CN112632140A - Course recommendation method, device, equipment and storage medium - Google Patents
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Abstract
The invention discloses a course recommendation method, a course recommendation device, a course recommendation equipment and a storage medium, wherein the course recommendation method comprises the following steps: calculating user similarity and course similarity according to historical course selection data of the user; calculating a first interest degree of the user to the recommended courses according to the user similarity, and taking a first preset number of courses with higher first interest degree as a first recommended course set; calculating a second interest degree of the user to the recommended courses according to the course similarity, and taking a second preset number of courses with higher second interest degree as a second recommended course set; and pushing the first recommended course set and the second recommended course set to a user together. According to the course recommending method disclosed by the invention, different courses can be quickly recommended to different users, so that the users can quickly find interesting courses, and user information and course information can be kept secret when the courses are recommended, so that the learning quality and the learning efficiency are improved, and the user experience is greatly improved.
Description
Technical Field
The invention relates to the technical field of intelligent education, in particular to a course recommendation method, a course recommendation device, course recommendation equipment and a storage medium.
Background
With the rapid development of internet technology, the network learning system has become an important platform for people to learn knowledge, users of the online education platform grow in a geometric form, and in order to meet the learning requirements of different users, the contents of various online education platforms are continuously rich, the coverage is continuously enlarged, and more course selection and learning spaces are provided for registered users. However, the intake demand of information varies from person to person depending on individual differences. Therefore, how to quickly and efficiently recommend interesting courses to the user according to the historical learning data of the user is particularly important.
In the prior art, the online learning platform mostly acquires the courses required by the online learning platform in a manner of active retrieval of the user, and the recommendation function is pushed in forms of new product online, ranking list and the like, so that the courses interested by the user cannot be recommended well according to the personalized requirements of different users. The user often wastes time and labor to find out the interested course from a plurality of courses, and the historical learning data of the user cannot be well utilized.
Disclosure of Invention
The embodiment of the disclosure provides a course recommending method, a course recommending device and a course recommending equipment and a storage medium. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
In a first aspect, an embodiment of the present disclosure provides a course recommendation method, including:
calculating user similarity and course similarity according to historical course selection data of the user;
calculating a first interest degree of the user to the recommended courses according to the similarity of the user, and taking a first preset number of courses with higher first interest degree as a first recommended course set;
calculating a second interest degree of the user to the recommended courses according to the similarity of the courses, and taking a second preset number of courses with higher second interest degree as a second recommended course set;
and pushing the first recommended course set and the second recommended course set to the user together.
In one embodiment, pushing the first set of recommended courses and the second set of recommended courses together to the user includes:
and all the courses in the first recommended course set and the second recommended course set are subjected to priority ranking, and then the courses are pushed to the user together according to the sequence of the priorities from high to low.
In one embodiment, before calculating the user similarity and the course similarity according to the historical course selection data of the user, the method further includes:
identifying sensitive information of a user when historical course selection data of the user is acquired, wherein the sensitive information comprises character sensitive information and image sensitive information;
desensitizing sensitive information.
In one embodiment, desensitizing sensitive information includes:
extracting image characteristics of an image to be desensitized in the sensitive information;
matching the mapping table of the template image with the image characteristics of the image to be desensitized, and determining the position of the area to be desensitized of the image to be desensitized;
desensitizing the region to be desensitized of the image to be desensitized;
extracting characters to be desensitized in sensitive information;
desensitization operation is carried out on the characters to be desensitized by adopting a preset character desensitization rule.
In one embodiment, calculating the user similarity and the course similarity according to the historical course selection data of the user comprises:
calculating a user similarity matrix and a course similarity matrix according to historical course selection data;
calculating the user similarity according to the user similarity matrix;
and calculating the course similarity according to the course similarity matrix.
In one embodiment, calculating a first interest degree of the user in the courses to be recommended according to the user similarity includes calculating a first interest degree of the user in the courses to be recommended by using a first interest degree formula, where the first interest degree is:
wherein p (u, i) represents the interest degree of the user u in the course i, S (u, v) is a set of K users with the interest close to the interest of the user u, N (i) is a set of users with past behaviors on the course i, then the two sets take the intersection, WuvIs the similarity of user u and user v, rviRepresenting the interest level of user v in lesson i.
