CN113868528A - Information recommendation method and device, electronic equipment and readable storage medium - Google Patents
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Abstract
The invention relates to an artificial intelligence technology, and discloses an information recommendation method, which comprises the following steps: acquiring historical query information of a user in a preset time interval, and screening all historical information labels according to the occurrence frequency of each historical information label in the historical query information of the user to obtain a screening label set; calculating the similarity between the user characteristic information and the information label to obtain a first recommendation score; calculating the similarity between each historical information tag in the screening tag set and the information tag to obtain a second recommendation score; calculating according to the first recommendation scores and all the second recommendation scores to obtain target recommendation scores; and screening the information to be pushed in the information set according to the target recommendation score and sending the information to the user. The invention also relates to a block chaining technique, wherein the historical information tags can be stored in block chaining 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, an information recommendation apparatus, an electronic device, and a readable storage medium.
Background
With the development of information technology, people receive more and more information every day, and in order to better meet the personalized information reading requirements of people, the information needs to be pushed to people.
However, the current information push method only depends on the inherent characteristics of the user to push, and the push process cannot be adjusted, so that the information received by the user is always the same, the information required by the user cannot be dynamically adjusted, and 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 computer readable storage medium, and mainly aims to improve the accuracy of information recommendation.
In order to achieve the above object, the present invention provides an information recommendation method, comprising:
acquiring historical query information of a user in a preset time interval, wherein the historical query information comprises a historical information tag corresponding to historical query information;
screening all the historical information labels according to the occurrence frequency of each historical information label in the user historical consulting information to obtain a historical information label subset;
acquiring an information set to be pushed, wherein each piece of information to be pushed in the information set to be pushed comprises a corresponding information tag;
acquiring user characteristic information of the user, and calculating the similarity between the user characteristic information and each information label to obtain a corresponding first recommendation score;
calculating the similarity between each historical information tag in the historical information tag subset and the information tag to obtain a corresponding second recommendation score;
calculating according to the first recommendation score and all the second recommendation scores corresponding to each piece of information to be pushed to obtain a target recommendation score;
and screening the information set to be pushed according to the target recommendation score, and sending the screened information set to be pushed to the user.
Optionally, the screening all the historical information tags according to the frequency of occurrence of each type of historical information tag in the user historical consulting information to obtain a historical information tag subset includes:
counting the occurrence frequency of each historical information label in the historical consulting information of the user;
calculating according to the occurrence frequency of each historical label and the number of all the historical labels to obtain a corresponding label reference ratio;
sorting and combining all types of the historical information labels according to the size of the label look-up ratio to obtain a historical information label sequence;
and performing sequence length screening on the historical information tag sequence to obtain the historical information tag subset.
Optionally, the performing sequence length screening on the historical information tag sequence to obtain the historical information tag subset includes:
judging whether the sequence length of the historical information label sequence is larger than a preset length or not;
when the sequence length of the historical information label sequence is not greater than a preset length, summarizing all historical information labels in the historical information label sequence to obtain the historical information label subset;
when the sequence length of the historical information label sequence is larger than the preset length, determining the position of a target sequence according to the preset length, and selecting all historical information labels of the sequence position in the historical information label sequence at the position of the target sequence and before to obtain the historical information label subset.
Optionally, the calculating the similarity between the user characteristic information and each information tag to obtain a corresponding first recommendation score includes:
acquiring all user characteristic attributes in the user characteristic information;
converting the user characteristic attribute into a vector to obtain a user characteristic attribute vector;
converting each information label into a vector to obtain an information label vector;
calculating the similarity between the user characteristic information vector and the information label vector to obtain a first similarity;
and calculating by using a preset characteristic weight and the first similarity to obtain the first recommendation score.
Optionally, the calculating a similarity between each historical information tag in the historical information tag subset and the information tag to obtain a corresponding second recommendation score includes:
converting each historical information tag in the historical information tag subset into a vector to obtain a historical information tag vector;
calculating the similarity between the historical label vector and the information label vector to obtain a corresponding second similarity;
calculating the weight of each historical information label in the historical information label subset according to the label look-up ratio and the characteristic weight to obtain the weight of the historical information label;
and performing weighted calculation according to the second similarity corresponding to each historical information label and the weight of the historical information label to obtain the corresponding second recommendation score.
