CN114781832A - Course recommendation method and device, electronic equipment and storage medium - Google Patents
Course recommendation method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN114781832A CN114781832A CN202210356827.0A CN202210356827A CN114781832A CN 114781832 A CN114781832 A CN 114781832A CN 202210356827 A CN202210356827 A CN 202210356827A CN 114781832 A CN114781832 A CN 114781832A
- Authority
- CN
- China
- Prior art keywords
- course
- capability
- recommendation
- index
- vector
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06398—Performance of employee with respect to a job function
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/335—Filtering based on additional data, e.g. user or group profiles
-
- 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
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
- G06Q50/2057—Career enhancement or continuing education service
Abstract
The invention relates to an intelligent decision making technology, and discloses a course recommendation method, which comprises the following steps: performing capability evaluation on the user to be recommended by using a pre-trained capability evaluation model according to the working data to obtain a capability index corresponding to each capability dimension in a preset capability dimension set; calculating the association degree of the ability dimension and the course label to obtain an initial recommendation index; performing weighted calculation according to the initial recommendation index and the ability index to obtain a recommendation index corresponding to each course; and screening all courses in the course set by using the recommendation index and a preset recommendation threshold, and pushing the screened courses to the user to be recommended. The invention also relates to a blockchain technique, the working data can be stored in blockchain link points. The invention also provides a course recommending device, equipment and a medium. The method and the system can improve the accuracy of course recommendation.
Description
Technical Field
The present invention relates to intelligent decision making technologies, and in particular, to a course recommendation method and apparatus, an electronic device, and a storage medium.
Background
In order to better improve the ability of the staff during the actual business operation, the staff needs to be frequently recommended with relevant training courses.
However, the existing course recommendation method does not screen courses, does not consider the difference of the working capacity of business personnel, and directly recommends all training courses to the business personnel, so that the accuracy rate of course recommendation is low.
Disclosure of Invention
The invention provides a course recommendation method, a course recommendation device, electronic equipment and a storage medium, and mainly aims to improve the accuracy of course recommendation.
Acquiring working data of a user to be recommended;
performing capability evaluation on the user to be recommended by using a pre-trained capability evaluation model according to the working data to obtain a capability index corresponding to each capability dimension in a preset capability dimension set;
acquiring a course set, wherein each course in the course set has a corresponding course label;
calculating the association degree of the ability dimension and the course label to obtain an initial recommendation index;
performing weighted calculation according to the initial recommendation index and the capability index to obtain a recommendation index corresponding to each course;
and screening all courses in the course set by using the recommendation index and a preset recommendation threshold value, and pushing the screened courses to preset terminal equipment of the user to be recommended.
Optionally, the performing, according to the working data, capability evaluation on the user to be recommended by using a pre-trained capability evaluation model to obtain a capability index corresponding to each capability dimension in a preset capability dimension set includes:
converting the working data into a vector form to obtain a working characteristic vector;
and inputting the working characteristic vectors into the capability evaluation model, and calculating by using a preset activation function in the capability evaluation model to obtain a capability index corresponding to each capability dimension.
Optionally, before the capability evaluation is performed on the user to be recommended by using a pre-trained capability evaluation model according to the working data, the method further includes:
acquiring a preset historical working data set, wherein each piece of historical working data in the historical working data set has a capability label value corresponding to each capability dimension;
converting each historical working data into a vector form to obtain a historical working vector;
the capability tag value corresponding to each historical working data is compared with the historical working vector corresponding to the historical working data;
and performing iterative training on the pre-constructed deep learning model by using the historical working vectors of all the marks to obtain the capability evaluation model.
Optionally, the calculating the association degree between the capability dimension and the course label to obtain an initial recommendation index includes:
converting the course labels into vectors to obtain course vectors;
converting the capability dimension into a vector to obtain a capability dimension vector;
and calculating the similarity between the course vector and the capability dimension vector to obtain the initial recommendation index.
Optionally, the converting the course label into a vector to obtain a course vector includes:
converting each character of the course label into a vector to obtain a character vector;
determining the character vector characteristic value of the character vector by the median of all elements in the character vector;
and connecting all the character vector characteristic values according to the sequence of the characters corresponding to the character vectors in the course labels to obtain the course vectors.