In one embodiment, calculating a second interest degree of the user in the to-be-recommended course according to the course similarity includes calculating a second interest degree of the user in the to-be-recommended course by using a second interest degree formula, where the second interest degree is:
wherein p (u, j) represents the interest level of the user u in the course j, S (j, K) represents a set of K courses similar to the course j, N (u) represents a set of courses liked by the user u, WjiRepresenting the degree of similarity between courses, ruiRepresenting the interest of user u in lesson i.
In a second aspect, an embodiment of the present disclosure provides a course recommending apparatus, including:
the first calculation module is used for calculating the similarity of the users and the similarity of courses according to historical course selection data of the users;
the second calculation module is used for calculating a first interest degree of the user to the courses to be recommended according to the user similarity, and taking a first preset number of courses with higher first interest degree as a first recommended course set;
the third calculation module is used for calculating a second interest degree of the user to the recommended courses according to the similarity of the courses, and taking a second preset number of courses with higher second interest degree as a second recommended course set;
and the recommending module is used for pushing the first recommended course set and the second recommended course set to the user together.
In a third aspect, an embodiment of the present disclosure provides a computer device, including a memory and a processor, where the memory stores computer readable instructions, and the computer readable instructions, when executed by the processor, cause the processor to perform the steps of the course recommendation method provided in the foregoing embodiment.
In a fourth aspect, the present disclosure provides a storage medium storing computer-readable instructions, which when executed by one or more processors, cause the one or more processors to perform the steps of the course recommendation method provided in the foregoing embodiments.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
according to the course personalized recommendation method provided by the application, the user similarity and the course similarity are calculated according to the historical course selection data of the user, the interest degree of the courses to be recommended is calculated for different users by combining the user similarity and the course similarity, the courses to be recommended are sequenced according to the interest degree of the users, and the courses with higher interest degree of the users are recommended to the users. The method can quickly recommend different courses to different users, so that the users can quickly find interesting courses, and user information and course information can be protected when the courses are recommended, thereby not only improving the learning quality and the learning efficiency, but also greatly improving the experience of the users.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a diagram illustrating an environment in which a course recommendation method may be implemented, according to an exemplary embodiment;
FIG. 2 is a diagram illustrating an internal structure of a computer device in accordance with one illustrative embodiment;
FIG. 3 is a flowchart illustrating a course recommendation method in accordance with an exemplary embodiment;
FIG. 4 is a diagram illustrating a course recommendation method in accordance with an exemplary embodiment;
FIG. 5 is a diagram illustrating user lesson selection data in accordance with an exemplary embodiment;
FIG. 6 is a schematic diagram illustrating a user similarity matrix in accordance with an exemplary embodiment;
FIG. 7 is a diagram illustrating a course similarity matrix, according to an exemplary embodiment;
FIG. 8 is a block diagram illustrating a course recommender, according to an exemplary embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
It will be understood that, as used herein, the terms "first," "second," and the like may be used herein to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish one element from another. For example, a first field and algorithm determination module may be referred to as a second field and algorithm determination module, and similarly, a second field and algorithm determination module may be referred to as a first field and algorithm determination module, without departing from the scope of the present application.
Fig. 1 is a diagram illustrating an implementation environment of a course recommendation method according to an exemplary embodiment, as shown in fig. 1, in which a server 110 and a terminal 120 are included.
The server 110 is a course recommendation device, such as a computer device used by a technician, and the course recommendation tool is installed on the server 110. The terminal 120 is installed with an application that needs to perform course recommendation, and when course recommendation needs to be provided, the technician may send a request for providing course recommendation at the computer device 110, where the request carries a request identifier, and the computer device 110 receives the request to obtain a course recommendation method stored in the computer device 110. And then recommending the course to the user by using the course recommending method.