Optionally, the screening the information set to be pushed according to the target recommendation score, and sending the screened information set to be pushed to the user includes:
selecting information to be pushed, of which the target recommendation score is larger than a preset recommendation score threshold value, in the information set to be pushed to obtain a target information set to be pushed;
sequencing all information to be pushed in the information set to be pushed of the target according to the target recommendation score to obtain an information sequence to be pushed;
selecting the first information to be pushed in the information sequence to be pushed as a starting point, wherein the length of the sequence is a preset pushing length, and obtaining a target sequence to be pushed;
and pushing the target sequence to be pushed to the user.
Optionally, before obtaining the historical query information of the user within the preset time interval, the method further includes:
taking the current time as the right endpoint of the interval;
and constructing an interval by using a preset time interval as an interval length according to the right end point to obtain the time interval.
In order to solve the above problems, the present invention further provides an information recommendation apparatus, comprising:
the similarity calculation module is used for receiving an information pushing request of a user and corresponding request time; constructing a time interval according to the request time, and acquiring historical query information of the user in a preset time interval, wherein the historical query information comprises historical lookup information and corresponding historical information tags; screening all the historical information labels according to the occurrence frequency of each historical information label in the user historical consulting information to obtain a historical information label subset; acquiring an information set to be pushed, wherein each piece of information to be pushed in the information set to be pushed comprises a corresponding information tag; acquiring user characteristic information of the user, and calculating the similarity between the user characteristic information and each information label to obtain a corresponding first recommendation score; calculating the similarity between each historical information tag in the historical information tag subset and the information tag to obtain a corresponding second recommendation score;
the recommendation score calculation module is used for calculating according to the first recommendation score and all the second recommendation scores corresponding to each piece of information to be pushed to obtain a target recommendation score;
and the information recommendation module is used for screening the information set to be pushed according to the target recommendation score and sending the screened information set to be pushed to the user.
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 the processor executes the computer program stored in the memory to realize the information recommendation method.
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 information recommendation method described above.
Calculating the similarity between each historical information label in the historical information label subsequence and the information label to obtain a corresponding second recommendation score; calculating according to the first recommendation score and all the second recommendation scores corresponding to each piece of information to be pushed to obtain a target recommendation score; the history information label of the recent history access information of the user is obtained, the history information label is comprehensively matched with the inherent characteristics of the user and the information label, the history information label is correspondingly adjusted along with the change of accessing the history access information of the user at different time, the current reading characteristics of the user can be more accurately reflected, the second recommendation score corresponding to the history information label and the first recommendation score corresponding to the inherent characteristics of the user are further utilized to comprehensively match and screen the information to be pushed, the screening is more accurate, and the information recommendation accuracy is higher, so that the information recommendation method, the device, the electronic equipment and the readable storage medium provided by the embodiment of the invention improve the information recommendation accuracy.
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FIG. 1 is a flowchart illustrating an information recommendation method according to an embodiment of the present invention;
FIG. 2 is a block diagram of an information recommendation apparatus according to an embodiment of the present invention;
fig. 3 is a schematic internal structure diagram 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 flow chart of an information recommendation method according to an embodiment of the present invention is shown, in the embodiment of the present invention, the information recommendation method includes:
s1, obtaining historical query information of a user in a preset time interval, wherein the historical query information comprises a historical information label corresponding to historical query information;
in detail, in order to obtain the historical query information of the user, the embodiment of the present invention may be more time-efficient, and therefore, the historical query information of the user in the preset time interval is obtained, optionally, the embodiment of the present invention takes the current time as a right end point of the interval, and uses a preset time interval as an interval length construction interval according to the right end point to obtain the time interval, for example: the time interval is 30 days, the right end point is 7/31, the time interval is [7/1,7/31], further, a tag corresponding to information consulted by the user in the time interval is obtained, the information consulted by the history is user consulted information, and the history information tag is an information tag corresponding to the information consulted by the user.
Further, in the embodiment of the present invention, the historical query information includes one or more historical information tags corresponding to the historical query information.
In another embodiment of the present invention, the history information tag may be stored in a blockchain node, and the data access efficiency is improved by using the characteristic of high throughput of the blockchain node.