Optionally, the performing weighted calculation according to the initial recommendation index and the ability index to obtain a recommendation index corresponding to each course includes:
calculating the ability index corresponding to the ability dimension and each initial recommendation index corresponding to the ability dimension to obtain a weight index of each initial recommendation index;
and calculating all the weight indexes corresponding to the courses to obtain the recommendation index.
Optionally, the method further includes screening all the courses in the course set by using the recommendation index and a preset recommendation threshold, and pushing the screened courses to a preset terminal device of the user to be recommended, where the method further includes:
determining the recommendation index larger than the recommendation threshold as a target recommendation index;
arranging all the target recommendation indexes according to the numerical sequence from large to small to obtain a target recommendation index sequence;
and the course recommendation values corresponding to all target recommendation indexes before the preset ranking in the target recommendation index sequence are sent to the terminal equipment.
In order to solve the above problem, the present invention also provides a course recommending apparatus, comprising:
the capability evaluation module is used for acquiring the working data of the user to be recommended; performing capability evaluation on the user to be recommended by using a pre-trained capability evaluation model according to the working data to obtain a capability index corresponding to each capability dimension in a preset capability dimension set;
the association calculation module is used for acquiring a course set, wherein each course in the course set has a corresponding course label; calculating the association degree of the ability dimension and the course label to obtain an initial recommendation index;
the course recommending module is used for carrying out weighted calculation according to the initial recommending index and the capability index to obtain a recommending index corresponding to each course; and screening all courses in the course set by using the recommendation index and a preset recommendation threshold value, and pushing the screened courses to preset terminal equipment of the user to be recommended.
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 course 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, the at least one computer program being executed by a processor in an electronic device to implement the course recommendation method described above.
According to the embodiment of the invention, capacity evaluation is carried out on the user to be recommended by utilizing a capacity evaluation model which is trained in advance according to the working data, and a capacity index corresponding to each capacity dimension in a preset capacity dimension set is obtained; calculating the association degree of the ability dimension and the course label to obtain an initial recommendation index;
performing weighted calculation according to the initial recommendation index and the ability index to obtain a recommendation index corresponding to each course;
and screening all courses in the course set by using the recommendation index and a preset recommendation threshold value, and pushing the screened courses to preset terminal equipment of the user to be recommended. The course suitable for the ability of the user to be recommended is screened for recommendation through the relevance between the evaluation result of each ability dimension of the user to be recommended and the course label, and the accuracy of course recommendation is improved.
Drawings
FIG. 1 is a flowchart illustrating a course recommendation method according to an embodiment of the present invention;
FIG. 2 is a block diagram of a course recommending apparatus according to an embodiment of the present invention;
fig. 3 is a schematic internal structural diagram of an electronic device implementing a course 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 do not limit the invention.
The embodiment of the invention provides a course recommendation method. The execution subject of the course recommendation method includes, but is not limited to, at least one of the electronic devices that can be configured to execute the method provided by the embodiments of the present application, such as a server, a terminal, and the like. In other words, the course 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 system comprises a single server, a server cluster, a cloud server or a cloud server cluster, and the like, wherein the server can be an independent server, and can also be a cloud server for providing basic cloud computing services such as cloud service, a cloud database, cloud computing, cloud functions, cloud storage, Network service, cloud communication, middleware service, domain name service, security service, Content Delivery Network (CDN), big data, an artificial intelligence platform and the like.
Referring to fig. 1, a flowchart of a course recommendation method according to an embodiment of the present invention is shown, in the embodiment of the present invention, the course recommendation method includes:
and S1, acquiring the working data of the user to be recommended.
In the embodiment of the present invention, the user to be recommended is a salesman who needs training course recommendation, for example: insurance sales personnel.
Further, in the embodiment of the present invention, the working data includes numerical data and character data, which are data of a relevant working index capable of evaluating the working capacity of the user to be recommended, such as: the employee position, the employee department age, the employee post duration, the employee sales and the like, and the embodiment of the invention does not limit the scope and the concrete representation form of the work data.
In another embodiment of the invention, the working data can be stored in the blockchain nodes, and the data access efficiency is improved by utilizing the characteristic of high throughput of the blockchain nodes.