It should be noted that the terminal 120 and the computer device 110 may be, but are not limited to, a smart phone, a tablet computer, a notebook computer, a desktop computer, and the like. The computer device 110 and the terminal 120 may be connected through bluetooth, USB (Universal Serial Bus), or other communication connection methods, which is not limited herein.
FIG. 2 is a diagram illustrating an internal structure of a computer device according to an exemplary embodiment. As shown in fig. 2, the computer device includes a processor, a non-volatile storage medium, a memory, and a network interface connected through a system bus. The non-volatile storage medium of the computer device stores an operating system, a database and computer readable instructions, the database can store control information sequences, and the computer readable instructions can make the processor realize a course recommendation method when being executed by the processor. The processor of the computer device is used for providing calculation and control capability and supporting the operation of the whole computer device. The memory of the computer device may have stored therein computer readable instructions that, when executed by the processor, may cause the processor to perform a course recommendation method. The network interface of the computer device is used for connecting and communicating with the terminal. Those skilled in the art will appreciate that the architecture shown in fig. 2 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
The course recommendation method provided by the embodiment of the present application will be described in detail below with reference to fig. 3 to 7. The method may be implemented in dependence on a computer program, operable on a data transmission device based on the von neumann architecture. The computer program may be integrated into the application or may run as a separate tool-like application.
Referring to fig. 3, a flow chart of a course recommendation method is provided in the embodiment of the present application, and as shown in fig. 3, the method in the embodiment of the present application may include the following steps:
s301, calculating user similarity and course similarity according to historical course selection data of the user.
The user similarity refers to the degree of selecting the same course by different users, and the more the same courses selected by different users, the higher the similarity between the users.
Firstly, historical course selection data of a user, including a user ID and a course set learned by the user, stored in an online learning platform are obtained. As shown in fig. 5, the schematic diagram of the historical course selection data of the user includes a user a, a user B, a user C, and a user D, where the course selected by the user a includes a course a, a course B, and a course D, the course selected by the user B includes a course a and a course C, the course selected by the user C includes a course a and a course D, and the course selected by the user D includes a course e.
Obtaining a similarity matrix of the users according to the historical course selection data of the users, as shown in fig. 6, a node of the matrix represents the number of the same courses selected by the two corresponding users, the node is 0, which represents that the two users have not selected the same courses, and there is no similarity between the two users, and calculating the user similarity W where the node is not 0uv：
Where N (u) represents the set of courses selected by user u, and N (v) represents the set of courses selected by user v.
Further, considering the influence of the general courses pushed by the learning platform on the user similarity, the user similarity may be calculated according to the following formula:
wherein the content of the first and second substances,
and the influence of the common course selected by the user u and the user v on the similarity of the common course is expressed, so that the result is more accurate.
Further, a course similarity matrix is obtained according to historical course selection data, then the course similarity is calculated, the course similarity represents the degree of different courses selected by the same user, and it is assumed that the user selecting course A also selects course B, and the fact that the courses A and B have high similarity is shown.
As shown in fig. 7, it is a course similarity matrix of the user, where the nodes in the matrix represent the times that the corresponding courses are selected by the same user. The course similarity is calculated as follows:
wherein, n (i) represents a user set liking course i, n (j) represents a user set liking course j, and Wij indicates how many users liking course i also like course j, so as to obtain the similarity between i and j.
Further, considering some general courses pushed for the learning platform, the course similarity may be calculated as follows:
according to the steps, the user similarity and the course similarity can be calculated.
S302, according to the user similarity, a first interest degree of the user to the recommended courses is calculated, and a first preset number of courses with higher interest degrees are used as a first recommended course set.
Specifically, the following formula may be used to calculate a first degree of interest of the user in treating the recommended course:
wherein p (u, i) represents the interest degree of the user u in the course i, S (u, v) is the set of K users with the closest interest to the user u, N (i) is the set of users with past behaviors to the course i, then the two sets take the intersection, WuvIs the similarity of user u and user v, rviRepresenting the interest degree of the user v in the course i, the practical implementation may take the ratio of the time length of the user viewing the course i to the total time length of the course.