S2, screening all the historical information labels according to the occurrence frequency of each historical information label in the user historical consulting information to obtain a historical information label subset;
in the embodiment of the invention, each piece of information consulted by the history has the corresponding history information label, and the history information labels of different pieces of information consulted by the history can be repeated, so that in order to count the type of information that a user likes to consult, the embodiment of the invention can obtain the label consulting ratio according to the occurrence frequency of each type of history information label in the user history consulting information.
In detail, in the embodiment of the present invention, obtaining the tag lookup ratio according to the number of occurrences of each type of history information tag in the user history lookup information includes:
counting the occurrence frequency of each historical information label in the historical consulting information of the user;
and calculating according to the occurrence frequency of each type of history label and the number of all history labels to obtain the corresponding label reference ratio.
For example: the number of all the historical information labels is a, the occurrence frequency of the historical information label A in all the historical information labels is b, and then the label reference ratio corresponding to the historical information label A is b/a.
In detail, in the embodiment of the present invention, the historical information tags of all categories are sorted and combined according to the size of the tag lookup ratio, so as to obtain a historical information tag sequence.
For example: all the historical information labels share a historical information label A and a historical information label B, and share two types of historical information labels, wherein the label look-up ratio corresponding to the historical information label A is 0.8, the label look-up ratio corresponding to the historical information label B is 0.2, and then the historical information label sequence is [ the historical information label A and the historical information label B ].
Further, in the embodiment of the present invention, the sequence length of the historical information tag sequence is screened to obtain the historical information tag subset.
In detail, in the embodiment of the present invention, the sequence length screening of the historical information tag sequence to obtain the historical information tag subset includes:
judging whether the sequence length of the historical information label sequence is larger than a preset length or not;
when the sequence length of the historical information label sequence is not greater than a preset length, summarizing all historical information labels in the historical information label sequence to obtain the historical information label subset;
when the sequence length of the historical information label sequence is larger than the preset length, determining the position of a target sequence according to the preset length, and selecting all historical information labels of the sequence position in the historical information label sequence at the position of the target sequence and before to obtain the historical information label subset.
For example: if the preset length is 2, the target sequence position is 2, the historical information tag sequence is [ historical information tag C, historical information tag B, historical information tag A ], and the historical information tag C and the historical information tag B with the sequence positions second and before in the historical information sequence tag are selected to obtain the historical information tag subset.
S3, acquiring an information set to be pushed, wherein each information to be pushed in the information set to be pushed comprises a corresponding information label;
in detail, in the embodiment of the present invention, the information set to be pushed may be a set of information to be pushed to a user, and each piece of information to be pushed in the information set to be pushed has a corresponding information tag.
S4, obtaining user characteristic information of the user, and calculating the similarity between the user characteristic information and each information label to obtain a corresponding first recommendation score;
in the embodiment of the present invention, the user feature information includes different user feature attributes, such as: the job attribute is a product manager; the information preference attribute is a political headline which likes watching.
In detail, in the embodiment of the present invention, calculating a similarity between the user feature information and each information tag to obtain a first recommendation score includes:
acquiring all user characteristic attributes in the user characteristic information;
converting the user characteristic attribute into a vector to obtain a user characteristic attribute vector;
optionally, in the embodiment of the present invention, converting the user characteristic attribute into a vector to obtain the user characteristic attribute vector includes:
converting each character in the user characteristic attribute into a vector to obtain a word vector;
optionally, in the embodiment of the present invention, each character in the user feature attribute may be converted into a vector by using an artificial intelligence model, such as a word2vec model and a bert model.
Selecting the average value of all elements in the word vector to obtain a word vector characteristic value;
optionally, in this embodiment of the present invention, a maximum value, a mode, and a median of all elements in the word vector may also be selected as a word vector feature value of the word vector.
And longitudinally combining all the character vector characteristic values according to the sequence of the characters of the corresponding character vectors in the user characteristic attribute to obtain the user characteristic attribute vector.