And S2, performing capability evaluation on the user to be recommended by using a pre-trained capability evaluation model according to the working data to obtain a capability index corresponding to each capability dimension in a preset capability dimension set.
The capability evaluation model in the embodiment of the invention is a deep learning model after training, and includes but is not limited to: a convolutional neural network model and a random forest network model.
In detail, the preset capability dimension set in the embodiment of the present invention includes various set capability dimensions that need to be evaluated, such as: work attitude, work knowledge, work method, etc.
Further, according to the working data, the working capacity of the user to be recommended is evaluated from the perspective of each capacity dimension in a preset capacity dimension set by using a capacity evaluation model trained in advance, namely, the capacity index of each capacity dimension is calculated to measure the capacity of the user with the capacity dimension.
Specifically, in the embodiment of the present invention, performing capability evaluation on the user to be recommended according to the working data by using a pre-trained capability evaluation model to obtain a capability index corresponding to each capability dimension in a preset capability dimension set, includes:
converting the working data into a vector form to obtain a working characteristic vector;
optionally, in the embodiment of the present invention, a specific method for converting the working data into a vector form is not limited.
And inputting the working characteristic vectors into the capability evaluation model, and calculating by using a preset activation function in the capability evaluation model to obtain a capability index corresponding to each capability dimension.
Optionally, in the embodiment of the present invention, the working feature vector is input into the capability evaluation model, and a capability index corresponding to each capability dimension of the working feature vector is calculated by using a softmax function in the capability evaluation model.
Further, in the embodiment of the present invention, before performing capability evaluation on the user to be recommended by using a pre-trained capability evaluation model according to the working data to obtain a capability index corresponding to each capability dimension in a preset capability dimension set, the method further includes:
acquiring a preset historical working data set, wherein each piece of historical working data in the historical working data set has a capability label value corresponding to each capability dimension;
optionally, in the embodiment of the present invention, the capability tag value is a parameter for evaluating the capability of the capability dimension corresponding to the user to which the historical working data belongs.
Converting each historical working data into a vector form to obtain a historical working vector;
the capability tag value corresponding to each historical working data is compared with the historical working vector corresponding to the historical working data;
and performing iterative training on the pre-constructed deep learning model by using the historical working vectors of all the marks to obtain the capability evaluation model.
Specifically, in the embodiment of the present invention, the performing iterative training on the pre-constructed deep learning model by using the historical working vectors of all the marks to obtain the capability evaluation model includes:
calculating a capability evaluation value corresponding to each capability dimension of the historical working vector by using an activation function preset in the deep learning model;
optionally, in an embodiment of the present invention, the activation function is a softmax function, and a category of a capacity dimension corresponding to the capacity assessment value is the same as a category of a capacity dimension in the capacity dimension set. Such as: and sharing two products of the capability dimension A and the capability dimension B in the product set, wherein the obtained capability evaluation values are the recommendation probability value of the capability dimension A and the capability evaluation value of the capability dimension B respectively.
And B, calculating by using a preset loss function according to the capability evaluation value and the capability label value to obtain a loss value, updating the model parameters of the deep learning model when the loss value is greater than or equal to a preset threshold value, and returning to the step A until the loss value is less than the preset threshold value, outputting the deep learning model to obtain the capability evaluation model.
Optionally, in an embodiment of the present invention, the loss function is a cross-entropy loss function.
S3, acquiring a course set, wherein each course in the course set has a corresponding course label.
In the embodiment of the present invention, the course is a skill training course for a user, wherein the course label is a short text for summarizing contents corresponding to the course, and is used for classifying or summarizing the contents of the course.
S4, calculating the association degree of the ability dimension and the course label to obtain an initial recommendation index.
According to the embodiment of the invention, the matching degree of the course and the user to be recommended is measured by calculating the association degree of the course label and the capability dimension.
Specifically, in the embodiment of the present invention, calculating the association degree between the course label and the capability dimension to obtain an initial recommendation index includes:
converting the course labels into vectors to obtain course vectors;
converting the capability dimension into a vector to obtain a capability dimension vector;
and calculating the similarity between the course vector and the capability dimension vector to obtain the initial recommendation index.