In a possible implementation manner, a course set which is not selected by the user is obtained according to the historical course selection data of the user, and the interest degree of the user in each course is calculated according to the formula.
And acquiring the first N courses with higher user interest degrees according to the calculated user interest degrees of each course and the Top-N analysis method, and taking the selected first N courses as a first recommended course set. The number N may be set by a person skilled in the art, and the embodiment of the present disclosure is not limited specifically.
S303, according to the similarity of the courses, calculating a second interest degree of the user to the courses to be recommended, and taking a second preset number of courses with higher second interest degree as a second recommended course set.
Specifically, the second degree of interest of the user in the to-be-recommended course may be calculated using the following formula:
where p (u, j) represents the level of interest by user u in lesson j, and S (j, K) represents the set of K lessons that are most similar to the lesson j. N (u) represents a collection of courses liked by user u. WjiRepresenting the similarity between courses. r isuiRepresenting the interest degree of the user u in the course i, the practical implementation may take the ratio of the time length of the user watching the course i to the total time length of the course.
In a possible implementation manner, a course set which is not selected by the user is obtained according to the historical course selection data of the user, and the interest degree of the user in each course is calculated according to the formula.
And acquiring the first N courses with higher user interest degrees according to the calculated user interest degrees of each course and the Top-N analysis method, and taking the selected first N courses as a second recommended course set. The number N may be set by a person skilled in the art, and the embodiment of the present disclosure is not limited specifically.
S304, the first recommended course set and the second recommended course set are pushed to the user together.
In a possible implementation manner, all the courses in the first recommended course set and the second recommended course set are subjected to priority ranking, and then are pushed to the user from high to low according to the priority, wherein the priority can be comprehensively evaluated according to factors such as the user interest degree, the course grading, the course opening time, the course liking degree and the like.
Specifically, after the recommended course set is obtained, the user interest degree of the courses, the historical scores of the courses, the setting time of the courses, the course liking degree (the comprehensive course playing times) are comprehensively considered, the priority of the courses in the course set is calculated, the courses with higher priorities are arranged in front, and the courses with higher comprehensive scores are preferentially recommended for the user.
Further, before calculating the user similarity and the course similarity according to the historical course selection data of the user, identifying sensitive information of the user when obtaining the historical course selection data of the user, wherein the sensitive information comprises character sensitive information and image sensitive information, and desensitizing the sensitive information.
Specifically, desensitization processing is performed on the text-sensitive information of the user, for example, the text-sensitive information such as an identity card, a name, an age, a mobile phone number, a communication address and the like of the user is identified, desensitization is performed in a mode formulated by a rule, desensitization processing is performed on the information in the program data transmission process, and the format is as follows: identity card: 320 × 17, name: liu, age: mobile phone number: 156 × 2, address: way, Shanghai; the specific mode is realized by conventional coding, a specific desensitization rule base is set through an interceptor, and corresponding information is transmitted after desensitization.
Desensitizing image sensitive information of the user, for example, identifying sensitive information such as a head portrait of the user, a user certificate type and the like, and desensitizing the image sensitive information.
In one embodiment, desensitizing image sensitive information of a user includes: extracting image characteristics of an image to be desensitized in the sensitive information; matching the mapping table of the template image with the image characteristics of the image to be desensitized, and determining the position of the area to be desensitized of the image to be desensitized; and carrying out desensitization operation on the region to be desensitized of the image to be desensitized.
Specifically, the method comprises a feature extraction technology, a template matching technology and an image desensitization processing technology, and the three are organically combined so as to achieve the purposes of automatically and accurately locking the position of the region to be desensitized and carrying out desensitization processing on the region to be desensitized. The characteristic extraction technology mainly adopts an image characteristic extraction technology, a computer is used for extracting image information, and points are divided into different subsets according to the characteristics of the points on an image so as to achieve the effect of defining the image characteristics of the image to be desensitized.