For example: the user characteristic attribute is teacher, and the character vector corresponding to the old character is
Thus, word vectors
The corresponding word vector eigenvalue is 3; the character teacher corresponds to a word vector of
Thus, word vectors
Corresponding word vector bitThe eigenvalue is 4; then, longitudinally combining all the character vector characteristic values according to the sequence of the characters of the corresponding character vector in the user characteristic attribute to obtain the user characteristic attribute vector of
Further, in the embodiment of the present invention, arithmetic mean calculation is performed on all the user characteristic attribute vectors to obtain the user characteristic information vector.
For example: there are two user feature attribute vectors, each being
Then the user feature information vector is
In another embodiment of the present invention, the user characteristic attribute vectors are sequentially connected to obtain the user characteristic information vector.
Converting each information label into a vector to obtain an information label vector;
optionally, in the embodiment of the present invention, a word2vec model may be used for vector transformation.
Calculating the similarity between the user characteristic information vector and the information label vector to obtain a first similarity;
and calculating by using a preset characteristic weight and the first similarity to obtain the first recommendation score.
Optionally, in the embodiment of the present invention, the feature weight is multiplied by the first similarity to obtain the first recommendation score, where the feature weight is an influence factor of the first similarity in information recommendation, when the history information tag subset is not an empty set, the feature weight of the history information tag subset is any real number in an interval (0,1), and when the history information tag subset is an empty set, the feature weight is 1.
S5, calculating the similarity between each historical information label in the historical information label subset and the information label to obtain a corresponding second recommendation score;
in detail, in the embodiment of the present invention, each historical information tag in the historical information tag subset is converted into a vector, so as to obtain the historical information tag vector;
further, in the embodiment of the present invention, the similarity between the history tag vector and the information tag vector is calculated to obtain a corresponding second similarity;
calculating the weight of each historical information label in the historical information label subset according to the label look-up ratio and the characteristic weight to obtain the weight of the historical information label;
and performing weighted calculation according to the second similarity and the historical information label weight to obtain the second recommendation score.
Optionally, in the embodiment of the present invention, the second similarity corresponding to each historical information tag and the weight of the historical information tag are multiplied to obtain the corresponding second recommendation score.
Optionally, in the embodiment of the present invention, the weight of the historical information tag is calculated by using the following formula:
wherein i is the serial number of the historical information tag in the historical information tag subset, fiAnd the label look-up ratio of the history information labels with the sequence number i in the history information label subset is obtained, Q is a preset characteristic weight, and n is the total number of the history information labels in the history information label subset.
S6, calculating according to the first recommendation score and all the second recommendation scores corresponding to each piece of information to be pushed to obtain a target recommendation score;
in detail, in the embodiment of the present invention, the first recommendation score corresponding to each piece of information to be pushed and all the second recommendation scores are summed to obtain the target recommendation score.
S7, screening the information set to be pushed according to the target recommendation score, and sending the screened information set to be pushed to the user.
In detail, in the embodiment of the present invention, the number of information that can be browsed by a user is limited, and therefore, in the embodiment of the present invention, information to be pushed, in which the target recommendation score in the information set to be pushed is greater than a preset recommendation score threshold, is selected to obtain a target information set to be pushed. In order to prevent unnecessary waste of push resources due to a limited number of information that can be browsed by a user, a target sequence to be pushed is obtained by selecting a first information to be pushed in the information sequence to be pushed as a starting point and a sequence with a preset push length, and the target sequence to be pushed is pushed to the user, optionally, the target sequence to be pushed can be pushed to a terminal device of the user, where the terminal device includes: intelligent terminals such as mobile phones, computers and tablets.
Furthermore, when the user refers to the target recommendation information, the user history reference information of the user is updated, the target recommendation information is used as history reference information, and a label corresponding to the target recommendation information is used as a corresponding history information label, so that information recommendation is more accurate for the user next time.
According to the embodiment of the invention, the label of the information to be referred is dynamically adjusted according to the historical information to be referred of the user, so that the accuracy of information recommendation is improved.
FIG. 2 is a functional block diagram of an information recommendation device according to the present invention.
