result set and the training analysis result set are normalized to have no difference.
Optionally, the performing similarity calculation on the training analysis result set and the standard analysis result set to obtain a similarity value includes:
similarity calculation is performed using the following similarity formula:
Simtopic＝Pearson(TPS,TPT)
wherein SimtopicRepresenting the similarity of the training analysis result set and the standard analysis result set; TPTAnalyzing a result set for the training; TPSIs the standard analysis result set.
Optionally, the performing occurrence probability calculation on each analysis result in the analysis result set to obtain a probability result set includes:
the probability of occurrence calculation is performed using the following formula:
wherein,
is the occurrence probability; y is the analysis result set; y istIs an analysis result in the analysis result set; k is standard associated data in the standard associated data set; a is the reciprocal of the sum of the number of elements in the analysis result set and the standard association data set.
Optionally, the network is a blockchain network.
Optionally, the selecting a target object from a pre-constructed target object database according to the feature association data set to recommend to the user includes:
acquiring feature associated data in the feature associated data set;
according to the mapping relation between the characteristic correlation data and the target object, selecting the target object in the target object database to obtain a recommended target object;
and outputting the characteristic correlation data and the recommended target object.
In order to solve the above problem, the present invention further provides a big data based object recommendation apparatus, including:
the information collection module is used for acquiring a basic information data set of a user and retrieving an original associated data set of the basic information data set from a network according to the basic information data set;
the information processing module is used for executing exception removal preprocessing operation on the original associated data set to obtain an initial associated data set, and numbering all initial associated data in the initial associated data set to obtain a standard associated data set;
the prediction analysis module is used for performing result prediction analysis on each standard associated data in the standard associated data set to generate a characteristic associated data set;
and the target object recommending module is used for selecting a target object from a pre-constructed target object database according to the characteristic association data set so as to recommend the user.
In order to solve the above problem, the present invention also provides an electronic device, including:
a memory storing at least one instruction; and
and the processor executes the instructions stored in the memory to realize the big data-based target object recommendation method.
In order to solve the above problem, the present invention further provides a computer-readable storage medium, where at least one instruction is stored, and the at least one instruction is executed by a processor in an electronic device to implement any one of the big data based object recommendation methods described above.
According to the method, the abnormal preprocessing operation is carried out on the original associated data set to obtain the initial associated data set, all initial associated data in the initial associated data set are numbered to obtain the standard associated data set, the accuracy of the collected data is guaranteed, and the follow-up recommendation of the target object is facilitated; performing result prediction analysis on each standard associated data in the standard associated data set to generate a characteristic associated data set, and analyzing possible results in advance to efficiently select a more appropriate target object; according to the characteristic associated data set, the target object is selected from the pre-constructed target object database to recommend the user, the recommendation of the target object is intelligently realized, and the user is helped to select the proper target object, so that the problems that various target objects are various and the user is difficult to efficiently select the target object suitable for the user are solved.
Drawings
Fig. 1 is a schematic flowchart of a big data-based object recommendation method according to an embodiment of the present invention;
FIG. 2 is a schematic block diagram of a big data-based object recommendation method according to an embodiment of the present invention
Fig. 3 is a schematic internal structural diagram of an electronic device according to an embodiment of the present invention, which is based on a big data object recommendation method;
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 invention provides a target object recommendation method based on big data. Fig. 1 is a schematic flow chart of a target object recommendation method based on big data according to an embodiment of the present invention. The method may be performed by an apparatus, which may be implemented by software and/or hardware.
In this embodiment, the target object recommendation method based on big data includes:
s1, acquiring a basic information data set of a user, and retrieving an original associated data set of the basic information data set from a network according to the basic information data set.
In this embodiment of the present invention, the basic information data set of the user may include the tour information of the user, such as: user gender, age, travel destination, travel time, mode of transportation for riding, hotel for lodging, etc. According to the embodiment of the invention, the recommendation of the travel insurance target object for the user can be realized according to the tour information.
Furthermore, the preset retrieval mode can be a web crawler mode and the like. The web crawler is also called a web spider or a web robot, and is a program or script that automatically captures web information according to a certain rule.
The embodiment of the invention utilizes the web crawler technology to retrieve the original associated data from the network.
Further, the network may be a blockchain network. 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. A Blockchain Network (Blockchain Network) incorporates new blocks into a set of nodes of a Blockchain in a consensus manner.