The template matching technology adopts the mapping table of the template image to be matched with the image characteristics of the image to be desensitized so as to determine the position of the area to be desensitized. The mapping table of the template image represents the mapping relationship between the position information of the area to be desensitized of the template image and the image characteristics of the template image, the mapping table of the template image is usually obtained in advance through the template image, the template image and the image with the same size and format layout as the image to be desensitized, or the partial area in the template image and the image with the same size and format layout as the image to be desensitized, or the template image and the image to be desensitized can form images with the same size and format through image processing. For example, the image to be desensitized is an identity card, the region with desensitization is a photograph in the identity card, and then the template image is an identity card template having a size format consistent with that of the identity card.
In the image desensitization processing technology, after the position of the region to be desensitized is determined, desensitization operation is performed on the region to be desensitized, which mainly aims to make contents displayed in the region to be desensitized invisible, and specifically may include mosaic processing, blurring processing of images, and the like performed on the region to be desensitized.
By desensitizing the user information, the privacy of the user can be guaranteed, and the problems of information tampering and information leakage are prevented.
In one embodiment, the course information includes a video file and a text file, and when the client displays the course information, if no special processing is performed, an actual URL address of the video file is exposed, so that encryption processing is required to avoid downloading and transmission, and a text file client performs a copy operation on a client or a browser page, which results in course leakage. In addition, when some users download video or other course information in a client mode, after retrieval is performed through special software, the storage addresses of the downloaded files can be inquired and further can be copied or shared to other people, so that special encryption processing is required.
In one possible implementation, the browser disables the course downloading function, only the client has the function of buffering to the local, and the browser hides the playing address of the video during playing.
Based on a refer mechanism supported by the HTTP protocol, the source of the request is identified through a refer field carried in the HTTP header. A developer can identify and authenticate the video request source by configuring a refer black and white list, and support two modes of the black list and the white list. When the video playing request reaches the CDN node, the node authenticates the request source according to a Referer black-and-white list configured by a user, the CDN returns video data for the request meeting the rules, otherwise, a 403 response code is returned to reject the playing request.
In a possible implementation manner, the method further includes encrypting the streaming media file to prevent the streaming media file from being stolen in the transmission process.
In a possible implementation mode, the text courses can be prevented from being copied, and for the Android mobile terminal APP, the SDK API is used for calling, and the screenshot function is closed. And for the IOS mobile terminal APP, calling through the SDK API, and closing the specific window copying function. For the PC browser, the screenshot and copy functions of a specific window are deactivated directly by JS.
According to the steps, the safety of the course information is greatly improved.
For further understanding of the course recommendation method provided by the embodiment of the present disclosure, the following description is made with reference to fig. 4, and as shown in fig. 4, first, historical course selection data of a user is obtained, user similarity is calculated according to the historical course selection data of the user, a first interest degree of the user in a course to be recommended is determined based on the user similarity, and the first recommended course set is defined as a first recommended course set with a high interest degree of the user; then, calculating the similarity of courses according to the historical course selection data of the user, determining a second interest degree of the user to the courses to be recommended based on the similarity of the courses, and taking the high interest degree of the user as a second recommended course set; and pushing the first recommended course set and the second recommended course set to the user together.
According to the course personalized recommendation method provided by the application, the interest degree of courses to be recommended is calculated for different users, the courses to be recommended are ranked according to the interest degree of the users, and the courses with higher interest degree of the users are recommended to the users. The method can quickly recommend different courses to different users, so that the users can quickly find interesting courses, and user information and course information can be protected when the courses are recommended, thereby not only improving the learning quality and the learning efficiency, but also greatly improving the experience of the users.
The following are embodiments of the apparatus of the present application that may be used to perform embodiments of the method of the present invention. For details which are not disclosed in the embodiments of the apparatus of the present invention, reference is made to the embodiments of the method of the present invention.
Referring to fig. 8, a schematic structural diagram of a course recommending apparatus according to an exemplary embodiment of the present invention is shown. As shown in fig. 8, the course recommending apparatus may be integrated in the computer device 110, and specifically may include a first calculating module 801, a second calculating module 802, a third calculating module 803, and a recommending module 804.