The method for calculating the similarity between the course vector and the capability dimension vector is not limited in the embodiment of the present invention, and optionally, the similarity between the course vector and the capability dimension vector may be calculated by using a euclidean distance algorithm, a pearson correlation coefficient algorithm, and a cosine similarity in the embodiment of the present invention.
Further, in the embodiment of the present invention, converting the course label into a vector to obtain a course vector, includes:
step a: converting each character of the course label into a vector to obtain a character vector;
in the embodiment of the present invention, a method for converting each character of the course label into a vector is not limited, and optionally, in the embodiment of the present invention, each character of the course label may be converted into a vector by using a model or method such as one-hot algorithm, Word2Vec model, bert model, and the like.
Step b: determining the character vector characteristic value of the character vector by the median of all elements in the character vector;
optionally, another embodiment of the present invention may further determine a word vector feature value of the character vector by using a maximum value or an average value of all elements in the character vector.
Step c: and connecting all the character vector characteristic values according to the sequence of the characters corresponding to the character vector to which the character vector belongs in the course label to obtain the course vector.
In another embodiment of the present invention, converting the course label into a vector to obtain a course vector, includes:
step I: performing word segmentation on the course label to obtain a label word;
step II: converting each of the tagged words into a word vector;
optionally, in the embodiment of the present invention, each of the tagged words may be converted into a Word vector by using a model or method such as a one-hot algorithm, a Word2Vec model, a bert model, and the like.
Step III: determining a word vector characteristic value of the word vector according to the maximum value of all elements in the word vector;
optionally, another embodiment of the present invention may further determine a word vector feature value of the word vector by averaging all elements in the word vector.
Step IV: and connecting all the word vector characteristic values according to the sequence of the label words corresponding to the word vectors in the course labels to obtain the course vectors.
And S5, performing weighted calculation according to the initial recommendation index and the ability index to obtain a recommendation index corresponding to each course.
In the embodiment of the present invention, performing weighted calculation according to the initial recommendation index and the ability index to obtain a recommendation index corresponding to each course, includes:
calculating the ability index corresponding to the ability dimension and each initial recommendation index corresponding to the ability dimension to obtain a weight index of each initial recommendation index;
and calculating all the weight indexes corresponding to the courses to obtain the recommendation indexes.
For example: the course A corresponds to two initial recommendation indexes, namely an initial recommendation index A and an initial recommendation index B, the preset capability dimension set comprises two capability dimensions, namely a capability dimension A and a capability dimension B, the capability dimension A corresponds to the initial recommendation index A, the capability dimension B corresponds to the initial recommendation index B, the capability index corresponding to the capability dimension A is 0.2, the capability index corresponding to the capability dimension B is 0.3, the weight index of the initial recommendation index A is (the initial recommendation index A is 0.2), the weight index of the initial recommendation index B is (the initial recommendation index B is 0.3), and the recommendation index corresponding to the course A is (the initial recommendation index A is 0.2) + (the initial recommendation index B is 0.3).
S6, screening all courses in the course set by using the recommendation index and a preset recommendation threshold, and pushing the screened courses to a preset terminal device of the user to be recommended.
In the embodiment of the invention, in order to recommend the courses more conforming to the user to be recommended, all courses in the course set need to be screened.
In detail, in the embodiment of the present invention, the screening of all the courses in the course set by using the recommendation index and a preset recommendation threshold, and the pushing of the screened courses to the preset terminal device of the user to be recommended include:
determining a recommendation index larger than the recommendation threshold as a target recommendation index;
and recommending the courses corresponding to the target recommendation index to the terminal equipment.
In another embodiment of the present invention, the method for screening all courses in the course set by using the recommendation index and a preset recommendation threshold and pushing the screened courses to a preset terminal device of the user to be recommended includes:
determining a recommendation index larger than the recommendation threshold as a target recommendation index;
arranging all the target recommendation indexes according to the numerical value from large to small to obtain a target recommendation index sequence;
and the course recommendation values corresponding to all target recommendation indexes before the preset ranking in the target recommendation index sequence are sent to the terminal equipment.
In the embodiment of the present invention, the setting rule of the preset ranking is not limited.
