CN114186132A - Information recommendation method and device, electronic equipment and storage medium - Google Patents
Information recommendation method and device, electronic equipment and storage medium Download PDFInfo
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- CN114186132A CN114186132A CN202111519162.2A CN202111519162A CN114186132A CN 114186132 A CN114186132 A CN 114186132A CN 202111519162 A CN202111519162 A CN 202111519162A CN 114186132 A CN114186132 A CN 114186132A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0241—Advertisements
- G06Q30/0251—Targeted advertisements
- G06Q30/0269—Targeted advertisements based on user profile or attribute
- G06Q30/0271—Personalized advertisement
Abstract
The invention relates to an artificial intelligence technology, and discloses an information recommendation method, which comprises the following steps: constructing a matrix according to the information in the information set to be recommended; calculating the information utilization rate of each piece of information according to historical information data of the user, and updating the constructed matrix by using the information utilization rate to obtain an information utilization rate matrix; updating the information utilization rate matrix according to the matching degree of the information corresponding to each element in the information utilization rate matrix and the user to obtain an information recommendation matrix; calculating the element attenuation weight in the information recommendation matrix according to the preset attenuation factor, the preset recommendation time and the historical information data acquisition time, and updating the matrix to obtain a target recommendation matrix; and screening information corresponding to each element in the target recommendation matrix according to a preset threshold value and recommending the information to the user. The invention also relates to a block chain technology, and the target recommendation matrix can be stored in block chain link points. The invention also provides an information recommendation device, equipment and a medium. The invention can improve the accuracy of information recommendation.
Description
Technical Field
The present invention relates to artificial intelligence technologies, and in particular, to an information recommendation method and apparatus, an electronic device, and a storage medium.
Background
With the arrival of an information society, more and more information is exposed to a user every day, and information recommendation needs to be carried out on the user in order to help the user to obtain effective information in time.
The conventional information recommendation is like common service interest recommendation, only single-dimensional features of information (such as service interests) can be extracted, the features of information extraction are inaccurate, and the information required by a user cannot be accurately matched, so that the accuracy of information recommendation is low.
Disclosure of Invention
The invention provides an information recommendation method, an information recommendation device, electronic equipment and a storage medium, and mainly aims to improve the accuracy of information recommendation.
Acquiring historical information use data of a user, wherein the historical information use data comprises: acquiring times and actual response times of different types of information;
acquiring an information set to be recommended, which comprises different types of information and corresponding information labels, and constructing a matrix according to the information in the information set to be recommended to obtain an initial information matrix;
calculating the information utilization rate of each piece of information according to the acquisition times and the actual response times, and updating matrix elements of the initial information matrix by using the information utilization rate to obtain an information utilization rate matrix;
acquiring a user tag of the user, performing similarity calculation according to the user tag and the information tag to obtain a matching weight of each information, and performing weighted calculation on elements of the information corresponding to the information utilization rate matrix according to the matching weight to obtain the information recommendation matrix;
calculating according to a preset attenuation factor, preset recommendation time and acquisition time of the historical information use data to obtain an attenuation weight, and performing weighted calculation according to the attenuation weight and the information recommendation matrix to obtain a target recommendation matrix;
and screening the information corresponding to each element in the target recommendation matrix according to a preset recommendation threshold value, and recommending the screened information to the user at a preset recommendation time.
Optionally, the constructing a matrix according to the information in the information set to be recommended to obtain an initial information matrix includes:
constructing blank matrixes with the same element number according to the category number of the information in the information set to be recommended;
marking information of one type for each element in the blank matrix in sequence by using the information in the information set to be recommended; wherein, the information of any two element marks in the blank matrix is different;
and acquiring an initial weight corresponding to each piece of information in the information set to be recommended, and updating elements marked with the same type of information in the blank matrix by using the initial weight to obtain an initial information matrix.
Optionally, the performing similarity calculation according to the user tag and the information tag to obtain a matching weight of each type of information, and performing weighting calculation on an element of the information corresponding to the information usage rate matrix according to the matching weight to obtain the information recommendation matrix includes:
carrying out vector conversion on the user tag to obtain a user tag vector;
carrying out vector conversion on the information tag to obtain an information tag vector;
calculating the similarity of the user tag vector and the information tag vector to obtain the matching weight of the information corresponding to the information tag;
and updating elements in the information utilization rate matrix by using the matching weight to obtain the information recommendation matrix.