A first calculating module 801, configured to calculate user similarity and course similarity according to historical course selection data of a user;
the second calculating module 802 is configured to calculate a first interest degree of the user for the courses to be recommended according to the user similarity, and use a first preset number of courses with higher first interest degree as a first recommended course set;
the third calculating module 803 is configured to calculate a second interest degree of the user for the to-be-recommended courses according to the similarity of the courses, and use a second preset number of courses with higher second interest degree as a second recommended course set;
and the recommending module 804 is used for pushing the first recommended course set and the second recommended course set to the user together.
In one embodiment, the recommendation module 804 is configured to prioritize all the courses in the first recommended course set and the second recommended course set, and then push the courses to the user together in an order from high to low.
In one embodiment, the system further comprises a user information desensitization module, configured to identify sensitive information of the user when obtaining historical course selection data of the user, where the sensitive information includes text sensitive information and image sensitive information; desensitizing sensitive information.
In one embodiment, the user information desensitization module is used for extracting image features of an image to be desensitized in sensitive information; matching the mapping table of the template image with the image characteristics of the image to be desensitized, and determining the position of the area to be desensitized of the image to be desensitized; desensitizing the region to be desensitized of the image to be desensitized; extracting characters to be desensitized in sensitive information; desensitization operation is carried out on the characters to be desensitized by adopting a preset character desensitization rule.
In one embodiment, the first calculating module 801 is configured to calculate a user similarity matrix and a course similarity matrix according to historical course selection data; calculating the user similarity according to the user similarity matrix; and calculating the course similarity according to the course similarity matrix.
In one embodiment, the second calculating module 802 is configured to calculate a first interest degree of the user in the course to be recommended according to the user similarity, including calculating the first interest degree of the user in the course to be recommended by using a first interest degree formula, where the first interest degree is:
wherein p (u, i) represents the interest degree of the user u in the course i, S (u, v) is a set of K users with the interest close to the interest of the user u, N (i) is a set of users with past behaviors on the course i, then the two sets take the intersection, WuvIs the similarity of user u and user v, rviRepresenting the interest level of user v in lesson i.
In one embodiment, the third calculating module 803 is configured to calculate a second interest level of the user in the to-be-recommended course according to the similarity of the courses, including calculating a second interest level of the user in the to-be-recommended course by using a second interest level formula, where the second interest level is:
wherein p (u, j) represents the interest level of the user u in the course j, S (j, K) represents a set of K courses similar to the course j, N (u) represents a set of courses liked by the user u, WjiRepresenting the degree of similarity between courses, ruiRepresenting the interest of user u in lesson i.
It should be noted that, when the course recommending apparatus provided in the foregoing embodiment executes the course recommending method, only the division of the functional modules is illustrated, and in practical applications, the functions may be distributed and completed by different functional modules according to needs, that is, the internal structure of the device may be divided into different functional modules to complete all or part of the functions described above. In addition, the course recommending device and the course recommending method provided by the above embodiments belong to the same concept, and the detailed implementation process thereof is referred to as the method embodiment, which is not described herein again.
In one embodiment, a computer device is proposed, the computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program: calculating user similarity and course similarity according to historical course selection data of the user; calculating a first interest degree of the user to the recommended courses according to the similarity of the user, and taking a first preset number of courses with higher first interest degree as a first recommended course set; calculating a second interest degree of the user to the recommended courses according to the similarity of the courses, and taking a second preset number of courses with higher second interest degree as a second recommended course set; and pushing the first recommended course set and the second recommended course set to the user together.
In one embodiment, pushing the first set of recommended courses and the second set of recommended courses together to the user includes: and all the courses in the first recommended course set and the second recommended course set are subjected to priority ranking, and then the courses are pushed to the user together according to the sequence of the priorities from high to low.
In one embodiment, before calculating the user similarity and the course similarity according to the historical course selection data of the user, the method further includes: identifying sensitive information of a user when historical course selection data of the user is acquired, wherein the sensitive information comprises character sensitive information and image sensitive information; desensitizing sensitive information.