Preferably, the original associated data set in the embodiment of the present invention may include, but is not limited to, information related to the travel destination, such as local climate, public security, etc.; information related to the riding mode: such as vehicle safety, punctuality, etc.; such as hotel related information for the accommodation, such as surrounding attractions, etc.
S2, executing the abnormality removing preprocessing operation on the original associated data set to obtain an initial associated data set, and numbering all initial associated data in the initial associated data set to obtain a standard associated data set.
In the embodiment of the present invention, the original associated data set is obtained from the network by means of a web crawler or the like, so that abnormal data may exist, and therefore, the embodiment of the present invention needs to perform a pre-processing operation of removing the abnormality on the original associated data.
In one embodiment of the present invention, the following formula can be used to perform the anomaly removal calculation:
C＝|Ca|
wherein C represents data after exception removal; ca represents data indicating the presence of an abnormality; "| |" indicates a denormal operation.
In detail, the abnormality removing operation includes, for example, displaying a running absolute value of a negative number in the age data of the user, and/or replacing with a mean value of the neighborhood data when the travel time data of the user is missing, and the like.
The initial associated data set is obtained after the original associated data set is subjected to the exception removing preprocessing operation.
Further, the embodiment of the present invention may perform numbering processing on the initial associated data in the initial associated data set to obtain a standard associated data set.
For example, the initial association data is represented as a set Z ═ { a1, a2, A3. }, which is the standard association data set.
And S3, performing result prediction analysis on each standard associated data in the standard associated data set to generate a feature associated data set.
In detail, the S3 includes:
performing result prediction analysis on the standard associated data set to obtain an analysis result set;
performing occurrence probability calculation on each analysis result in the analysis result set to obtain a probability result set;
generating a number which is the same as the standard associated data for each probability result in the probability result set to obtain a probability result number set;
and merging the items with the same number in the probability result number set and the standard associated data set to obtain the characteristic associated data set.
Further, in the embodiment of the present invention, the generating of the same number as the standard associated data for each probability result in the probability result set to obtain the probability result number set means that, for example, the standard associated data set is Z ═ a1, a2, and A3. }, and then the probability result set is represented as X ═ a1, a2, and a3..
The feature association data set includes a result that may occur in each standard association data set and a probability of the result, for example: travel destinations are not well secured, with sixty percent of the possibility of property loss.
In detail, in the embodiment of the present invention, a CNN (Convolutional Neural Networks) may be used to perform predictive analysis on the standard associated data set to obtain an analysis result set.
The convolutional neural network is one of representative algorithms of deep learning and is used in the fields of computer vision, natural language processing and the like.
Performing result prediction analysis on the standard associated data set to obtain an analysis result set, including:
step A: randomly generating a training initial association data set and a standard analysis result set corresponding to the training initial association data set;
and B: performing predictive analysis on the training initial association data set by using a convolutional neural network to obtain a training analysis result set;
and C: comparing and judging the training analysis result set and the standard analysis result set, if the training analysis result set and the standard analysis result set are different, adjusting parameters of the neural network, and returning to the step B to continue to execute conversion;
step D: if the training analysis result set and the standard analysis result set do not have difference, finishing the training;
step E: and performing predictive analysis on the standard associated data set by using the trained convolutional neural network to obtain an analysis result set.
In detail, the embodiment of the present invention may adopt a similarity calculation method to compare and judge the training analysis result set and the standard analysis result set, including:
similarity calculation is performed using the following similarity formula:
Simtopic＝Pearson(TPS,TPT)
wherein SimtopicRepresenting the similarity of the training analysis result set and the standard analysis result set; TPTAnalyzing a result set for the training; TPSIs the standard analysis result set.
The invention limits the calculation result in the interval of [0,1] by using a sigmoid function, and can preset a similarity threshold value of 0.6.
The sigmoid function is also called a Logistic function, is often used as an activation function of a convolutional neural network, and has a value range of (0, 1).
If the calculation result is less than or equal to 0.6, the standard analysis result set and the training analysis result set are considered to have difference, and the calculation is finished;
and if the calculation result is greater than 0.6, determining that the standard analysis result set and the training analysis result set have no difference, and finishing the training of the convolutional neural network.
Preferably, in the embodiment of the present invention, an existing gradient descent algorithm may be used to adjust parameters of the neural network, where the gradient descent algorithm is a most common parameter adjustment method in machine learning at present, and parameters of the convolutional neural network may be calculated according to a preset training target and a result obtained by training.
Further, the probability calculation module may calculate the occurrence probability of each analysis result in the analysis result set using the following probability calculation formula
And (3) calculating:
wherein Y is the analysis result set; y istIs an analysis result in the analysis result set; k is standard associated data in the standard associated data set Z; a is the reciprocal of the sum of the number of elements in the analysis result set and the standard association data set.
And obtaining the probability result set after probability calculation is completed on all the analysis results in the analysis result set by using the probability calculation formula.
And S4, selecting a target object from a pre-constructed target object database according to the characteristic association data set to recommend the user.
In detail, the object database may be a database for storing insurance objects by an insurance company according to an embodiment of the present invention.
Further, the selecting a target object from a pre-constructed target object database for recommendation according to the feature association data in the feature association data set includes:
acquiring feature associated data in the feature associated data set;
according to the mapping relation between the characteristic correlation data and the target object, selecting the target object in the target object database to obtain a recommended target object;
and outputting the characteristic correlation data and the recommended target object.
Further, in the embodiment of the present invention, a Tacotron architecture may be used to convert the feature association data and the recommendation target object into a voice form, and perform voice output. For example, the feature related data is that the outbound destination is in a messy state, a life safety risk is selected from the pre-constructed object database, and the Tacotron architecture is used for converting the feature related data and the recommended object into a voice that your outbound destination is in a messy state, so that a potential safety hazard may exist, and the life safety risk is recommended to be purchased. "
The tacontron architecture is a voice synthesis system and comprises an encode module, a decoder module and a post-processing net module.
In detail, the converting the feature association data and the recommendation target object into a voice form by using a tacontron architecture and performing voice output includes:
performing voice vector conversion on the feature associated data and the recommended target object to obtain a voice data set;
carrying out audio format conversion on the voice data set to obtain an audio data set;
and performing voice output on the audio data set.
Further, the Tacotron architecture may convert the feature association data and the recommendation target object into the speech vector by using a predetermined convolutional neural network for speech vector conversion.
The Tacotron architecture may use a convolutional neural network for audio format conversion, which is preset to convert the speech vector into the audio data set.
In the invention, comprehensive, the original associated data set is subjected to abnormality removing preprocessing operation to obtain an initial associated data set, all initial associated data in the initial associated data set are numbered to obtain a standard associated data set, the accuracy of the collected data is ensured, and the follow-up recommendation of the target object is facilitated; performing result prediction analysis on each standard associated data in the standard associated data set to generate a characteristic associated data set, predicting a possible result in advance, and selecting a more appropriate target object; and selecting a target object from a pre-constructed target object database according to the characteristic association data set to recommend the user, intelligently recommending the target object and helping to select a proper target object. Therefore, the target object recommendation method, the target object recommendation device and the computer-readable storage medium based on big data can realize that the suitable insurance target object can be found from various insurance.
Fig. 2 is a functional block diagram of the big data-based object recommendation apparatus according to the present invention.
The big data based object recommendation 100 of the present invention may be installed in an electronic device. According to the implemented functions, the big data based object recommendation device may include an information collection module 101, an information processing module 102, a prediction analysis module 103, and an object recommendation module 104. A module according to the present invention, 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 an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the information collection module 101 is configured to obtain a basic information data set of a user, and retrieve an original associated data set of the basic information data set from a network according to the basic information data set;
the information processing module 102 is configured to perform an exception-removing preprocessing operation on the original associated data set to obtain an initial associated data set, and number all initial associated data in the initial associated data set to obtain a standard associated data set;
the prediction analysis module 103 is configured to perform result prediction analysis on each standard associated data in the standard associated data set to generate a feature associated data set;
the object recommending module 104 is configured to select an object from a pre-constructed object database according to the feature association data set to recommend the user.
In detail, the specific implementation steps of each module of the target object recommending device based on big data are as follows:
the information collection module 101 acquires a basic information data set of a user, and retrieves an original associated data set of the basic information data set from a network according to the basic information data set.
In this embodiment of the present invention, the basic information data set of the user may include the tour information of the user, such as: user gender, age, travel destination, travel time, mode of transportation for riding, hotel for lodging, etc. According to the embodiment of the invention, the recommendation of the travel insurance target object for the user can be realized according to the tour information.
Furthermore, the preset retrieval mode can be a web crawler mode and the like. The web crawler is also called a web spider or a web robot, and is a program or script that automatically captures web information according to a certain rule.
The embodiment of the invention utilizes the web crawler technology to retrieve the original associated data from the network.
Further, the network may be a blockchain network.
Preferably, the original associated data set in the embodiment of the present invention may include, but is not limited to, information related to the travel destination, such as local climate, public security, etc.; information related to the riding mode: such as vehicle safety, punctuality, etc.; such as hotel related information for the accommodation, such as surrounding attractions, etc.
The information processing module 102 executes an exception-removing preprocessing operation on the original associated data set to obtain an initial associated data set, and numbers all initial associated data in the initial associated data set to obtain a standard associated data set.
In the embodiment of the present invention, the original associated data set is obtained from the network by means of a web crawler or the like, so that abnormal data may exist, and therefore, the embodiment of the present invention needs to perform a pre-processing operation of removing the abnormality on the original associated data.
In one embodiment of the present invention, the following formula can be used to perform the anomaly removal calculation:
C＝|Ca|
wherein C represents data after exception removal; ca represents data indicating the presence of an abnormality; "| |" indicates a denormal operation.
In detail, the abnormality removing operation is, for example, an absolute value of progress showing a negative number in the age data of the user, and/or a mean value of the neighborhood data when the travel time data of the user is missing, and the like.
The initial associated data set is obtained after the original associated data set is subjected to the exception removing preprocessing operation.
Further, the embodiment of the present invention may perform numbering processing on the initial associated data in the initial associated data set to obtain a standard associated data set.
For example, the initial association data is represented as a set Z ═ { a1, a2, A3. }, which is the standard association data set.
And the prediction analysis module 103 is used for performing result prediction analysis on each standard associated data in the standard associated data set to generate a characteristic associated data set.
In detail, the performing result prediction analysis on each standard associated data in the standard associated data set to generate a feature associated data set includes:
performing result prediction analysis on the standard associated data set to obtain an analysis result set;
performing occurrence probability calculation on each analysis result in the analysis result set to obtain a probability result set;
generating a number which is the same as the standard associated data for each probability result in the probability result set to obtain a probability result number set;
and merging the items with the same number in the probability result number set and the standard associated data set to obtain the characteristic associated data set.
Further, in the embodiment of the present invention, the generating of the same number as the standard associated data for each probability result in the probability result set to obtain the probability result number set means that, for example, the standard associated data set is Z ═ a1, a2, and A3. }, and then the probability result set is represented as X ═ a1, a2, and a3..
The feature association data set includes a result that may occur in each standard association data set and a probability of the result, for example: travel destinations are not well secured, with sixty percent of the possibility of property loss.
In detail, in the embodiment of the present invention, a CNN (Convolutional Neural Networks) may be used to perform predictive analysis on the standard associated data set to obtain an analysis result set.
The convolutional neural network is one of representative algorithms of deep learning and is used in the fields of computer vision, natural language processing and the like.
Performing result prediction analysis on the standard associated data set to obtain an analysis result set, including:
step A: randomly generating a training initial association data set and a standard analysis result set corresponding to the training initial association data set;
and B: performing predictive analysis on the training initial association data set by using a convolutional neural network to obtain a training analysis result set;
and C: comparing and judging the training analysis result set and the standard analysis result set, if the training analysis result set and the standard analysis result set are different, adjusting parameters of the neural network, and returning to the step B to continue to execute conversion;
step D: if the training analysis result set and the standard analysis result set do not have difference, finishing the training;
step E: and performing predictive analysis on the standard associated data set by using the trained convolutional neural network to obtain an analysis result set.
In detail, the embodiment of the present invention may adopt a similarity calculation method to compare and judge the training analysis result set and the standard analysis result set, including:
similarity calculation is performed using the following similarity formula:
Simtopic＝Pearson(TPS,TPT)
wherein SimtopicRepresenting the similarity of the training analysis result set and the standard analysis result set; TPTAnalyzing a result set for the training; TPSIs the standard analysis result set.
The invention limits the calculation result in the interval of [0,1] by using a sigmoid function, and can preset a similarity threshold value of 0.6.
The sigmoid function is also called a Logistic function, is often used as an activation function of a convolutional neural network, and has a value range of (0, 1).
If the calculation result is less than or equal to 0.6, the standard analysis result set and the training analysis result set are considered to have difference, and the calculation is finished;
and if the calculation result is greater than 0.6, determining that the standard analysis result set and the training analysis result set have no difference, and finishing the training of the convolutional neural network.
Preferably, in the embodiment of the present invention, an existing gradient descent algorithm may be used to adjust parameters of the neural network, where the gradient descent algorithm is a most common parameter adjustment method in machine learning at present, and parameters of the convolutional neural network may be calculated according to a preset training target and a result obtained by training.
Further, the probability calculation module may calculate the occurrence probability of each analysis result in the analysis result set using the following probability calculation formula
And (3) calculating:
wherein Y is the analysis result set; y istIs an analysis result in the analysis result set; k is standard associated data in the standard associated data set Z; a is the reciprocal of the sum of the number of elements in the analysis result set and the standard association data set.
And obtaining the probability result set after probability calculation is completed on all the analysis results in the analysis result set by using the probability calculation formula.
And the target object recommending module 104 selects a target object from a pre-constructed target object database according to the characteristic association data set to recommend the user.
In detail, the object database may be a database for storing insurance objects by an insurance company according to an embodiment of the present invention.
Further, the selecting a target object from a pre-constructed target object database for recommendation according to the feature association data in the feature association data set includes:
acquiring feature associated data in the feature associated data set;
according to the mapping relation between the characteristic correlation data and the target object, selecting the target object in the target object database to obtain a recommended target object;
and outputting the characteristic correlation data and the recommended target object.
Further, in the embodiment of the present invention, a Tacotron architecture may be used to convert the feature association data and the recommendation target object into a voice form, and perform voice output. For example, the feature related data is that the outbound destination is in a messy state, a life safety risk is selected from the pre-constructed object database, and the Tacotron architecture is used for converting the feature related data and the recommended object into a voice that your outbound destination is in a messy state, so that a potential safety hazard may exist, and the life safety risk is recommended to be purchased. "
The tacontron architecture is a voice synthesis system and comprises an encode module, a decoder module and a post-processing net module.
In detail, the converting the feature association data and the recommendation target object into a voice form by using a tacontron architecture and performing voice output includes:
performing voice vector conversion on the feature associated data and the recommended target object to obtain a voice data set;
carrying out audio format conversion on the voice data set to obtain an audio data set;
and performing voice output on the audio data set.
Further, the Tacotron architecture may convert the feature association data and the recommendation target object into the speech vector by using a predetermined convolutional neural network for speech vector conversion.
The Tacotron architecture may use a convolutional neural network for audio format conversion, which is preset to convert the speech vector into the audio data set.
Fig. 3 is a schematic structural diagram of an electronic device for implementing a big data-based object recommendation method according to the present invention.
The electronic device 1 may comprise a processor 10, a memory 11 and a bus, and may further comprise a computer program stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, which includes flash memory, removable hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, 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 resource scheduler, etc., but also to temporarily store data that has been output or is to be output.
The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device 1 by running or executing programs or modules (e.g., object recommendation programs based on big data, etc.) stored in the memory 11 and calling data stored in the memory 11.
The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
Fig. 3 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 3 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.
Further, the electronic device 1 may further include a network interface, and optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a bluetooth interface, etc.), which are generally used for establishing a communication connection between the electronic device 1 and other electronic devices.
Optionally, the electronic device 1 may further comprise a user interface, which may be a Display (Display), an input unit (such as a 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.
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 big data based object recommendation program 12 stored in the memory 11 of the electronic device 1 is a combination of a plurality of instructions, which when executed in the processor 10, can realize:
acquiring a basic information data set of a user, and retrieving an original associated data set of the basic information data set from a network according to the basic information data set;
performing an exception removal preprocessing operation on the original associated data set to obtain an initial associated data set, and numbering all initial associated data in the initial associated data set to obtain a standard associated data set;
performing result prediction analysis on each standard associated data in the standard associated data set to generate a characteristic associated data set;
and selecting a target object from a pre-constructed target object database according to the characteristic association data set to recommend the user.
Specifically, the specific implementation method of the processor 10 for the above instruction may refer to the description of the relevant steps in the embodiment corresponding to fig. 2, which is not repeated herein.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as independent objects, may be stored in a computer readable storage medium. 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).
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.
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 big data-based object recommendation method is characterized by comprising the following steps:
acquiring a basic information data set of a user, and retrieving an original associated data set of the basic information data set from a network according to the basic information data set;
performing an exception removal preprocessing operation on the original associated data set to obtain an initial associated data set, and numbering all initial associated data in the initial associated data set to obtain a standard associated data set;
performing result prediction analysis on each standard associated data in the standard associated data set to generate a characteristic associated data set;
and selecting a target object from a pre-constructed target database according to the characteristic association data set to recommend the user.
2. The big-data-based object recommendation method according to claim 1, wherein performing result prediction analysis on each standard associated data in the standard associated data set to generate a feature associated data set comprises:
performing result prediction analysis on the standard associated data set to obtain an analysis result set;
performing occurrence probability calculation on each analysis result in the analysis result set to obtain a probability result set;
generating a number which is the same as the standard associated data for each probability result in the probability result set to obtain a probability result number set;
and merging the items with the same number in the probability result number set and the standard associated data set to obtain the characteristic associated data set.
3. The big-data-based object recommendation method according to claim 2, wherein performing result prediction analysis on the standard associated data set to obtain an analysis result set comprises:
step A: randomly generating a training initial association data set and a standard analysis result set corresponding to the training initial association data set;
and B: performing predictive analysis on the training initial association data set by using a convolutional neural network to obtain a training analysis result set;
and C: comparing and judging the training analysis result set and the standard analysis result set, if the training analysis result set and the standard analysis result set are different, adjusting parameters of the neural network, and returning to the step B to continue to execute conversion;
step D: if the training analysis result set and the standard analysis result set do not have difference, finishing the training;
step E: and performing predictive analysis on the standard associated data set by using the trained convolutional neural network to obtain an analysis result set.
4. The big-data-based object recommendation method according to claim 3, wherein the comparing and determining the training analysis result set and the standard analysis result set comprises:
carrying out similarity calculation on the training analysis result set and the standard analysis result set to obtain a similarity value;
comparing and judging the similarity value with a preset threshold value, and if the similarity value is smaller than or equal to the preset threshold value, standardizing the standard analysis result set and the training analysis result set to have difference;
and if the similarity value is larger than the preset threshold value, the standard analysis result set and the training analysis result set are normalized to have no difference.
5. The big-data-based object recommendation method of claim 4, wherein the performing similarity calculation on the training analysis result set and the standard analysis result set to obtain a similarity value comprises:
similarity calculation is performed using the following similarity formula:
Simtopic＝Pearson(TPS，TPT)
wherein SimtopicRepresenting the similarity of the training analysis result set and the standard analysis result set; TPTAnalyzing a result set for the training; TPSIs that it isA set of standard analysis results.
6. The big-data-based object recommendation method according to claim 2, wherein performing occurrence probability calculation on each analysis result in the analysis result set to obtain a probability result set comprises:
the probability of occurrence calculation is performed using the following formula:
wherein,
is the occurrence probability; y is the analysis result set; y istIs an analysis result in the analysis result set; k is standard associated data in the standard associated data set; a is the reciprocal of the sum of the number of elements in the analysis result set and the standard association data set.
7. The big data based object recommendation method according to any one of claims 1 to 6, wherein selecting an object from a pre-constructed object database to recommend to the user according to the feature association data set comprises:
acquiring feature associated data in the feature associated data set;
according to the mapping relation between the characteristic correlation data and the target object, selecting the target object in the target object database to obtain a recommended target object;
and outputting the characteristic correlation data and the recommended target object.
8. An object recommendation device based on big data, the device comprising:
the information collection module is used for acquiring a basic information data set of a user and retrieving an original associated data set of the basic information data set from a network according to the basic information data set;
the information processing module is used for executing exception removal preprocessing operation on the original associated data set to obtain an initial associated data set, and numbering all initial associated data in the initial associated data set to obtain a standard associated data set;
the prediction analysis module is used for performing result prediction analysis on each standard associated data in the standard associated data set to generate a characteristic associated data set;
and the target object recommending module is used for selecting a target object from a pre-constructed target object database according to the characteristic association data set so as to recommend the user.
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 memory stores instructions executable by the at least one processor to enable the at least one processor to perform a big data based object recommendation method as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the big-data based object recommendation method according to any one of claims 1 to 7.
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