CN115186188A - Product recommendation method, device and equipment based on behavior analysis and storage medium - Google Patents
Product recommendation method, device and equipment based on behavior analysis and storage medium Download PDFInfo
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Abstract
The invention relates to an artificial intelligence technology, and discloses a product recommendation method based on behavior analysis, which comprises the following steps: generating a standard user portrait according to the basic information of the user; adding specific behavior information of the user into the standard user portrait, and inquiring N users with the specific behavior information as associated users; acquiring a related user portrait of a related user, calculating a distance value between a standard user portrait and the related user portrait, and determining the related user of the related user portrait with the distance value smaller than a preset threshold value as a target user; acquiring specific behavior information of a target user, and counting preference probability of the user on different information according to the specific behavior information of the target user; and generating an information recommendation list according to the preference probability sequence and outputting the information recommendation list to the user. In addition, the invention also relates to a block chain technology, and the basic information can be stored in the nodes of the block chain. The invention also provides a product recommendation device, equipment and a medium based on the behavior analysis. The invention can improve the accuracy of information recommendation.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a product recommendation method and device based on behavior analysis, electronic equipment and a computer-readable storage medium.
Background
Today, with e-commerce, social platforms and short videos emerging, each large internet platform provides personalized information recommendation for users by using data information browsed, browsed and collected by users, and a recommendation system slowly replaces an old inherent information searching and filtering way, and becomes an important way for users to acquire different information.
Two recommendation engine algorithms that are currently mainly used in the industry are: content-based recommendations and collaborative filtering-based recommendations. But both have common disadvantages: the sparseness problem. When the user data volume of the service system is not large enough, the sparse problem is generated, and some users and articles lack similar classes which can not be matched with effective data, so that the recommendation cannot be completed.
Disclosure of Invention
The invention provides a product recommendation method and device based on behavior analysis and a computer readable storage medium, and mainly aims to solve the problem of low precision in product recommendation.
In order to achieve the above object, the present invention provides a product recommendation method based on behavior analysis, including:
acquiring basic information of a user, and generating a standard user portrait according to the basic information;
acquiring specific behavior information of the user, adding the specific behavior information into the standard user portrait, and querying N users with the specific behavior information as associated users;
acquiring the associated user portrait of each associated user, respectively calculating a distance value between the standard user portrait and each associated user portrait, and determining the associated user corresponding to the associated user portrait of which the distance value is smaller than a preset threshold value as a target user;
acquiring specific behavior information of each target user, and counting preference probability of the user on different information according to the specific behavior information of each target user;
and generating an information recommendation list according to the sequence from large preference probability to small preference probability, and outputting the information recommendation list to the user.
Optionally, the generating a standard user representation according to the basic information includes:
selecting one piece of information from the basic information as target information one by one;
performing core semantic extraction on the target information to obtain information semantics;
performing vector conversion on the core semantics to obtain a semantic vector;
and splicing semantic vectors corresponding to all basic information into the standard user portrait.
Optionally, the extracting core semantics from the target information to obtain information semantics includes:
performing convolution and pooling on the target information to obtain low-dimensional feature semantics of the target information;
mapping the low-dimensional feature semantics to a pre-constructed high-dimensional space to obtain high-dimensional feature semantics;
and screening the high-dimensional characteristic semantics by using a preset activation function to obtain information semantics.
Optionally, the stitching the semantic vectors corresponding to all the basic information into the standard user portrait includes:
counting the vector lengths of all vectors in the information vector;
determining the maximum value in the vector lengths as a target length;
utilizing preset parameters to prolong the lengths of all information vectors to the target length;
and merging the column dimensions of all the information vectors after the length is prolonged to obtain the standard user portrait.
Optionally, the querying N users having the specific behavior information is associated users, and includes:
acquiring a specific behavior information table of M users, wherein M is greater than or equal to N;
constructing an index of the specific behavior information table;
retrieving in the index according to the specific behavior information to obtain an index containing the specific behavior information;
and determining the user corresponding to the index containing the specific behavior information as the associated user.
Optionally, said calculating a distance value between said standard user representation and each said associated user representation separately comprises:
calculating a distance value between the standard user representation and each of the associated user representations using the following distance value algorithm, respectively:
wherein D is the distance value, x is the standard user representation, y i Is the ith associated user representation.
Optionally, the counting preference probabilities of the users for different information according to the specific behavior information of each target user includes:
selecting one target user from the target users one by one;
counting the browsing times of different information in the specific behavior information of the selected target user;
calculating the total browsing times of all the target users for each different information according to the browsing times of each selected target user for the different information;
selecting one piece of information from the different pieces of information one by one as target information, and calculating the proportion weight of the total times of browsing the target information by all target users in the sum of the total times of browsing the different pieces of information by all target users;
and determining the proportion weight as the preference probability of the user on the target information.
In order to solve the above problems, the present invention also provides a product recommendation apparatus based on behavior analysis, the apparatus comprising:
the portrait generation module is used for acquiring basic information of a user and generating a standard portrait of the user according to the basic information;
the user query module is used for acquiring specific behavior information of the user, adding the specific behavior information into the standard user portrait and querying N users with the specific behavior information as associated users;
the user screening module is used for acquiring the associated user portrait of each associated user, respectively calculating a distance value between the standard user portrait and each associated user portrait, and determining the associated user corresponding to the associated user portrait of which the distance value is smaller than a preset threshold value as a target user;
the probability calculation module is used for acquiring the specific behavior information of each target user and counting the preference probability of the user to different information according to the specific behavior information of each target user;
and the information recommendation module is used for generating an information recommendation list according to the sequence of the preference probabilities from large to small and outputting the information recommendation list to the user.
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 memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the behavioral analysis-based product recommendation method described above.
In order to solve the above problem, the present invention further provides a computer-readable storage medium, in which at least one computer program is stored, and the at least one computer program is executed by a processor in an electronic device to implement the product recommendation method based on behavior analysis.
According to the embodiment of the invention, the associated user having an association relation with the user can be inquired through the browsing record of the information by the user, the target user with higher similarity with the user is screened out from the associated user by utilizing the user portrait generated by the basic information of the user, and the specific behavior information of the target user is further analyzed, so that the information recommendation of the user is realized, and the accuracy of the information recommendation of the user can be improved by screening and analyzing the associated user even when the specific behavior information of the user is less. Therefore, the product recommendation method, the product recommendation device, the electronic equipment and the computer readable storage medium based on the behavior analysis can solve the problem that the accuracy of information recommendation for users is low.
Drawings
Fig. 1 is a schematic flowchart of a product recommendation method based on behavior analysis according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of core semantic extraction according to an embodiment of the present invention;
fig. 3 is a schematic flowchart of querying associated users according to an embodiment of the present invention;
FIG. 4 is a functional block diagram of a product recommendation device based on behavior analysis according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device implementing the product recommendation method based on behavior analysis 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 product recommendation method based on behavior analysis. The execution subject of the product recommendation method based on behavior analysis 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 embodiments of the present application. In other words, the product recommendation method based on behavior analysis 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. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Referring to fig. 1, a flowchart of a product recommendation method based on behavior analysis according to an embodiment of the present invention is shown. In this embodiment, the product recommendation method based on behavior analysis includes:
s1, acquiring basic information of a user, and generating a standard user portrait according to the basic information.
In the embodiment of the invention, the basic information of the user comprises data information which is related to the user, such as name, age, occupation, address and marital status.
In detail, a computer sentence with data crawling function (such as java sentence, python sentence, etc.) can be used to crawl the stored basic information from a predetermined storage area, including but not limited to a database, a block chain node, a network cache.
Further, in order to implement recommendation of information to a user, the obtained basic information may be analyzed to generate a standard user portrait corresponding to the user according to the basic information.
In an embodiment of the present invention, the generating a standard user portrait according to the basic information includes:
selecting one piece of information from the basic information as target information one by one;
performing core semantic extraction on the target information to obtain information semantics;
performing vector conversion on the core semantics to obtain a semantic vector;
and splicing semantic vectors corresponding to all the basic information into the standard user portrait.
In the embodiment of the present invention, the target information may be sequentially selected from the basic information, or the target information may be randomly selected from the basic information without being replaced.
In the embodiment of the invention, a pre-constructed semantic analysis model is used for extracting the core semantics of the target information to obtain the information semantics.
In detail, the semantic analysis Model includes, but is not limited to, a Natural Language Processing (NLP) Model, a Hidden Markov Model (HMM) Model.
For example, the target information is convolved, pooled and the like by using a pre-constructed semantic analysis model to extract the low-dimensional feature expression of the target information, the extracted low-dimensional feature expression is mapped to a pre-constructed high-dimensional space to obtain the high-dimensional feature expression of the low-dimensional feature, and the high-dimensional feature expression is selectively output by using a preset activation function to obtain the information semantic.
In the embodiment of the present invention, as shown in fig. 2, the extracting core semantics from the target information to obtain information semantics includes:
s21, performing convolution and pooling on the target information to obtain low-dimensional feature semantics of the target information;
s22, mapping the low-dimensional feature semantics to a pre-constructed high-dimensional space to obtain high-dimensional feature semantics;
and S23, screening the high-dimensional feature semantics by using a preset activation function to obtain information semantics.
In detail, convolution and pooling processing can be performed on the target information through a semantic analysis model so as to reduce the data dimension of the target information, further reduce the occupation of computing resources when the target information is analyzed, and improve the efficiency of core semantic extraction.
Specifically, the low-dimensional feature semantics can be mapped to the pre-constructed high-dimensional space by using a preset mapping Function, wherein the mapping Function comprises a Gaussian Radial Basis Function, a Gaussian Function and the like in the MATLAB library.
For example, if the low-dimensional feature semantics are points in a two-dimensional plane, a mapping function may be used to calculate two-dimensional coordinates of the points in the two-dimensional plane to convert the two-dimensional coordinates into three-dimensional coordinates, and the calculated three-dimensional coordinates are used to map the points to a pre-constructed three-dimensional space, so as to obtain high-dimensional feature semantics of the low-dimensional feature semantics.
And mapping the low-dimensional feature semantics to a pre-constructed high-dimensional space, so that the classifiability of the low-dimensional feature can be improved, and the accuracy of screening the features from the obtained high-dimensional feature semantics to obtain the information semantics is further improved.
In the embodiment of the invention, a preset activation function can be used for calculating an output value of each feature semantic in the high-dimensional feature semantics, and the feature semantics of which the output value is greater than a preset output threshold are selected as information semantics, wherein the activation function includes but is not limited to a sigmoid activation function, a tanh activation function and a relu activation function.
For example, the high-dimensional feature semantics include feature semantics a, feature semantics B, and feature semantics C, the feature semantics a, the feature semantics B, and the feature semantics C are respectively calculated by using an activation function, and an output value of the feature semantics a is 80, an output value of the feature semantics B is 30, and an output value of the feature semantics C is 70, and when an output threshold is 50, the feature semantics a and the feature semantics C are output as the information semantics of the target information.
In the embodiment of the invention, the information semantics can be subjected to vector conversion through a preset vector conversion model to obtain an information vector, wherein the vector conversion model comprises but is not limited to a word2vec model and a Bert model.
In the embodiment of the invention, after the information vector is obtained, the information vector can be subjected to vector splicing to generate the standard user portrait.
In the embodiment of the present invention, the splicing semantic vectors corresponding to all basic information into the standard user portrait includes:
counting the vector lengths of all vectors in the information vector;
determining the maximum value in the vector lengths as a target length;
utilizing preset parameters to prolong the lengths of all information vectors to the target length;
and merging the column dimensions of all the information vectors after the length is prolonged to obtain the standard user portrait.
In detail, since the lengths of the information vectors may not be the same, in order to perform vector concatenation on the information vectors, it is necessary to unify the vector lengths of the information vectors.
In the embodiment of the invention, the vector length of all information vectors can be compared, and the vector with shorter vector length can be subjected to vector extension, so that the vector length of all information vectors is the same.
For example, there is vector a in the information vector: [11, 36, 22], vector B: [14, 25, 31, 27], it is statistically known that the vector length of the vector a is 3, the vector length of the vector B is 4, and the vector length of the vector B is greater than the first vector length, then the vector a is vector-extended by using a preset parameter (e.g. 0) until the vector length of the vector a is equal to the vector length of the vector B, so as to obtain an extended vector a: [11, 36, 22,0].
In the embodiment of the invention, the two vectors can be subjected to column dimension combination by adding corresponding column elements in the two vectors.
In the embodiment of the invention, the matrix can be generated by utilizing the two vectors in a mode of parallelly displaying the corresponding column elements in the two vectors, so that the column dimension combination between the vectors is realized.
For example, the information vector is [11, 36, 22,0]]The second semantic vector is [14, 25, 31, 27]]Then the information can be processedElements of corresponding columns in the vectors are displayed in parallel to obtain a matrix
And using the matrix as the standard user representation.
S2, obtaining the specific behavior information of the users, and inquiring N users with the specific behavior information as associated users.
In the embodiment of the present invention, the specific behavior information includes a record of operations of the user on click times, click time distribution, page retirement rate, attachment click times, feedback suggestions, recommendation sharing, business card sharing, hot-line dialing, and the like of different information, for example, the user browses information a for 1 time and browses information B for 2 times in historical time.
In detail, the step of obtaining the specific behavior information of the user is consistent with the step of obtaining the basic information of the user in S1, and is not repeated here.
In one practical application scene, audiences of different information are inconsistent, so that certain relevance exists among different users browsing the same information, and further, a plurality of users with the specific behavior information can be inquired to be related users of the users.
In the embodiment of the present invention, referring to fig. 3, the querying N users having the specific behavior information is associated users, and includes:
s31, obtaining a specific behavior information table of M users, wherein M is larger than or equal to N;
s32, constructing an index of the specific behavior information table;
s33, retrieving in the index according to the specific behavior information to obtain the index containing the specific behavior information;
and S34, determining the user corresponding to the index containing the specific behavior information as the associated user.
In detail, the specific behavior information table may be uploaded by service personnel in advance, and the specific behavior information table includes M users and specific behavior information corresponding to each user.
Specifically, the CREATE INDEX function in the SQL library can be utilized to CREATE an INDEX to the table of specific behavior information.
Illustratively, the INDEX of the specific behavior information table may be created using the CREATE INDEX function as follows:
CREATE INDEX index-name
ON table-name(column-name)
the index-name is the name of the created index, the table-name is the table name of the specific behavior information table, and the column-name is the column name of the data column needing to create the index in the specific behavior information table.
In the embodiment of the present invention, the specific behavior information may be retrieved from the index, an index including the specific behavior information is obtained, and a user corresponding to the index including the specific behavior information is determined as the associated user.
In the embodiment of the invention, the associated user of the user is analyzed through the specific behavior information, so that the associated user can be searched when the browsing record of the user is less, information recommendation can be subsequently performed on the user according to the data of the associated user, and the accuracy of information recommendation on the user when the browsing record of the user is less is improved.
And S3, obtaining the associated user portrait of each associated user, respectively calculating a distance value between the standard user portrait and each associated user portrait, and determining the associated user corresponding to the associated user portrait of which the distance value is smaller than a preset threshold value as a target user.
In the embodiment of the present invention, the associated user portrait is a portrait corresponding to each associated user generated in advance according to the basic information of each associated user and the step of generating a standard user portrait according to the basic information as in S1.
In one practical application scenario of the invention, since the associated users are obtained by analyzing a small number of browsing records of the users, if information recommendation is directly performed on the users according to the associated users, the accuracy of the information recommendation is reduced, and therefore, the distance value between the standard user portrait and each associated user portrait can be respectively calculated, so as to screen out the associated users corresponding to the associated user portraits of which the distance values are smaller than a preset threshold value as target users, thereby realizing screening of the associated users and improving the accuracy of subsequent information recommendation on the users.
In an embodiment of the present invention, the calculating the distance value between the standard user representation and each of the associated user representations respectively includes:
calculating a distance value between the standard user representation and each of the associated user representations using the following distance value algorithm, respectively:
wherein D is the distance value, x is the standard user portrait, y i Is the ith associated user representation.
In detail, the associated user corresponding to the associated user image with the distance value smaller than the preset threshold value between the standard user image and the associated user image can be screened out as the target user.
S4, obtaining the specific behavior information of each target user, and counting the preference probability of the user to different information according to the specific behavior information of each target user.
In the embodiment of the present invention, the specific behavior information of the target user includes a record of information browsed by each target user in a historical time, for example, data such as browsing time and browsing times of the associated user X on the information a and the information B in the historical time, and data such as browsing time and browsing times of the associated user Y on the information a and the information B in the historical time.
In detail, the step of obtaining the specific behavior information of each target user is consistent with the step of obtaining the specific behavior information of the user in S2, and is not described herein again.
In an embodiment of the present invention, the counting preference probabilities of the users for different information according to the specific behavior information of each target user includes:
selecting one target user from the target users one by one;
counting the browsing times of different information in the specific behavior information of the selected target user;
calculating the total browsing times of all the target users for each different information according to the browsing times of each selected target user for the different information;
selecting one piece of information from the different pieces of information one by one as target information, and calculating the proportion weight of the total times of browsing the target information by all target users in the sum of the total times of browsing the different pieces of information by all target users;
and determining the proportion weight as the preference probability of the user on the target information.
For example, the target users include user a and user B, where the browsing times of user a on information a are 10 times, the browsing times of user a on information B are 40 times, the browsing times of user B on information a are 30 times, and the browsing times of user B on information B are 20 times, then the total number of times that information a is browsed by all target users (user a and user B) is 40 times, and the total number of times that information B is browsed by all target users (user a and user B) is 60 times, and further, the ratio weight of the total number of times that information a is browsed by all target users to the sum of the total number of times that each different information is browsed by all target users is 40%, and the ratio weight of the total number of times that information B is browsed by all target users to the sum of the total number of times that each different information is browsed by all target users is 60% can be calculated, so that the preference probability of user on information a is determined to be 40%, and the preference probability of user on information a is 60%.
In the embodiment of the invention, the preference probability of the user to different information is determined by comprehensively analyzing the specific behavior information of each target user, so that the accuracy of information recommendation to the user in the follow-up process is improved.
And S5, generating an information recommendation list according to the sequence from large preference probability to small preference probability, and outputting the first K pieces of information of the information recommendation list to the user.
In the embodiment of the invention, the information recommendation list can be generated according to the sequence of the preference probabilities from large to small, and then the front K pieces of information in the information recommendation list are output to the user, so that the information recommendation of the user is realized.
According to the embodiment of the invention, the associated users having an associated relationship with the users can be inquired through browsing records of the information by the users, the target users with higher similarity with the users are screened out from the associated users by utilizing the user portrait generated by the basic information of the users, and the specific behavior information of the target users is further analyzed, so that the information recommendation of the users is realized, and the accuracy of the information recommendation of the users can be improved by screening and analyzing the associated users even when the specific behavior information of the users is less. Therefore, the product recommendation method based on the behavior analysis can solve the problem that the accuracy of information recommendation for users is low.
Fig. 4 is a functional block diagram of a product recommendation apparatus based on behavior analysis according to an embodiment of the present invention.
