CN115204971A - Product recommendation method and device, electronic equipment and computer-readable storage medium - Google Patents
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Abstract
The invention relates to an artificial intelligence technology, and discloses a product recommendation method based on a user portrait and a knowledge graph, which comprises the following steps: acquiring basic information of a user and behavior information of the user to generate a target user portrait; the method comprises the steps of obtaining product data of a plurality of products and user feedback data of each product, and generating a product portrait of each product; calculating the correlation degree of the product portrait and constructing a knowledge graph of the product; calculating matching values of the target user portrait and product portraits in the knowledge graph, and collecting products corresponding to the product portraits of which the matching values are greater than a preset threshold value as first products to be recommended; and determining a second product to be recommended according to the knowledge graph, and recommending the product to be recommended to the user. In addition, the invention also relates to a block chain technology, and the data list can be stored in the node of the block chain. The invention also provides a product recommendation device, electronic equipment and a storage medium. The invention can improve the accuracy of product 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 user portrait and knowledge graph, electronic equipment and a computer readable storage medium.
Background
The continuous abundance of financial institution product systems provides more selection opportunities for users, but also makes marketing work more troublesome, and particularly when facing new customers, the first opening of the product recommended by marketers may directly affect the affinity of the customers to the product, and how to grasp the pain points of the customers becomes especially important.
The existing product recommendation method is mostly based on single product data to generate a product image, feedback information and behavior information of a user are not obtained, data integration analysis is carried out, meanwhile, a user portrait is generated based on single user data, and then a product is selected by using a matching value of the product portrait and the user image to recommend the user. In the method, because the data is single and cannot support accurate product recommendation, the product recommendation is realized by generating the portrait by only the product data and the user data, and the accuracy of the product recommendation is low.
Disclosure of Invention
The invention provides a product recommendation method and device based on user portrait and a knowledge graph and a computer readable storage medium, and mainly aims to solve the problem of low precision in product recommendation.
In order to achieve the purpose, the invention provides a product recommendation method based on user portrait and knowledge graph, which comprises the following steps:
acquiring basic information of a user, and generating a standard user portrait according to the basic information;
acquiring behavior information of the user, and adding the behavior information into the standard user portrait to generate a target user portrait;
the method comprises the steps of obtaining product data of a plurality of products and user feedback data of each product, and generating a product portrait of each product by using the product data and the user feedback data;
calculating the association degree of the product portrait, and constructing a knowledge graph of the product by using the association degree;
calculating matching values of the target user portrait and product portraits in the knowledge graph, and collecting products corresponding to the product portraits of which the matching values are greater than a preset threshold value as first products to be recommended;
and determining the product with the correlation degree larger than the correlation degree threshold value with the first product to be recommended as a second product to be recommended according to the correlation degree of the product in the knowledge graph, and recommending the first product to be recommended and the second product to be recommended to the user.
Optionally, the generating a standard user representation according to the basic information includes:
selecting one of the basic information as target information;
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.
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 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.
Optionally, the generating a product representation of each product using the product data and the user feedback data includes:
collecting the historical data and the user feedback data as product data, and performing word segmentation processing on the product data one by one to obtain product word segmentation;
counting the occurrence frequency of the product participles, and selecting a frequency greater than a preset frequency threshold value as a target participle;
randomly selecting any product as a product to be analyzed, calculating the distance between the target participles and the product to be analyzed one by one, and selecting the target participles with the distance smaller than a preset distance threshold value as keywords of the product to be analyzed;
and counting the keywords to generate a product portrait of the product to be analyzed.
Optionally, constructing a knowledge graph of the product by using the association degree, including:
selecting one of the product images one by one to serve as a target product image;
carrying out unique ID coding on the product participles of the target product portrait to obtain product participles ID;
counting the occurrence frequency of each product word segmentation ID in the target product portrait to obtain an ID word frequency;
assigning the ID word frequency to a blank matrix to obtain a word frequency statistical matrix;
calculating the word frequency statistical matrix by using a preset weight algorithm to obtain the target weight of the target product portrait, and collecting the product portrait of which the target weight is greater than the preset weight as a strongly-associated product portrait of the target product portrait;
and acquiring the association degree of the strong association product portrait according to a preset association degree algorithm, and constructing a knowledge graph of the product by using the association degree.
Optionally, the calculating a matching value of the target user representation and the product representation in the knowledge-graph includes:
calculating a matching value of the target user portrait and the product portrait in the knowledge-graph by using the following formula:
wherein, A = (a) 1 ,a 2 ,...,a i ,...,a n )，B＝(b 1 ,b 2 ,...,b i ,...,b n ) Cosx is the matching value, A is the target user portrait, B is the product portrait, a i Segmenting words for the ith user in the target user portrait, b i For the ith product participle in the strongly associated content set, m represents the number of the user participles in the target user representation, and n represents the number of the product participles in the product representation.
In order to solve the above problems, the present invention further provides a product recommendation apparatus based on a user portrait and a knowledge graph, the apparatus comprising:
the basic information module is used for acquiring basic information of a user and generating a standard user portrait according to the basic information;
the behavior information module is used for acquiring behavior information of the user, adding the behavior information into the standard user portrait and generating a target user portrait;
the product portrait module is used for acquiring product data of a plurality of products and user feedback data of each product and generating a product portrait of each product by using the product data and the user feedback data;
the knowledge graph module is used for calculating the association degree of the product portrait and constructing a knowledge graph of the product by using the association degree;
the matching value module is used for calculating the matching value of the target user portrait and the product portrait in the knowledge graph, and collecting a product corresponding to the product portrait of which the matching value is greater than a preset threshold value as a first to-be-recommended product;
and the product recommending module is used for determining a product with the association degree larger than the association degree threshold value with the first product to be recommended as a second product to be recommended according to the association degree of the products in the knowledge graph, and recommending the first product to be recommended and the second product to be recommended 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 content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the user representation and knowledge-graph 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 user portrait and knowledge map.
According to the embodiment of the invention, the user portrait is generated by acquiring the basic information and the behavior information of the user, so that the tendency of a main resident can be mastered more quickly when the user is recommended with a product; the product information and the user feedback data of each product are obtained, the product portrait of each product is generated, the product portrait is used for generating the knowledge graph of the product, blind product recommendation is avoided, loss of customers is prevented, the matching value of the target user portrait and the product portrait in the knowledge graph is calculated, the product with the highest matching degree with the target user can be obtained, and accurate recommendation is achieved.
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FIG. 1 is a schematic flow chart of a product recommendation method based on a user profile and a knowledge graph according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of semantic extraction according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a process for generating a product representation according to an embodiment of the present invention;
FIG. 4 is a functional block diagram of a product recommendation device based on a user representation and a knowledge graph 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 a user portrait and a knowledge graph 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 a user portrait and a knowledge graph. The execution subject of the product recommendation method based on the user portrait and the knowledge graph includes, but is not limited to, at least one of the electronic devices such as a server and a terminal, which can be configured to execute the method provided by the embodiment of the application. In other words, the product recommendation method based on the user portrait and the knowledge graph can be executed by software or hardware installed in the terminal device or the server device, and the software can be a block chain 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 cloud service, a cloud database, cloud computing, cloud functions, cloud storage, web service, cloud communication, middleware service, domain name service, security service, content Delivery Network (CDN), and a big data and artificial intelligence platform.
Referring to fig. 1, a flowchart of a product recommendation method based on a user portrait and a knowledge graph according to an embodiment of the present invention is shown. In this embodiment, the product recommendation method based on the user portrait and the knowledge graph 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 related to the user, such as name, age, occupation, address, mobile phone number, marital status and the like.
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 product recommendation for users, the acquired 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; 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 the basic information into the standard user portrait.
In detail, the standard user representation may include user name, age, gender, income, risk preferences, and the like.
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, the target information can be subjected to convolution and pooling processing 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 is a point in a two-dimensional plane, a mapping function may be used to calculate a two-dimensional coordinate of the point in the two-dimensional plane, so as to convert the two-dimensional coordinate into a three-dimensional coordinate, and the calculated three-dimensional coordinate is used to map the point to a pre-constructed three-dimensional space, so as to obtain a 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 present invention, a preset activation function may be used to calculate an output value of each feature semantic in the high-dimensional feature semantics, and a feature semantic with the output value greater than a preset output threshold is selected as an information semantic, where 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 a feature semantic a, a feature semantic B, and a feature semantic C, and the feature semantic a, the feature semantic B, and the feature semantic C are respectively calculated by using an activation function, so that an output value of the feature semantic a is 80, an output value of the feature semantic B is 30, and an output value of the feature semantic C is 70, and when an output threshold value is 50, the feature semantic a and the feature semantic 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, and 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 is 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 in a mode of adding corresponding column elements in the two vectors.
In the embodiment of the invention, the two vectors can be used for generating the matrix in a mode of parallelly displaying corresponding column elements in the two vectors, so that the column dimensionality 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 elements of the corresponding column in the information vector can be displayed in parallel to obtain a matrix
And using the matrix as the standard user representation.
S2, acquiring behavior information of the user, and adding the behavior information into the standard user portrait to generate a target user portrait;
in the embodiment of the present invention, the acquiring of the behavior information of the user may perform a buried point setting, acquire the required behavior information, and perform data integration analysis according to operations of clicking, browsing, and the like of the user on each product. When the user redeems or takes a position on the fund product, the behavior information can comprise position taking information, position taking income, position taking risk grade, position taking time, position taking record, position taking income and the like related to the fund.
Specifically, a behavior tag of the behavior information and a portrait tag of the standard user portrait are generated, and the behavior tag and the portrait tag are collected to obtain a tag of the target user portrait.
S3, acquiring historical data of a plurality of products and user feedback data of each product, and generating a product portrait of each product by using the historical data and the user feedback data;
in the embodiment of the invention, the historical data refers to the holding information of the fund previously held by the user, and can comprise fund types, fund names, fund holding time and the like; the user feedback data can comprise the position adjusting data of the user, the user A adjusts the held product A into a product B, the product to be adjusted is inquired according to the fund product, and then the user system regularly updates the position holding change, the income and other information of the user every day.
In an embodiment of the present invention, the generating a product representation of each product by using the product data and the user feedback data includes:
s31, collecting the historical data and the user feedback data as product data, and performing word segmentation processing on the product data one by one to obtain product word segments;
s32, counting the occurrence frequency of the product word segmentation, and selecting a frequency threshold value larger than a preset frequency threshold value as a target word segmentation;
s33, randomly selecting any product as a product to be analyzed, calculating the distance between the target word segmentation and the product to be analyzed one by one, and selecting the target word segmentation of which the distance is smaller than a preset distance threshold value as a keyword of the product to be analyzed;
and S34, counting the keywords to generate a product portrait of the product to be analyzed.
In detail, the preset frequency threshold is a critical value showing product characterization, and when the product word segmentation frequency is high, a certain word segmentation can be used for representing the product as a target word segmentation.
In detail, the calculating of the distance between the target segmented word and the product to be analyzed may be performed using a euclidean distance. The Euclidean distance is suitable for solving the distance of a straight line between two points, and is suitable for the condition that each vector standard is unified, such as the use amount of various medicines, the sale amount of commodities and the like. Calculating the distance between the target word segmentation and the product to be analyzed by using the following formula:
wherein, x is i Is the ith target word segmentation vector after the vectorization of the target word segmentation characteristics, y i The vector is the ith product vector to be analyzed after the characteristic vectorization of the product to be analyzed, and n is the number of vectors obtained after the characteristic vectorization of the target word segmentation.
S4, calculating the correlation degree of the product portrait, and constructing a knowledge graph of the product by using the correlation degree;
in the embodiment of the invention, the association degree knowledge graph is essentially a knowledge base of a semantic network, and the knowledge graph can be simply understood as a multi-relation graph. A graph is composed of nodes and edges, and the multi-relationship graph generally contains multiple types of nodes and multiple types of edges. Entities (nodes) refer to things in the real world such as people, place names, concepts, drugs, companies, etc., and relationships (edges) are used to express some kind of connection between different entities.
In the embodiment of the present invention, the calculating the association degree of the product portrait and constructing a knowledge graph of the product by using the association degree includes: selecting one of the product images one by one to serve as a target product image; carrying out unique ID coding on the product participles of the target product portrait to obtain product participles ID; counting the occurrence frequency of each product word segmentation ID in the target product portrait to obtain an ID word frequency; assigning the ID word frequency to a blank matrix to obtain a word frequency statistical matrix; calculating the word frequency statistical matrix by using a preset weight algorithm to obtain the target weight of the target product portrait, and collecting the product portrait of which the target weight is greater than the preset weight as a strongly-associated product portrait of the target product portrait; and acquiring the association degree of the strong association product portrait according to a preset association degree algorithm, and constructing a knowledge graph of the product by using the association degree.
In detail, the target product representation may be a product representation among a number of the product representations; the ID has uniqueness, can represent the attribute of each product portrait, and has the characteristic of one-to-one correspondence. For example, taobao user ID, each member has a unique user ID, which represents identity and qualification, and for example, the identity number of each person can be used to distinguish citizenship.
In detail, the word frequency statistical matrix is a matrix representing the frequency of occurrence of the ID word frequency; the weighting algorithm needs to be selected in combination with the characteristic situation of the data, for example, the volatility between the data is an information amount, and a CRITIC weighting method or an information amount weighting method can be considered; or the expert scores data, an AHP (analytic hierarchy process) hierarchical method or a priority graph method can be used, and the preset weight algorithm can also comprise an entropy value method, an independence weight, a principal component analysis method and the like.
S5, calculating matching values of the target user portrait and product portraits in the knowledge graph, and collecting products corresponding to the product portraits of which the matching values are larger than a preset threshold value as first products to be recommended;
in an embodiment of the present invention, the calculating a matching value between the target user portrait and the product portrait in the knowledge graph includes:
calculating a matching value of the target user portrait and the product portrait in the knowledge-graph by using the following formula:
wherein, A = (a) 1 ,a 2 ,...,a i ,...,a n )，B＝(b 1 ,b 2 ,...,b i ,...,b n ) Cosx is the matching value, A is the target user representation, B is the product representation, a i Segmenting words for ith user in the target user portrait，b i For the ith product participle in the strongly associated content set, m represents the number of the user participles in the target user representation, and n represents the number of the product participles in the product representation.
In the embodiment of the present invention, the preset threshold is determined based on a similarity between the target user portrait and a product portrait in the knowledge graph, and is used for determining a recommendation order of the product.
S6, determining that the product with the correlation degree larger than the correlation degree threshold value with the first product to be recommended is a second product to be recommended according to the correlation degree of the product in the knowledge graph, and recommending the first product to be recommended and the second product to be recommended to the user.
In the embodiment of the invention, the construction of the knowledge graph is an iterative updating process, and each iteration comprises three stages according to the logic of knowledge acquisition: information extraction, knowledge fusion and knowledge processing. The information extraction is to extract entities, attributes and interrelations among the entities from various types of data sources, and form ontology knowledge expression on the basis; the knowledge fusion is to integrate new knowledge after obtaining it to eliminate contradictions and ambiguities, for example, some entities may have multiple expressions, a certain name may correspond to multiple different entities, etc.; the knowledge processing is to add qualified parts into the knowledge base after quality evaluation (part of the knowledge needs to be manually screened) for the new fused knowledge so as to ensure the quality of the knowledge base.
In detail, the association degree of the products in the knowledge graph is obtained by analyzing product information during construction of the knowledge graph, and represents the similarity between the products, the product closest to the product can be obtained from the first product to be recommended, so that the product with the association degree greater than the association degree threshold value with the first product to be recommended is determined to be a second product to be recommended, and the first product to be recommended and the second product to be recommended are recommended to the user.
According to the embodiment of the invention, the user portrait is generated by acquiring the basic information and the behavior information of the user, so that the tendency of a main resident can be mastered more quickly when the user is recommended; the product information and the user feedback data of each product are obtained, the product portrait of each product is generated, the product portrait is used for generating the knowledge graph of the product, blind product recommendation is avoided, customer loss is prevented, the matching value of the target user portrait and the product portrait in the knowledge graph is calculated, the product with the highest matching degree with the target user can be obtained, and accurate recommendation is achieved.
Fig. 4 is a functional block diagram of a product recommendation apparatus based on a user profile and a knowledge graph according to an embodiment of the present invention.
The product recommendation device 100 based on the user portrait and the knowledge graph can be installed in electronic equipment. Depending on the functionality implemented, the user representation and knowledge graph based product recommendation device 100 may include a base information module 101, a behavior information module 102, a product representation module 103, a knowledge graph module 104, a match value module 105, and a product recommendation module 106. 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 basic information module is used for acquiring basic information of a user and generating a standard user portrait according to the basic information;
the behavior information module is used for acquiring behavior information of the user, adding the behavior information into the standard user portrait and generating a target user portrait;
the product portrait module is used for acquiring product data of a plurality of products and user feedback data of each product and generating a product portrait of each product by using the product data and the user feedback data;
the knowledge graph module is used for calculating the association degree of the product portrait and constructing a knowledge graph of the product by using the association degree;
the matching value module is used for calculating matching values of the target user portrait and the product portraits in the knowledge graph, and collecting products corresponding to the product portraits of which the matching values are larger than a preset threshold value as first to-be-recommended products;
the product recommending module is used for determining a product with the association degree larger than an association degree threshold value with the first product to be recommended as a second product to be recommended according to the association degree of the product in the knowledge graph, and recommending the first product to be recommended and the second product to be recommended to the user.
Fig. 5 is a schematic structural diagram of an electronic device for implementing a product recommendation method based on a user portrait and a knowledge graph according to an embodiment of the present invention.
The electronic device 1 may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may further include a computer program, such as a product recommendation program based on a user representation and a knowledge graph, 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 whole 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 stored in the memory 11 (for example, executing a product recommendation program based on a user profile and a knowledge graph, etc.), and calling data stored in the memory 11.
The memory 11 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, 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 kinds of data, such as codes of a product recommendation program based on a user profile and a knowledge graph, 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 such as charge management, discharge management, and power consumption management are implemented 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 memory 11 of the electronic device 1 stores a product recommendation program based on user images and knowledge-graphs, which is a combination of instructions that, when executed in the processor 10, implement:
acquiring basic information of a user, and generating a standard user portrait according to the basic information;
acquiring behavior information of the user, and adding the behavior information into the standard user portrait to generate a target user portrait;
the method comprises the steps of obtaining product data of a plurality of products and user feedback data of each product, and generating a product portrait of each product by using the product data and the user feedback data;
calculating the association degree of the product portrait, and constructing a knowledge graph of the product by using the association degree;
calculating matching values of the target user portrait and product portraits in the knowledge graph, and collecting products corresponding to the product portraits of which the matching values are greater than a preset threshold value as first products to be recommended;
and determining a product with the association degree larger than the association degree threshold value with the first product to be recommended as a second product to be recommended according to the association degree of the products in the knowledge graph, and recommending the first product to be recommended and the second product to be recommended 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, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM).
The present invention also provides a computer-readable storage medium, storing a computer program which, when executed by a processor of an electronic device, may implement:
acquiring basic information of a user, and generating a standard user portrait according to the basic information;
acquiring behavior information of the user, and adding the behavior information into the standard user portrait to generate a target user portrait;
the method comprises the steps of obtaining product data of a plurality of products and user feedback data of each product, and generating a product portrait of each product by using the product data and the user feedback data;
calculating the association degree of the product portrait, and constructing a knowledge graph of the product by using the association degree;
calculating matching values of the target user portrait and product portraits in the knowledge graph, and collecting products corresponding to the product portraits of which the matching values are larger than a preset threshold value as first products to be recommended;
and determining the product with the correlation degree larger than the correlation degree threshold value with the first product to be recommended as a second product to be recommended according to the correlation degree of the product in the knowledge graph, and recommending the first product to be recommended and the second product to be recommended to the user.
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 this 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 intended to illustrate the technical solutions of the present invention and not to limit the same, 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 can be made to 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 product recommendation method based on a user portrait and a knowledge graph, the method comprising:
acquiring basic information of a user, and generating a standard user portrait according to the basic information;
acquiring behavior information of the user, and adding the behavior information into the standard user portrait to generate a target user portrait;
the method comprises the steps of obtaining product data of a plurality of products and user feedback data of each product, and generating a product portrait of each product by using the product data and the user feedback data;
calculating the correlation degree of the product portrait, and constructing a knowledge graph of the product by using the correlation degree;
calculating matching values of the target user portrait and product portraits in the knowledge graph, and collecting products corresponding to the product portraits of which the matching values are larger than a preset threshold value as first products to be recommended;
and determining the product with the correlation degree larger than the correlation degree threshold value with the first product to be recommended as a second product to be recommended according to the correlation degree of the product in the knowledge graph, and recommending the first product to be recommended and the second product to be recommended to the user.
2. The user representation and knowledge-graph based product recommendation method of claim 1 wherein said generating a standard user representation from said base information comprises:
selecting one of the basic information as target information;
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.
3. The user portrait and knowledge-graph based product recommendation method of claim 2, wherein the extracting core semantics from the 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 user representation and knowledge-graph based product recommendation method of claim 2, wherein the 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 user representation and knowledge-graph based product recommendation method of claim 1, wherein generating a product representation for each product using the product data and the user feedback data comprises:
collecting the historical data and the user feedback data as product data, and performing word segmentation processing on the product data one by one to obtain product word segmentation;
counting the occurrence frequency of the product segmentation, and selecting the frequency greater than a preset frequency threshold value as a target segmentation;
randomly selecting any product as a product to be analyzed, calculating the distance between the target participles and the product to be analyzed one by one, and selecting the target participles with the distance smaller than a preset distance threshold value as keywords of the product to be analyzed;
and counting the keywords to generate a product portrait of the product to be analyzed.
6. The user representation and knowledge-graph based product recommendation method of claim 1, wherein building a knowledge-graph of a product using the relevancy, comprises:
selecting one of the product images one by one to serve as a target product image;
carrying out unique ID coding on the product participles of the target product portrait to obtain product participle IDs;
counting the occurrence frequency of each product word segmentation ID in the target product portrait to obtain an ID word frequency;
assigning the ID word frequency to a blank matrix to obtain a word frequency statistical matrix;
calculating the word frequency statistical matrix by using a preset weight algorithm to obtain a target weight of the target product portrait, and collecting the product portrait with the target weight larger than the preset weight as a strongly-associated product portrait of the target product portrait;
and acquiring the association degree of the strongly associated product portrait according to a preset association degree algorithm, and constructing a knowledge graph of the product by using the association degree.
7. The user representation and knowledge-graph based product recommendation method of any of claims 1-6, wherein said calculating matching values of the target user representation to product representations in the knowledge-graph comprises:
calculating a matching value of the target user portrait and the product portrait in the knowledge-graph by using the following formula:
wherein, A = (a) 1 ,a 2 ,...,a i ,...,a n )，B＝(b 1 ,b 2 ,...,b i ,...,b n ) Cosx is the matching value, A is the target user representation, B is the product representation, a i Segmenting words for the ith user in the target user representation, b i Segmenting the ith product in the strongly associated content set, m representing the number of user segments in the target user representation, n representing the number of user segmentsThe number of the product participles in the product portrait.
8. A product recommendation device based on a user representation and a knowledge graph, the device comprising:
the basic information module is used for acquiring basic information of a user and generating a standard user portrait according to the basic information;
the behavior information module is used for acquiring behavior information of the user, adding the behavior information into the standard user portrait and generating a target user portrait;
the product portrait module is used for acquiring product data of a plurality of products and user feedback data of each product and generating a product portrait of each product by using the product data and the user feedback data;
the knowledge graph module is used for calculating the association degree of the product portrait and constructing a knowledge graph of the product by using the association degree;
the matching value module is used for calculating the matching value between the target user portrait and the product portrait in the knowledge map, and collecting the product corresponding to the product portrait of which the matching value is greater than a preset threshold value as a first to-be-recommended product;
and the product recommending module is used for determining a product with the association degree larger than the association degree threshold value with the first product to be recommended as a second product to be recommended according to the association degree of the products in the knowledge graph, and recommending the first product to be recommended and the second product to be recommended 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 first and the second end of the pipe are connected with each other,
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 user representation and knowledge-graph 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 a user representation and knowledge graph based product recommendation method according to any one of claims 1 to 7.
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