In another embodiment of the present invention, the method for screening all courses in the course set by using the recommendation index and a preset recommendation threshold and pushing the screened courses to a preset terminal device of the user to be recommended includes:
judging whether a recommendation index larger than the recommendation threshold exists or not;
when there is a recommendation index greater than the recommendation threshold;
determining a recommendation index larger than the recommendation threshold as a target recommendation index;
arranging all the target recommendation indexes according to the numerical sequence from large to small to obtain a target recommendation index sequence;
and the course recommendation values corresponding to all target recommendation indexes before the preset ranking in the target recommendation index sequence are sent to the terminal equipment.
When there is no recommendation index greater than the recommendation threshold;
and pushing the courses corresponding to the maximum recommendation index in all the recommendation indexes to the terminal equipment.
In this embodiment of the present invention, the terminal device is a terminal capable of receiving and displaying information (such as one or more of video information, audio information, and text information), and the specific representation form of the terminal is not limited in this embodiment of the present invention, and optionally, in this embodiment of the present invention, the terminal device includes: intelligent terminals such as mobile phones, computers and tablets.
FIG. 2 is a functional block diagram of the course recommending apparatus according to the present invention.
The course recommending apparatus 100 of the present invention can be installed in an electronic device. Depending on the implemented functions, the course recommending apparatus may include a capability evaluating module 101, an association calculating module 102, and a course recommending 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 ability evaluation module 101 is used for acquiring the working data of the user to be recommended; performing capability evaluation on the user to be recommended by using a pre-trained capability evaluation model according to the working data to obtain a capability index corresponding to each capability dimension in a preset capability dimension set;
the association calculation module 102 is configured to obtain a course set, where each course in the course set has a corresponding course tag; calculating the association degree of the ability dimension and the course label to obtain an initial recommendation index;
the course recommending module 103 is configured to perform weighted calculation according to the initial recommendation index and the ability index to obtain a recommendation index corresponding to each course; and screening all courses in the course set by using the recommendation index and a preset recommendation threshold value, and pushing the screened courses to preset terminal equipment of the user to be recommended.
In detail, when the modules in the course recommending apparatus 100 according to the embodiment of the present invention are used, the same technical means as the course recommending method described in fig. 1 is adopted, 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 course 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 a course 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, 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 can be used not only for storing application software installed in the electronic device and various types of data, such as codes of course recommendation programs, etc., but also for temporarily storing 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 whole electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (e.g., a course recommendation program, 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 such as charge management, discharge management, and power consumption management are implemented 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 course recommendation program stored in the memory 11 of the electronic device is a combination of a plurality of computer programs, which when run in the processor 10, can implement:
acquiring working data of a user to be recommended;
performing capability evaluation on the user to be recommended by using a pre-trained capability evaluation model according to the working data to obtain a capability index corresponding to each capability dimension in a preset capability dimension set;
acquiring a course set, wherein each course in the course set has a corresponding course label;
calculating the association degree of the ability dimension and the course label to obtain an initial recommendation index;
performing weighted calculation according to the initial recommendation index and the ability index to obtain a recommendation index corresponding to each course;
and screening all courses in the course set by using the recommendation index and a preset recommendation threshold value, and pushing the screened courses to preset terminal equipment of the user to be recommended.
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, a recording medium, a usb-disk, a removable hard disk, a magnetic diskette, an optical disk, a computer Memory, a 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 working data of a user to be recommended;
performing capability evaluation on the user to be recommended by using a pre-trained capability evaluation model according to the working data to obtain a capability index corresponding to each capability dimension in a preset capability dimension set;
acquiring a course set, wherein each course in the course set has a corresponding course label;
calculating the association degree of the ability dimension and the course label to obtain an initial recommendation index;
performing weighted calculation according to the initial recommendation index and the ability index to obtain a recommendation index corresponding to each course;
and screening all courses in the course set by using the recommendation index and a preset recommendation threshold value, and pushing the screened courses to preset terminal equipment of the user to be recommended.
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 position, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
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 to denote any particular order.