The information recommendation device 100 of the present invention can be installed in an electronic device. According to the implemented functions, the information recommendation apparatus may include a similarity calculation 101, a recommendation score calculation 102, and an information recommendation module 103, which may also be referred to as a unit, and refers to a series of computer program segments that can be executed by a processor of an electronic device and can perform a fixed function, 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 similarity calculation module 101 is configured to obtain historical query information of a user within a preset time interval, where the historical query information includes a historical information tag corresponding to historical query information; screening all the historical information labels according to the occurrence frequency of each historical information label in the user historical consulting information to obtain a historical information label subset; acquiring an information set to be pushed, wherein each piece of information to be pushed in the information set to be pushed comprises a corresponding information tag; acquiring user characteristic information of the user, and calculating the similarity between the user characteristic information and each information label to obtain a corresponding first recommendation score; calculating the similarity between each historical information tag in the historical information tag subset and the information tag to obtain a corresponding second recommendation score;
the recommendation score calculating module 102 is configured to calculate a target recommendation score according to a first recommendation score and all second recommendation scores corresponding to each piece of information to be pushed;
the information recommendation module 103 is configured to screen the information set to be pushed according to the target recommendation score, and send the screened information set to be pushed to the user.
In detail, in the embodiment of the present invention, each module in the information recommendation apparatus 100 adopts the same technical means as the information recommendation method described in fig. 1, and can produce the same technical effect, which is not described herein again.
Fig. 2 is a schematic structural diagram of an electronic device implementing the information recommendation method according to the present invention.
The electronic device 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 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 to store not only application software installed in the electronic device and various data, such as codes of an information recommendation program, but also temporarily store data that has been output or will 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 operating 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. 2 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 2 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, and when running in the processor 10, can realize:
acquiring historical query information of a user in a preset time interval, wherein the historical query information comprises a historical information tag corresponding to historical query information;
screening all the historical information labels according to the occurrence frequency of each historical information label in the user historical consulting information to obtain a historical information label subset;
acquiring an information set to be pushed, wherein each piece of information to be pushed in the information set to be pushed comprises a corresponding information tag;
acquiring user characteristic information of the user, and calculating the similarity between the user characteristic information and each information label to obtain a corresponding first recommendation score;
calculating the similarity between each historical information tag in the historical information tag subset and the information tag to obtain a corresponding second recommendation score;
calculating according to the first recommendation score and all the second recommendation scores corresponding to each piece of information to be pushed to obtain a target recommendation score;
and screening the information set to be pushed according to the target recommendation score, and sending the screened information set to be pushed to the user.
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 query information of a user in a preset time interval, wherein the historical query information comprises a historical information tag corresponding to historical query information;
screening all the historical information labels according to the occurrence frequency of each historical information label in the user historical consulting information to obtain a historical information label subset;
acquiring an information set to be pushed, wherein each piece of information to be pushed in the information set to be pushed comprises a corresponding information tag;
acquiring user characteristic information of the user, and calculating the similarity between the user characteristic information and each information label to obtain a corresponding first recommendation score;
calculating the similarity between each historical information tag in the historical information tag subset and the information tag to obtain a corresponding second recommendation score;
calculating according to the first recommendation score and all the second recommendation scores corresponding to each piece of information to be pushed to obtain a target recommendation score;
and screening the information set to be pushed according to the target recommendation score, and sending the screened information set to be pushed to the user.
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, the method comprising:
acquiring historical query information of a user in a preset time interval, wherein the historical query information comprises a historical information tag corresponding to historical query information;
screening all the historical information labels according to the occurrence frequency of each historical information label in the user historical consulting information to obtain a historical information label subset;
acquiring an information set to be pushed, wherein each piece of information to be pushed in the information set to be pushed comprises a corresponding information tag;
acquiring user characteristic information of the user, and calculating the similarity between the user characteristic information and each information label to obtain a corresponding first recommendation score;
calculating the similarity between each historical information tag in the historical information tag subset and the information tag to obtain a corresponding second recommendation score;
calculating according to the first recommendation score and all the second recommendation scores corresponding to each piece of information to be pushed to obtain a target recommendation score;
and screening the information set to be pushed according to the target recommendation score, and sending the screened information set to be pushed to the user.
2. The information recommendation method of claim 1, wherein the filtering all the history information tags according to the occurrence frequency of each history information tag in the user history lookup information to obtain a history information tag subset comprises:
counting the occurrence frequency of each historical information label in the historical consulting information of the user;
calculating according to the occurrence frequency of each historical label and the number of all the historical labels to obtain a corresponding label reference ratio;
sorting and combining all types of the historical information labels according to the size of the label look-up ratio to obtain a historical information label sequence;
and performing sequence length screening on the historical information tag sequence to obtain the historical information tag subset.
3. The information recommendation method of claim 2, wherein said performing sequence length filtering on said historical information tag sequence to obtain said historical information tag subset comprises:
judging whether the sequence length of the historical information label sequence is larger than a preset length or not;
when the sequence length of the historical information label sequence is not greater than a preset length, summarizing all historical information labels in the historical information label sequence to obtain the historical information label subset;
when the sequence length of the historical information label sequence is larger than the preset length, determining the position of a target sequence according to the preset length, and selecting all historical information labels of the sequence position in the historical information label sequence at the position of the target sequence and before to obtain the historical information label subset.
4. The information recommendation method of claim 1, wherein said calculating a similarity between said user characteristic information and each of said information tags to obtain a corresponding first recommendation score comprises:
acquiring all user characteristic attributes in the user characteristic information;
converting the user characteristic attribute into a vector to obtain a user characteristic attribute vector;
converting each information label into a vector to obtain an information label vector;
calculating the similarity between the user characteristic information vector and the information label vector to obtain a first similarity;
and calculating by using a preset characteristic weight and the first similarity to obtain the first recommendation score.
5. The information recommendation method of claim 4, wherein said calculating the similarity between each historical information tag in said subset of historical information tags and said information tag to obtain a corresponding second recommendation score comprises:
converting each historical information tag in the historical information tag subset into a vector to obtain a historical information tag vector;
calculating the similarity between the historical label vector and the information label vector to obtain a corresponding second similarity;
calculating the weight of each historical information label in the historical information label subset according to the label look-up ratio and the characteristic weight to obtain the weight of the historical information label;
and performing weighted calculation according to the second similarity corresponding to each historical information label and the weight of the historical information label to obtain the corresponding second recommendation score.
6. The information recommendation method according to any one of claims 1 to 5, wherein the filtering the information set to be pushed according to the target recommendation score and sending the filtered information set to be pushed to the user comprises:
selecting information to be pushed, of which the target recommendation score is larger than a preset recommendation score threshold value, in the information set to be pushed to obtain a target information set to be pushed;
sequencing all information to be pushed in the information set to be pushed of the target according to the target recommendation score to obtain an information sequence to be pushed;
selecting the first information to be pushed in the information sequence to be pushed as a starting point, wherein the length of the sequence is a preset pushing length, and obtaining a target sequence to be pushed;
and pushing the target sequence to be pushed to the user.
7. The information recommendation method of claim 1, wherein before obtaining the historical query information of the user within the predetermined time interval, the method further comprises:
taking the current time as the right endpoint of the interval;
and constructing an interval by using a preset time interval as an interval length according to the right end point to obtain the time interval.
8. An information recommendation apparatus, comprising:
the similarity calculation module is used for acquiring historical query information of a user in a preset time interval, wherein the historical query information comprises historical query information and corresponding historical information labels; screening all the historical information labels according to the occurrence frequency of each historical information label in the user historical consulting information to obtain a historical information label subset; acquiring an information set to be pushed, wherein each piece of information to be pushed in the information set to be pushed comprises a corresponding information tag; acquiring user characteristic information of the user, and calculating the similarity between the user characteristic information and each information label to obtain a corresponding first recommendation score; calculating the similarity between each historical information tag in the historical information tag subset and the information tag to obtain a corresponding second recommendation score;
the recommendation score calculation module is used for calculating according to the first recommendation score and all the second recommendation scores corresponding to each piece of information to be pushed to obtain a target recommendation score;
and the information recommendation module is used for screening the information set to be pushed according to the target recommendation score and sending the screened information set to be pushed to the user.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor;
wherein the memory stores a computer program executable by the at least one processor, 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 1-7.
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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| CN202111148142.9A CN113868528A (en) | 2021-09-29 | 2021-09-29 | Information recommendation method and device, electronic equipment and readable storage medium |
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| CN202111148142.9A CN113868528A (en) | 2021-09-29 | 2021-09-29 | Information recommendation method and device, electronic equipment and readable storage medium |
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