In one embodiment, desensitizing sensitive information includes: extracting image characteristics of an image to be desensitized in the sensitive information; matching the mapping table of the template image with the image characteristics of the image to be desensitized, and determining the position of the area to be desensitized of the image to be desensitized; desensitizing the region to be desensitized of the image to be desensitized; extracting characters to be desensitized in sensitive information; desensitization operation is carried out on the characters to be desensitized by adopting a preset character desensitization rule.
In one embodiment, calculating the user similarity and the course similarity according to the historical course selection data of the user comprises: calculating a user similarity matrix and a course similarity matrix according to historical course selection data; calculating the user similarity according to the user similarity matrix; and calculating the course similarity according to the course similarity matrix.
In one embodiment, calculating a first interest degree of the user in the courses to be recommended according to the user similarity includes calculating a first interest degree of the user in the courses to be recommended by using a first interest degree formula, where the first interest degree is:
wherein p (u, i) represents the interest degree of the user u in the course i, S (u, v) is a set of K users with the interest close to the interest of the user u, N (i) is a set of users with past behaviors on the course i, then the two sets take the intersection, WuvIs the similarity of user u and user v, rviRepresenting the interest level of user v in lesson i.
In one embodiment, calculating a second interest degree of the user in the to-be-recommended course according to the course similarity includes calculating a second interest degree of the user in the to-be-recommended course by using a second interest degree formula, where the second interest degree is:
where p (u, j) represents the level of interest of user u in class j, S (j, K) represents a set of K classes similar to the j class, and N (u) represents the class liked by user uSet of programs, WjiRepresenting the degree of similarity between courses, ruiRepresenting the interest of user u in lesson i.
In one embodiment, a storage medium is provided that stores computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of: calculating user similarity and course similarity according to historical course selection data of the user; calculating a first interest degree of the user to the recommended courses according to the similarity of the user, and taking a first preset number of courses with higher first interest degree as a first recommended course set; calculating a second interest degree of the user to the recommended courses according to the similarity of the courses, and taking a second preset number of courses with higher second interest degree as a second recommended course set; and pushing the first recommended course set and the second recommended course set to the user together.
In one embodiment, pushing the first set of recommended courses and the second set of recommended courses together to the user includes: and all the courses in the first recommended course set and the second recommended course set are subjected to priority ranking, and then the courses are pushed to the user together according to the sequence of the priorities from high to low.
In one embodiment, before calculating the user similarity and the course similarity according to the historical course selection data of the user, the method further includes: identifying sensitive information of a user when historical course selection data of the user is acquired, wherein the sensitive information comprises character sensitive information and image sensitive information; desensitizing sensitive information.
In one embodiment, desensitizing sensitive information includes: extracting image characteristics of an image to be desensitized in the sensitive information; matching the mapping table of the template image with the image characteristics of the image to be desensitized, and determining the position of the area to be desensitized of the image to be desensitized; desensitizing the region to be desensitized of the image to be desensitized; extracting characters to be desensitized in sensitive information; desensitization operation is carried out on the characters to be desensitized by adopting a preset character desensitization rule.
In one embodiment, calculating the user similarity and the course similarity according to the historical course selection data of the user comprises: calculating a user similarity matrix and a course similarity matrix according to historical course selection data; calculating the user similarity according to the user similarity matrix; and calculating the course similarity according to the course similarity matrix.
In one embodiment, calculating a first interest degree of the user in the courses to be recommended according to the user similarity includes calculating a first interest degree of the user in the courses to be recommended by using a first interest degree formula, where the first interest degree is:
wherein p (u, i) represents the interest degree of the user u in the course i, S (u, v) is a set of K users with the interest close to the interest of the user u, N (i) is a set of users with past behaviors on the course i, then the two sets take the intersection, WuvIs the similarity of user u and user v, rviRepresenting the interest level of user v in lesson i.
In one embodiment, calculating a second interest degree of the user in the to-be-recommended course according to the course similarity includes calculating a second interest degree of the user in the to-be-recommended course by using a second interest degree formula, where the second interest degree is:
wherein p (u, j) represents the interest level of the user u in the course j, S (j, K) represents a set of K courses similar to the course j, N (u) represents a set of courses liked by the user u, WjiRepresenting the degree of similarity between courses, ruiRepresenting the interest of user u in lesson i.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the computer program is executed. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above examples only show some embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A course recommendation method, comprising:
calculating user similarity and course similarity according to historical course selection data of the user;
calculating a first interest degree of the user to the recommended courses according to the user similarity, and taking a first preset number of courses with higher first interest degree as a first recommended course set;
calculating a second interest degree of the user to the recommended courses according to the course similarity, and taking a second preset number of courses with higher second interest degree as a second recommended course set;
and pushing the first recommended course set and the second recommended course set to a user together.
2. The method of claim 1, wherein pushing the first set of recommended courses and the second set of recommended courses together to a user comprises:
and performing priority ranking on all the courses in the first recommended course set and the second recommended course set, and then pushing the courses to the user together according to the sequence of the priorities from high to low.
3. The method of claim 1, wherein before calculating the user similarity and the course similarity according to the user's historical course selection data, further comprising:
identifying sensitive information of a user when historical course selection data of the user is acquired, wherein the sensitive information comprises character sensitive information and image sensitive information;
and desensitizing the sensitive information.
4. The method of claim 3, wherein desensitizing the sensitive information comprises:
extracting image characteristics of an image to be desensitized in the sensitive information;
matching a mapping table of a template image with the image characteristics of the image to be desensitized, and determining the position of the area to be desensitized of the image to be desensitized;
desensitizing the region to be desensitized of the image to be desensitized;
extracting characters to be desensitized in the sensitive information;
and carrying out desensitization operation on the characters to be desensitized by adopting a preset character desensitization rule.
5. The method of claim 1, wherein calculating the user similarity and the course similarity according to the user's historical course selection data comprises:
calculating a user similarity matrix and a course similarity matrix according to the historical course selection data;
calculating the user similarity according to the user similarity matrix;
and calculating the course similarity according to the course similarity matrix.
6. The method as claimed in claim 1, wherein calculating the first interest level of the user in the to-be-recommended course according to the user similarity comprises calculating the first interest level of the user in the to-be-recommended course by using a first interest level formula, where the first interest level is:
wherein p (u, i) represents the interest degree of the user u in the course i, S (u, v) is a set of K users with the interest close to the interest of the user u, N (i) is a set of users with past behaviors on the course i, then the two sets take the intersection, WuvIs the similarity of user u and user v, rviRepresenting the interest level of user v in lesson i.
7. The method as claimed in claim 1, wherein calculating a second interest level of the user in the to-be-recommended course according to the course similarity comprises calculating a second interest level of the user in the to-be-recommended course by using a second interest level formula, where the second interest level is:
wherein p (u, j) represents the interest level of the user u in the course j, S (j, K) represents a set of K courses similar to the course j, N (u) represents a set of courses liked by the user u, WjiRepresenting the degree of similarity between courses, ruiRepresenting the interest of user u in lesson i.
8. A course recommending apparatus, comprising:
the first calculation module is used for calculating the similarity of the users and the similarity of courses according to historical course selection data of the users;
the second calculation module is used for calculating a first interest degree of the user to the recommended courses according to the user similarity, and taking a first preset number of courses with higher first interest degree as a first recommended course set;
the third calculation module is used for calculating a second interest degree of the user to the recommended courses according to the course similarity, and taking a second preset number of courses with higher second interest degree as a second recommended course set;
and the recommending module is used for pushing the first recommended course set and the second recommended course set to a user together.
9. A computer device comprising a memory and a processor, the memory having stored therein computer readable instructions which, when executed by the processor, cause the processor to perform the steps of the course recommendation method of any of claims 1 to 7.
10. A storage medium having stored thereon computer-readable instructions which, when executed by one or more processors, cause the one or more processors to perform the steps of the course recommendation method as claimed in any one of claims 1 to 7.
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