Finally, it should be noted that the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the same, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A course recommendation method, the method comprising:
acquiring working data of a user to be recommended;
performing capability evaluation on the user to be recommended by using a pre-trained capability evaluation model according to the working data to obtain a capability index corresponding to each capability dimension in a preset capability dimension set;
acquiring a course set, wherein each course in the course set has a corresponding course label;
calculating the association degree of the ability dimension and the course label to obtain an initial recommendation index;
performing weighted calculation according to the initial recommendation index and the capability index to obtain a recommendation index corresponding to each course;
and screening all courses in the course set by using the recommendation index and a preset recommendation threshold value, and pushing the screened courses to preset terminal equipment of the user to be recommended.
2. The course recommendation method as claimed in claim 1, wherein said performing capability evaluation on the user to be recommended according to the working data by using a pre-trained capability evaluation model to obtain a capability index corresponding to each capability dimension in a preset capability dimension set comprises:
converting the working data into a vector form to obtain a working characteristic vector;
and inputting the working characteristic vectors into the capability evaluation model, and calculating by using a preset activation function in the capability evaluation model to obtain a capability index corresponding to each capability dimension.
3. The course recommendation method as claimed in claim 1, wherein before performing the capability evaluation on the user to be recommended by using the pre-trained capability evaluation model according to the working data, the method further comprises:
acquiring a preset historical working data set, wherein each historical working data in the historical working data set has a capability label value corresponding to each capability dimension;
converting each historical working data into a vector form to obtain a historical working vector;
the capability tag value corresponding to each historical working data is compared with the historical working vector corresponding to the historical working data;
and performing iterative training on the pre-constructed deep learning model by using the historical working vectors of all the marks to obtain the capability evaluation model.
4. The course recommendation method of claim 1, wherein said calculating a degree of association between said capability dimension and said course label to obtain an initial recommendation index comprises:
converting the course labels into vectors to obtain course vectors;
converting the capability dimension into a vector to obtain a capability dimension vector;
and calculating the similarity between the course vector and the capability dimension vector to obtain the initial recommendation index.
5. The course recommendation method of claim 4, wherein said converting said course tag into a vector to obtain a course vector comprises:
converting each character of the course label into a vector to obtain a character vector;
determining the character vector characteristic value of the character vector by the median of all elements in the character vector;
and connecting all the character vector characteristic values according to the sequence of the characters corresponding to the character vector to which the character vector belongs in the course label to obtain the course vector.
6. The course recommendation method of claim 1, wherein said performing a weighted calculation according to said initial recommendation index and said ability index to obtain a recommendation index corresponding to each of said courses comprises:
calculating the ability index corresponding to the ability dimension and each initial recommendation index corresponding to the ability dimension to obtain a weight index of each initial recommendation index;
and calculating all the weight indexes corresponding to the courses to obtain the recommendation indexes.
7. The course recommendation method according to any one of claims 1 to 6, wherein all courses in the set of courses are screened by using the recommendation index and a preset recommendation threshold, and the screened courses are pushed to a preset terminal device of the user to be recommended, the method further comprising:
determining a recommendation index larger than the recommendation threshold as a target recommendation index;
arranging all the target recommendation indexes according to the numerical value from large to small to obtain a target recommendation index sequence;
and the course recommendation values corresponding to all target recommendation indexes before the preset ranking in the target recommendation index sequence are sent to the terminal equipment.