Optionally, the vector conversion of the information tag to obtain an information tag vector includes:
converting each character in the information label into a vector to obtain a corresponding character vector;
selecting the maximum value of each character vector to obtain a corresponding character characteristic value;
and combining the character characteristic values corresponding to all the character vectors according to the sequence of the characters corresponding to the character vectors in the information tag to obtain the information tag vector.
Optionally, the updating, by using the matching weight, elements in the information usage rate matrix to obtain the information recommendation matrix includes:
carrying out weighted calculation on the matching weight of each piece of information and elements of the same type of information in the information utilization rate matrix;
and replacing the element corresponding to the category information in the information utilization rate matrix with the weighted calculation result to obtain the information recommendation matrix.
Optionally, the calculating according to a preset attenuation factor, a preset recommendation time, and the acquisition time of the historical information usage data to obtain an attenuation weight includes:
calculating by using the following formula to obtain the attenuation weight:
S＝e-γ(T-t)
wherein S is the attenuation weight, gamma is the attenuation factor, T is the recommended time, and T is the acquisition time of the historical information use data.
Optionally, before the screening, according to a preset recommendation threshold, information corresponding to each element in the target recommendation matrix, the method further includes:
clustering all elements in the target recommendation matrix by using a preset clustering algorithm to obtain one or more category clusters;
calculating all elements in all the category clusters to obtain corresponding cluster characteristic values;
and selecting the maximum value of all the cluster characteristic values as the recommendation threshold value.
In order to solve the above problem, the present invention also provides an information recommendation apparatus, including:
the information matrix construction module is used for acquiring historical information use data of a user, wherein the historical information use data comprises: acquiring times and actual response times of different types of information; acquiring an information set to be recommended, which comprises different types of information and corresponding information labels, and constructing a matrix according to the information in the information set to be recommended to obtain an initial information matrix;
the information matrix updating module is used for calculating the information utilization rate of each piece of information according to the acquisition times and the actual response times, and updating matrix elements of the initial information matrix by using the information utilization rate to obtain an information utilization rate matrix; acquiring a user tag of the user, performing similarity calculation according to the user tag and the information tag to obtain a matching weight of each information, and performing weighted calculation on elements of the information corresponding to the information utilization rate matrix according to the matching weight to obtain the information recommendation matrix; calculating according to a preset attenuation factor, preset recommendation time and acquisition time of the historical information use data to obtain an attenuation weight, and performing weighted calculation according to the attenuation weight and the information recommendation matrix to obtain a target recommendation matrix;
and the information recommendation module is used for screening the information corresponding to each element in the target recommendation matrix according to a preset recommendation threshold value and recommending the screened information to the user at a preset recommendation time.
In order to solve the above problem, the present invention also provides an electronic device, including:
a memory storing at least one computer program; and
and a processor executing the computer program stored in the memory to implement the information recommendation method.
In order to solve the above problem, the present invention also provides a computer-readable storage medium having at least one computer program stored therein, the at least one computer program being executed by a processor in an electronic device to implement the information recommendation method described above.
According to the embodiment of the invention, the information utilization rate of each piece of information is calculated according to the acquisition times and the actual response times, and matrix elements of the initial information matrix are updated by utilizing the information utilization rate to obtain an information utilization rate matrix; performing similarity calculation according to the user tags and the information tags to obtain a matching weight of each information, and performing weighted calculation on elements of the information corresponding to the information utilization rate matrix according to the matching weight to obtain the information recommendation matrix; calculating according to a preset attenuation factor, preset recommendation time and acquisition time of the historical information use data to obtain an attenuation weight, and performing weighted calculation according to the attenuation weight and the information recommendation matrix to obtain a target recommendation matrix; according to the information recommendation method and device, the information suitable for the user is screened through three dimensions of the historical characteristics of the information, the matching characteristics of the user and the recommendation time characteristics, the information screening accuracy is higher, and the information recommendation is more accurate, so that the information recommendation method and device, the electronic device and the readable storage medium provided by the embodiment of the invention improve the accuracy of information recommendation.
Drawings
Fig. 1 is a schematic flowchart of an information recommendation method according to an embodiment of the present invention;
fig. 2 is a schematic block diagram of an information recommendation apparatus according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an internal structure of an electronic device implementing an information recommendation method according to an embodiment of the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the invention provides an information recommendation method. The execution subject of the information recommendation method includes, but is not limited to, at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiments of the present application. In other words, the information recommendation method may be executed by software or hardware installed in the terminal device or the server device, and the software may be a block chain platform. The server includes but is not limited to: the cloud server can be an independent server, or can be a cloud server providing basic cloud computing services such as cloud service, a cloud database, cloud computing, a cloud function, cloud storage, Network service, cloud communication, middleware service, domain name service, security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Referring to fig. 1, a schematic flow chart of an information recommendation method according to an embodiment of the present invention is shown, where in the embodiment of the present invention, the information recommendation method includes:
s1, obtaining historical information use data of the user, wherein the historical information use data comprises: acquiring times and actual response times of different types of information;
in the embodiment of the present invention, the historical information usage data is information such as a type, an acquisition frequency, a response frequency, and time of information acquired by the user, where the information includes, but is not limited to, service right information such as a coupon, a discount coupon, and a coupon, where the information may be actively issued to the user by a company, an enterprise, and the like that can provide a service corresponding to the information, and the actual response frequency is a frequency of using a service right corresponding to a certain category of service right information by the user.
In detail, the information retrieval record may be retrieved from a pre-constructed access area for storing the information retrieval record, which may be, but is not limited to, a database, a block chain node, a network cache, using a computer sentence with data fetching function (e.g., java sentence, python sentence, etc.).
S2, acquiring an information set to be recommended, which contains different types of information and corresponding information labels, and constructing a matrix according to the information in the information set to be recommended to obtain an initial information matrix;
in the embodiment of the invention, because the information has timeliness, the information available for recommendation is constantly changed, and therefore, an information set to be recommended needs to be acquired.
Further, in the embodiment of the present invention, the information set to be recommended is a set of information that can be recommended. The information label is a short text for summarizing the information characteristics.
In detail, the constructing a blank matrix according to the information in the information set to be recommended in the embodiment of the present invention includes:
constructing blank matrixes with the same element number according to the category number of the information in the information set to be recommended;
optionally, in the embodiment of the present invention, the blank matrix is a matrix whose elements are all zero.
Alternatively, the embodiment of the present invention may create a blank matrix of m rows and n columns by using a B ═ zeros (m, n) function in the R language library.
Marking information of one type for each element in the blank matrix in sequence by using the information in the information set to be recommended; wherein, the information of any two element marks in the blank matrix is different;
such as: the information set to be recommended has 2 types of information, namely information A and information B, the blank matrix is a 1 x 2 matrix, elements in a first row in the blank matrix are marked as the information A, and elements in a second row in the blank matrix are marked as the information B.
Further, the embodiment of the present invention obtains an initial weight corresponding to each information in the information set to be recommended, and updates elements in the blank matrix, which mark the same type of information, with the initial weight to obtain an initial information matrix, where the initial weight is a preset value for measuring the importance of each information.
S3, calculating the information utilization rate of each piece of information according to the acquisition times and the actual response times, and updating matrix elements of the initial information matrix by using the information utilization rate to obtain an information utilization rate matrix;
in detail, in the embodiment of the present invention, the information usage rate of each piece of information in the history information usage data is calculated by using the following information usage rate function;
wherein x is the information utilization rate, Use, of the information j acquired by the userjThe number of responses to the information j acquired by the user, ReleasejThe number of times of obtaining the information j obtained for the user.
Further, in the embodiment of the present invention, each information usage rate and an element of the same type of information in the initial information matrix are calculated, and the element corresponding to the type of information in the initial information matrix is replaced with the calculation result, so as to obtain an information usage rate matrix, where the information corresponding to the position element is not affected in the replacement process.
Optionally, in the embodiment of the present invention, the information usage rate of each piece of information is multiplied by an element of the same type of information in the initial information matrix, for example: the information utilization rate corresponding to the information a is 0.2, and the element corresponding to the information a in the initial information matrix is 0.5, then 0.2 × 0.5 is 0.1, and 0.1 replaces 0.5 of the element corresponding to the information a in the initial information matrix.
S4, obtaining a user label of the user, performing similarity calculation according to the user label and the information label to obtain a matching weight of each information, and performing weighted calculation on elements of the information corresponding to the information utilization rate matrix according to the matching weight to obtain the information recommendation matrix.
Optionally, in the embodiment of the present invention, the user tag is a short text that summarizes user behavior characteristics.
Further, in order to determine the user's preference for different information, the similarity between the user tag and the information tag needs to be calculated, and the higher the similarity is, the higher the probability that the user likes the service is.
In detail, in the embodiment of the present invention, the obtaining the matching weight of each type of information by performing similarity calculation according to the user tag and the information tag includes:
carrying out vector conversion on the user tag to obtain a user tag vector;
optionally, in the embodiment of the present invention, the user tag is vectorized by using an artificial intelligence model, such as a bert model, a one-hot algorithm, and the like, to obtain the user tag vector.
Carrying out vector conversion on the information tag to obtain an information tag vector;
in detail, in the embodiment of the present invention, each character in the information tag is converted into a vector to obtain a corresponding character vector, and further, a maximum value of each character vector is selected to obtain a corresponding character characteristic value; and combining the character characteristic values corresponding to all the character vectors according to the sequence of the characters corresponding to the character vectors in the information tag to obtain the information tag vector.
In another embodiment of the present invention, an average value of all elements in each of the character vectors may be calculated to obtain the character feature value.
In another embodiment of the present invention, all the character vectors are subjected to first splicing according to the sequence of the characters corresponding to the character vectors in the information tag, so as to obtain the information tag vector.
And calculating the similarity of the user label vector and the information label vector to obtain the matching weight of the information corresponding to the information label.
Optionally, in the embodiment of the present invention, the correlation may be calculated by using the following formula:
wherein, XiThe i-th element, Y, representing the user tag vector XiFor the ith element of the information label vector Y, n represents Sim represents the similarity between the user label vector X and the information label vector Y.
Further, in the embodiment of the present invention, the matching weight is used to update elements in the information usage rate matrix, so as to obtain the information recommendation matrix.
In detail, in the embodiment of the present invention, the information recommendation matrix is obtained by performing weighted calculation on the matching weight of each piece of information and the element of the same type of information in the information usage rate matrix, and replacing the element corresponding to the type of information in the information usage rate matrix with the weighted calculation result.
S5, calculating according to a preset attenuation factor, preset recommendation time and acquisition time of the historical information use data to obtain an attenuation weight, and performing weighted calculation according to the attenuation weight and the information recommendation matrix to obtain a target recommendation matrix;
optionally, in the embodiment of the present invention, the recommendable degree of the service information corresponding to the element may be measured according to the size of the element in the information recommendation matrix, but since different information is time-sensitive, the recommendable degree of the service information is continuously attenuated with the time. Therefore, the embodiment of the invention calculates according to the preset attenuation factor, the preset recommendation time and the acquisition time of the historical information use data to obtain the attenuation weight.
In detail, the embodiment of the present invention may calculate the attenuation weight by using the following formula:
S＝e-γ(T-t)
wherein S is the attenuation weight, y is the attenuation factor, T is the recommended time, and T is the acquisition time of the historical information use data.
Further, in the embodiment of the present invention, the attenuation weight and the information recommendation matrix are subjected to weighted calculation to obtain the target recommendation matrix. Optionally, in the embodiment of the present invention, the attenuation weight and the information recommendation matrix are multiplied to obtain the target recommendation matrix.
In another embodiment of the invention, the target recommendation matrix can be stored in block link points, and the data access efficiency is improved by using the characteristic of high throughput of the block link points.
S6, screening information corresponding to each element in the target recommendation matrix according to a preset recommendation threshold value, and recommending the screened information to the user at a preset recommendation time.
Optionally, in the embodiment of the present invention, an element greater than the recommendation threshold in the target recommendation matrix may be selected to obtain a target element; recommending the information corresponding to the target element to the user at preset recommending time.
In another embodiment of the present invention, since the recommendation threshold is set in advance, which may cause recommendation solidification, and thus information that does not meet the condition is recommended, before the information corresponding to each element in the target recommendation matrix is screened according to the preset recommendation threshold, the method further includes:
clustering all elements in the target recommendation matrix by using a preset clustering algorithm to obtain one or more category clusters;
calculating all elements in all the category clusters to obtain corresponding cluster characteristic values;
in detail, in the embodiment of the present invention, all elements in each of the category clusters are averaged to obtain a corresponding cluster feature value, for example: the category cluster a has 3 elements in total, 5.1, 5.2, and 5.3, and the corresponding cluster feature value is (5.1+5.2+5.3)/3 ═ 5.1.
And selecting the maximum value of all the cluster characteristic values as the recommendation threshold value.
Fig. 2 is a functional block diagram of the information recommendation apparatus according to the present invention.
The information recommendation device 100 of the present invention may be installed in an electronic device. According to the realized functions, the information recommendation device may include an information matrix construction module 101, an information matrix update module 102, and an information recommendation module 103, which may also be referred to as a unit, and refer to a series of computer program segments that can be executed by a processor of an electronic device and can perform fixed functions, and are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the information matrix building module 101 is configured to obtain historical information usage data of a user, where the historical information usage data includes: acquiring times and actual response times of different types of information; acquiring an information set to be recommended, which comprises different types of information and corresponding information labels, and constructing a matrix according to the information in the information set to be recommended to obtain an initial information matrix;
the information matrix updating module 102 is configured to calculate an information utilization rate of each piece of information according to the acquisition times and the actual response times, and perform matrix element updating on the initial information matrix by using the information utilization rate to obtain an information utilization rate matrix; acquiring a user tag of the user, performing similarity calculation according to the user tag and the information tag to obtain a matching weight of each information, and performing weighted calculation on elements of the information corresponding to the information utilization rate matrix according to the matching weight to obtain the information recommendation matrix; calculating according to a preset attenuation factor, preset recommendation time and acquisition time of the historical information use data to obtain an attenuation weight, and performing weighted calculation according to the attenuation weight and the information recommendation matrix to obtain a target recommendation matrix;
the information recommendation module 103 is configured to screen information corresponding to each element in the target recommendation matrix according to a preset recommendation threshold, and recommend the screened information to the user at a preset recommendation time.
In detail, when the modules in the information recommendation apparatus 100 according to the embodiment of the present invention are used, the same technical means as the information recommendation method described in fig. 1 are used, and the same technical effects can be produced, which is not described herein again.
Fig. 3 is a schematic structural diagram of an electronic device implementing the information recommendation method according to the present invention.
The electronic device 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 an information recommendation program, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, which includes flash memory, removable hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, 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, which are 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 an information recommendation program, etc., but also to temporarily store data that has been output or is to be output.
The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (e.g., information recommendation programs, etc.) stored in the memory 11 and calling data stored in the memory 11.
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 communication bus 12 is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
Fig. 3 shows only an electronic device having components, and those skilled in the art will appreciate that the structure shown in fig. 3 does not constitute a limitation of the electronic device, and may include fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device 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 source may also include any component of one or more dc or ac power sources, recharging devices, power failure classification circuits, 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.
Optionally, the communication interface 13 may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices.
Optionally, the communication interface 13 may further include a user interface, which may be a Display (Display), an input unit (such as a Keyboard (Keyboard)), and optionally, a standard wired interface, or 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.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The information recommendation program stored in the memory 11 of the electronic device is a combination of a plurality of computer programs, which when executed in the processor 10, can realize:
acquiring historical information use data of a user, wherein the historical information use data comprises: acquiring times and actual response times of different types of information;
acquiring an information set to be recommended, which comprises different types of information and corresponding information labels, and constructing a matrix according to the information in the information set to be recommended to obtain an initial information matrix;
calculating the information utilization rate of each piece of information according to the acquisition times and the actual response times, and updating matrix elements of the initial information matrix by using the information utilization rate to obtain an information utilization rate matrix;
acquiring a user tag of the user, performing similarity calculation according to the user tag and the information tag to obtain a matching weight of each information, and performing weighted calculation on elements of the information corresponding to the information utilization rate matrix according to the matching weight to obtain the information recommendation matrix;
calculating according to a preset attenuation factor, preset recommendation time and acquisition time of the historical information use data to obtain an attenuation weight, and performing weighted calculation according to the attenuation weight and the information recommendation matrix to obtain a target recommendation matrix;
and screening the information corresponding to each element in the target recommendation matrix according to a preset recommendation threshold value, and recommending the screened information to the user at a preset recommendation time.
Specifically, the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the computer program, which is not described herein again.
Further, the electronic device integrated module/unit, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in a computer readable storage medium. The computer readable medium may be non-volatile or volatile. 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).
Embodiments of the present invention may also provide a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program may implement:
acquiring historical information use data of a user, wherein the historical information use data comprises: acquiring times and actual response times of different types of information;
acquiring an information set to be recommended, which comprises different types of information and corresponding information labels, and constructing a matrix according to the information in the information set to be recommended to obtain an initial information matrix;
calculating the information utilization rate of each piece of information according to the acquisition times and the actual response times, and updating matrix elements of the initial information matrix by using the information utilization rate to obtain an information utilization rate matrix;
acquiring a user tag of the user, performing similarity calculation according to the user tag and the information tag to obtain a matching weight of each information, and performing weighted calculation on elements of the information corresponding to the information utilization rate matrix according to the matching weight to obtain the information recommendation matrix;
calculating according to a preset attenuation factor, preset recommendation time and acquisition time of the historical information use data to obtain an attenuation weight, and performing weighted calculation according to the attenuation weight and the information recommendation matrix to obtain a target recommendation matrix;
and screening the information corresponding to each element in the target recommendation matrix according to a preset recommendation threshold value, and recommending the screened information to the user at a preset recommendation time.
Further, the computer usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
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.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. An information recommendation method, characterized in that the method comprises:
acquiring historical information use data of a user, wherein the historical information use data comprises: acquiring times and actual response times of different types of information;
acquiring an information set to be recommended, which comprises different types of information and corresponding information labels, and constructing a matrix according to the information in the information set to be recommended to obtain an initial information matrix;
calculating the information utilization rate of each piece of information according to the acquisition times and the actual response times, and updating matrix elements of the initial information matrix by using the information utilization rate to obtain an information utilization rate matrix;
acquiring a user tag of the user, performing similarity calculation according to the user tag and the information tag to obtain a matching weight of each information, and performing weighted calculation on elements of the information corresponding to the information utilization rate matrix according to the matching weight to obtain the information recommendation matrix;
calculating according to a preset attenuation factor, preset recommendation time and acquisition time of the historical information use data to obtain an attenuation weight, and performing weighted calculation according to the attenuation weight and the information recommendation matrix to obtain a target recommendation matrix;
and screening the information corresponding to each element in the target recommendation matrix according to a preset recommendation threshold value, and recommending the screened information to the user at a preset recommendation time.
2. The information recommendation method according to claim 1, wherein the constructing a matrix according to the information in the information set to be recommended to obtain an initial information matrix comprises:
constructing blank matrixes with the same element number according to the category number of the information in the information set to be recommended;
marking information of one type for each element in the blank matrix in sequence by using the information in the information set to be recommended; wherein, the information of any two element marks in the blank matrix is different;
and acquiring an initial weight corresponding to each piece of information in the information set to be recommended, and updating elements marked with the same type of information in the blank matrix by using the initial weight to obtain an initial information matrix.
3. The information recommendation method according to claim 1, wherein the performing similarity calculation according to the user tag and the information tag to obtain a matching weight of each kind of information, and performing weighting calculation according to the matching weight on an element of information corresponding to the information usage rate matrix to obtain the information recommendation matrix comprises:
carrying out vector conversion on the user tag to obtain a user tag vector;
carrying out vector conversion on the information tag to obtain an information tag vector;
calculating the similarity of the user tag vector and the information tag vector to obtain the matching weight of the information corresponding to the information tag;
and updating elements in the information utilization rate matrix by using the matching weight to obtain the information recommendation matrix.
4. The information recommendation method of claim 3, wherein the vector converting the information tag to obtain an information tag vector comprises:
converting each character in the information label into a vector to obtain a corresponding character vector;
selecting the maximum value of each character vector to obtain a corresponding character characteristic value;
and combining the character characteristic values corresponding to all the character vectors according to the sequence of the characters corresponding to the character vectors in the information tag to obtain the information tag vector.
5. The information recommendation method of claim 3, wherein said updating elements in said information usage matrix with said matching weights to obtain said information recommendation matrix comprises:
carrying out weighted calculation on the matching weight of each piece of information and elements of the same type of information in the information utilization rate matrix;
and replacing the element corresponding to the category information in the information utilization rate matrix with the weighted calculation result to obtain the information recommendation matrix.
6. The information recommendation method of claim 1, wherein the calculating according to a preset decay factor, a preset recommendation time, and an acquisition time of the historical information usage data to obtain a decay weight comprises:
calculating by using the following formula to obtain the attenuation weight:
S＝e-γ(T-t)
wherein S is the attenuation weight, gamma is the attenuation factor, T is the recommended time, and T is the acquisition time of the historical information use data.
7. The information recommendation method according to any one of claims 1 to 6, wherein before the filtering the information corresponding to each element in the target recommendation matrix according to a preset recommendation threshold, the method further comprises:
clustering all elements in the target recommendation matrix by using a preset clustering algorithm to obtain one or more category clusters;
calculating all elements in all the category clusters to obtain corresponding cluster characteristic values;
and selecting the maximum value of all the cluster characteristic values as the recommendation threshold value.
8. An information recommendation apparatus, comprising:
the information matrix construction module is used for acquiring historical information use data of a user, wherein the historical information use data comprises: acquiring times and actual response times of different types of information; acquiring an information set to be recommended, which comprises different types of information and corresponding information labels, and constructing a matrix according to the information in the information set to be recommended to obtain an initial information matrix;
the information matrix updating module is used for calculating the information utilization rate of each piece of information according to the acquisition times and the actual response times, and updating matrix elements of the initial information matrix by using the information utilization rate to obtain an information utilization rate matrix; acquiring a user tag of the user, performing similarity calculation according to the user tag and the information tag to obtain a matching weight of each information, and performing weighted calculation on elements of the information corresponding to the information utilization rate matrix according to the matching weight to obtain the information recommendation matrix; calculating according to a preset attenuation factor, preset recommendation time and acquisition time of the historical information use data to obtain an attenuation weight, and performing weighted calculation according to the attenuation weight and the information recommendation matrix to obtain a target recommendation matrix;
and the information recommendation module is used for screening the information corresponding to each element in the target recommendation matrix according to a preset recommendation threshold value and recommending the screened information to the user at a preset recommendation time.
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, the computer program being executable by the at least one processor to enable the at least one processor to perform the information recommendation method of any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the information recommendation method according to any one of claims 1 to 7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115809373A (en) * | 2023-02-06 | 2023-03-17 | 一智科技有限公司 | Intelligent recommendation method, system and storage medium |
| CN116301734A (en) * | 2023-05-17 | 2023-06-23 | 安徽思高智能科技有限公司 | Method and device for recommending flows in RPA flow asset library and electronic equipment |
| CN116307284A (en) * | 2023-05-19 | 2023-06-23 | 工业富联(佛山)创新中心有限公司 | Energy consumption prediction method, electronic device and storage medium |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115809373A (en) * | 2023-02-06 | 2023-03-17 | 一智科技有限公司 | Intelligent recommendation method, system and storage medium |
| CN116301734A (en) * | 2023-05-17 | 2023-06-23 | 安徽思高智能科技有限公司 | Method and device for recommending flows in RPA flow asset library and electronic equipment |
| CN116301734B (en) * | 2023-05-17 | 2023-07-28 | 安徽思高智能科技有限公司 | Method and device for recommending flows in RPA flow asset library and electronic equipment |
| CN116307284A (en) * | 2023-05-19 | 2023-06-23 | 工业富联(佛山)创新中心有限公司 | Energy consumption prediction method, electronic device and storage medium |
| CN116307284B (en) * | 2023-05-19 | 2023-08-08 | 工业富联(佛山)创新中心有限公司 | Energy consumption prediction method, electronic device and storage medium |
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