The product recommendation apparatus 100 based on behavioral analysis according to the present invention may be installed in an electronic device. According to the realized function, the product recommending device 100 based on behavior analysis may include a representation generating module 101, a user querying module 102, a user filtering module 103, a probability calculating module 104 and an information recommending module 105. The module of 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 portrait generation module 101 is configured to obtain basic information of a user, and generate a standard portrait of the user according to the basic information;
the user query module 102 is configured to obtain specific behavior information of the user, add the specific behavior information to the standard user representation, and query N users having the specific behavior information as associated users;
the user screening module 103 is configured to obtain an associated user portrait of each associated user, calculate a distance value between the standard user portrait and each associated user portrait respectively, and determine that an associated user corresponding to an associated user portrait of which the distance value is smaller than a preset threshold is a target user;
the probability calculation module 104 is configured to obtain specific behavior information of each target user, and calculate preference probabilities of the users for different information according to the specific behavior information of each target user;
the information recommendation module 105 is configured to generate an information recommendation list according to the order of the preference probabilities from large to small, and output the information recommendation list to the user.
In detail, when the modules in the product recommendation device 100 based on behavior analysis according to the embodiment of the present invention are used, the same technical means as the product recommendation method based on behavior analysis described in fig. 1 to 3 are adopted, and the same technical effects can be produced, which is not described herein again.
Fig. 5 is a schematic structural diagram of an electronic device for implementing a product recommendation method based on behavior analysis according to an embodiment of 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 product recommendation program based on behavioral analysis, 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, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (for example, executing a product recommendation program based on behavior analysis, 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, and the like. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device 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, provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only to store application software installed in the electronic device and various types of data, such as codes of a product recommendation program based on behavior analysis, 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 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 typically used to establish a communication connection between the electronic device and other electronic devices. 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, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
Only electronic devices having components are shown, and those skilled in the art will appreciate that the structures shown in the figures do not constitute limitations on the electronic devices, and may include fewer or more components than shown, or some components in combination, or a different arrangement of components.
For example, although not shown, the electronic device 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 that functions of charge management, discharge management, power consumption management and the like are realized 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 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 embodiments described are illustrative only and are not to be construed as limiting the scope of the claims.
The product recommendation program based on behavior analysis stored in the memory 11 of the electronic device 1 is a combination of instructions, which when executed in the processor 10, can realize:
acquiring basic information of a user, and generating a standard user portrait according to the basic information;
acquiring specific behavior information of the user, adding the specific behavior information into the standard user portrait, and querying N users with the specific behavior information as associated users;
acquiring the associated user portrait of each associated user, respectively calculating a distance value between the standard user portrait and each associated user portrait, and determining the associated user corresponding to the associated user portrait of which the distance value is smaller than a preset threshold value as a target user;
acquiring specific behavior information of each target user, and counting preference probability of the user on different information according to the specific behavior information of each target user;
and generating an information recommendation list according to the sequence from large preference probability to small preference probability, and outputting the information recommendation list to the user.
Specifically, the specific implementation method of the processor 10 for the instruction may refer to the description of the relevant steps in the embodiment corresponding to the drawing, and is not repeated here.
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 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, a recording medium, a usb-disk, a removable hard disk, a magnetic diskette, an optical disk, a computer Memory, a 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, implements:
acquiring basic information of a user, and generating a standard user portrait according to the basic information;
acquiring specific behavior information of the user, adding the specific behavior information into the standard user portrait, and querying N users with the specific behavior information as associated users;
acquiring the associated user portrait of each associated user, respectively calculating a distance value between the standard user portrait and each associated user portrait, and determining the associated user corresponding to the associated user portrait of which the distance value is smaller than a preset threshold value as a target user;
acquiring specific behavior information of each target user, and counting preference probability of the user on different information according to the specific behavior information of each target user;
and generating an information recommendation list according to the sequence from large preference probability to small preference probability, and outputting the information recommendation list to the user.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. 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.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
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 first, 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 method for recommending products based on behavioral analysis, the method comprising:
acquiring basic information of a user, and generating a standard user portrait according to the basic information;
acquiring specific behavior information of the user, adding the specific behavior information into the standard user portrait, and querying N users with the specific behavior information as associated users;
acquiring the associated user portrait of each associated user, respectively calculating a distance value between the standard user portrait and each associated user portrait, and determining the associated user corresponding to the associated user portrait of which the distance value is smaller than a preset threshold value as a target user;
acquiring specific behavior information of each target user, and counting preference probability of the user on different information according to the specific behavior information of each target user;
and generating an information recommendation list according to the sequence from large preference probability to small preference probability, and outputting the information recommendation list to the user.
2. The behavioral analysis-based product recommendation method according to claim 1, wherein said generating a standard user representation from said underlying information comprises:
selecting one piece of information from the basic information as target information one by one;
extracting core semantics of the target information to obtain information semantics;
performing vector conversion on the core semantics to obtain a semantic vector;
and splicing semantic vectors corresponding to all basic information into the standard user portrait.
3. The product recommendation method based on behavioral analysis according to claim 2, wherein said extracting core semantics from said target information to obtain information semantics comprises:
performing convolution and pooling on the target information to obtain low-dimensional feature semantics of the target information;
mapping the low-dimensional feature semantics to a pre-constructed high-dimensional space to obtain high-dimensional feature semantics;
and screening the high-dimensional characteristic semantics by using a preset activation function to obtain information semantics.
4. The behavioral analysis-based product recommendation method according to claim 2, wherein said stitching semantic vectors corresponding to all basic information into the standard user representation comprises:
counting the vector lengths of all vectors in the information vector;
determining the maximum value in the vector lengths as a target length;
utilizing preset parameters to prolong the lengths of all information vectors to the target length;
and merging the column dimensions of all the information vectors after the length is prolonged to obtain the standard user portrait.
5. The product recommendation method based on behavioral analysis according to claim 1, wherein said querying N users possessing the specific behavioral information are associated users, including:
obtaining a specific behavior information table of M users, wherein M is greater than or equal to N;
constructing an index of the specific behavior information table;
retrieving in the index according to the specific behavior information to obtain an index containing the specific behavior information;
and determining the user corresponding to the index containing the specific behavior information as the associated user.
6. The behavioral analysis-based product recommendation method according to claim 1, wherein said separately calculating distance values between said standard user representation and each of said associated user representations comprises:
calculating a distance value between the standard user representation and each of the associated user representations using the following distance value algorithm, respectively:
wherein D is the distance value, x is the standard user portrait, y i Is the ith associated user representation.
7. The product recommendation method based on behavioral analysis according to any one of claims 1 to 6, characterized in that said counting preference probabilities of said users for different information according to specific behavioral information of each said target user comprises:
selecting one target user from the target users one by one;
counting the browsing times of different information in the specific behavior information of the selected target user;
calculating the total browsing times of all target users for each different information according to the browsing times of each selected target user for the different information;
selecting one piece of information from the different pieces of information one by one as target information, and calculating the proportion weight of the total times of browsing the target information by all target users in the sum of the total times of browsing the different pieces of information by all target users;
and determining the proportion weight as the preference probability of the user on the target information.
8. A product recommendation device based on behavioral analysis, the device comprising:
the portrait generation module is used for acquiring basic information of a user and generating a standard portrait of the user according to the basic information;
the user query module is used for acquiring specific behavior information of the user, adding the specific behavior information into the standard user portrait and querying N users with the specific behavior information as associated users;
the user screening module is used for acquiring the associated user portrait of each associated user, respectively calculating a distance value between the standard user portrait and each associated user portrait, and determining the associated user corresponding to the associated user portrait of which the distance value is smaller than a preset threshold value as a target user;
the probability calculation module is used for acquiring the specific behavior information of each target user and counting the preference probability of the user to different information according to the specific behavior information of each target user;
and the information recommendation module is used for generating an information recommendation list according to the sequence of the preference probabilities from large to small and outputting the information recommendation list to 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 a computer program executable by the at least one processor to enable the at least one processor to perform the behavioral analysis-based product 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 the behavioral analysis-based product recommendation method according to any one of claims 1 to 7.
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