8. A course recommending apparatus, comprising:
the capability evaluation module is used for acquiring the working data of the user to be recommended; performing capability evaluation on the user to be recommended by using a pre-trained capability evaluation model according to the working data to obtain a capability index corresponding to each capability dimension in a preset capability dimension set;
the association calculation module is used for acquiring a course set, wherein each course in the course set has a corresponding course label; calculating the association degree of the ability dimension and the course label to obtain an initial recommendation index;
the course recommending module is used for carrying out weighted calculation according to the initial recommending index and the capability index to obtain a recommending index corresponding to each course; and screening all courses in the course set by using the recommendation index and a preset recommendation threshold value, and pushing the screened courses to preset terminal equipment of the user to be recommended.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor;
wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the course 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 course recommendation method as recited in any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210356827.0A CN114781832A (en) | 2022-04-06 | 2022-04-06 | Course recommendation method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210356827.0A CN114781832A (en) | 2022-04-06 | 2022-04-06 | Course recommendation method and device, electronic equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114781832A true CN114781832A (en) | 2022-07-22 |
Family
ID=82428051
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210356827.0A Pending CN114781832A (en) | 2022-04-06 | 2022-04-06 | Course recommendation method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114781832A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114996588A (en) * | 2022-08-01 | 2022-09-02 | 平安科技（深圳）有限公司 | Product recommendation method, device, equipment and storage medium based on double-tower model |
| CN115757950A (en) * | 2022-11-15 | 2023-03-07 | 读书郎教育科技有限公司 | Learning system based on AI intelligence recommendation |
| CN116070861A (en) * | 2023-02-08 | 2023-05-05 | 武汉博奥鹏程教育科技有限公司 | Course customization method and device based on dynamic learning target |
-
2022
- 2022-04-06 CN CN202210356827.0A patent/CN114781832A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114996588A (en) * | 2022-08-01 | 2022-09-02 | 平安科技（深圳）有限公司 | Product recommendation method, device, equipment and storage medium based on double-tower model |
| CN114996588B (en) * | 2022-08-01 | 2022-10-21 | 平安科技（深圳）有限公司 | Product recommendation method, device, equipment and storage medium based on double-tower model |
| CN115757950A (en) * | 2022-11-15 | 2023-03-07 | 读书郎教育科技有限公司 | Learning system based on AI intelligence recommendation |
| CN115757950B (en) * | 2022-11-15 | 2023-09-26 | 读书郎教育科技有限公司 | Learning system based on AI intelligent recommendation |
| CN116070861A (en) * | 2023-02-08 | 2023-05-05 | 武汉博奥鹏程教育科技有限公司 | Course customization method and device based on dynamic learning target |
| CN116070861B (en) * | 2023-02-08 | 2023-08-04 | 武汉博奥鹏程教育科技有限公司 | Course customization method and device based on dynamic learning target |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114781832A (en) | Course recommendation method and device, electronic equipment and storage medium | |
| CN112883190A (en) | Text classification method and device, electronic equipment and storage medium | |
| CN114663198A (en) | Product recommendation method, device and equipment based on user portrait and storage medium | |
| CN114612194A (en) | Product recommendation method and device, electronic equipment and storage medium | |
| CN115002200A (en) | User portrait based message pushing method, device, equipment and storage medium | |
| CN113688923A (en) | Intelligent order abnormity detection method and device, electronic equipment and storage medium | |
| CN114491047A (en) | Multi-label text classification method and device, electronic equipment and storage medium | |
| CN113868529A (en) | Knowledge recommendation method and device, electronic equipment and readable storage medium | |
| CN113657495A (en) | Insurance product recommendation method, device and equipment based on probability prediction model | |
| CN114186132A (en) | Information recommendation method and device, electronic equipment and storage medium | |
| CN113516417A (en) | Service evaluation method and device based on intelligent modeling, electronic equipment and medium | |
| CN113868528A (en) | Information recommendation method and device, electronic equipment and readable storage medium | |
| CN113505273B (en) | Data sorting method, device, equipment and medium based on repeated data screening | |
| CN111930897B (en) | Patent retrieval method, device, electronic equipment and computer-readable storage medium | |
| CN113837631A (en) | Employee evaluation method and device, electronic device and readable storage medium | |
| CN113658002A (en) | Decision tree-based transaction result generation method and device, electronic equipment and medium | |
| CN113268665A (en) | Information recommendation method, device and equipment based on random forest and storage medium | |
| CN112269875A (en) | Text classification method and device, electronic equipment and storage medium | |
| CN113656690B (en) | Product recommendation method and device, electronic equipment and readable storage medium | |
| CN113705201B (en) | Text-based event probability prediction evaluation algorithm, electronic device and storage medium | |
| CN115982454A (en) | User portrait based questionnaire pushing method, device, equipment and storage medium | |
| CN113706019A (en) | Service capability analysis method, device, equipment and medium based on multidimensional data | |
| CN114219367A (en) | User scoring method, device, equipment and storage medium | |
| CN113486646A (en) | Product report issuing method and device, electronic equipment and readable storage medium | |
| CN113626605A (en) | Information classification method and device, electronic equipment and readable storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |