CN112434223A - Information recommendation method and device - Google Patents
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Abstract
The embodiment of the application provides an information recommendation method and device, and relates to the technical field of data processing, wherein the information recommendation method comprises the following steps: when an account login instruction is detected, acquiring a target login account included in the account login instruction; acquiring historical operation records and user learning information corresponding to a target login account; acquiring a first recommended learning package according to the historical operation record, and acquiring a second recommended learning package according to the user learning information; and generating recommendation information according to the first recommendation learning package and the second recommendation learning package and outputting the recommendation information. Therefore, by the implementation of the implementation mode, the learning condition and the interested content of the user can be considered when recommending the recommendation information, so that the recommendation information quality is improved, and the requirements of the user are effectively matched.
Description
Technical Field
The application relates to the technical field of data processing, in particular to an information recommendation method and device.
Background
With the popularization and the high-speed development of the internet, the internet becomes more and more close to the life of people, and students can learn in a network course learning mode. In the online education field, education institutions often push recommended information of course learning to users, and expect that the users take benefits and then perform consumption behaviors such as course purchasing and the like, so that course consumption is promoted. The existing information recommendation method generally determines the content of interest of a user according to browsing records of the user and then pushes corresponding general recommendation information. However, in practice, it is found that in the existing information recommendation method for providing a course preference, when different users browse the same course content, the recommended information is the same, and the information recommendation method cannot be performed by simultaneously being compatible with the content of interest of the users and the learning conditions of the different users, so that the recommended information quality is low, and the requirements of the different users cannot be effectively matched.
Disclosure of Invention
The purpose of the application is to provide an information recommendation method and device, which can give consideration to the learning condition and the interested content of a user when recommending recommendation information, and further are beneficial to improving the quality of the recommendation information and effectively matching the requirements of the user.
A first aspect of an embodiment of the present application provides an information recommendation method, including:
when an account login instruction is detected, acquiring a target login account included in the account login instruction;
acquiring historical operation records and user learning information corresponding to the target login account;
acquiring a first recommended learning package according to the historical operation record, and acquiring a second recommended learning package according to the user learning information;
and generating recommendation information according to the first recommended learning package and the second recommended learning package and outputting the recommendation information.
In the implementation process, the method can preferentially judge whether an account login instruction is detected, and when the account login instruction is detected, a target login account included in the account login instruction is obtained; then, acquiring a historical operation record and user learning information corresponding to the target login account; acquiring a first recommended learning package according to the historical operation record, and acquiring a second recommended learning package according to the user learning information; and finally, generating recommendation information according to the first recommendation learning package and the second recommendation learning package and outputting the recommendation information. Therefore, by the implementation of the implementation mode, when the user login is detected, the learning package which the user may need to learn is determined according to the user operation record and the learning information, and the learning package is packaged, so that targeted preferential recommendation is completed.
Further, after the obtaining of the target login account included in the account login instruction, the method further includes:
judging whether the target login account is the account for the first login;
and if not, executing the step of acquiring the historical operation record and the user learning information corresponding to the target login account.
In the implementation process, after the target login account included in the account login instruction is obtained, whether the target login account is the account for the first login can be judged; and when the target login account is not the account for the first login, acquiring the historical operation record and the user learning information corresponding to the target login account. Therefore, by implementing the implementation mode, the selection of the subsequent operation can be carried out according to whether the user logs in for the first time, so that the recognition degree of the user is improved, and the effectiveness of the recommendation process is further improved.
Further, the method further comprises:
when the target login account is judged to be the first login account, acquiring user basic information input by a user in the target login account;
determining grade information and learning information of the user according to the user basic information;
acquiring a third recommended learning package matched with the grade information;
determining weak subject information of the user according to the learning information;
acquiring a fourth recommended learning packet according to the weak subject information;
and generating recommendation information according to the third recommended learning package and the fourth recommended learning package, and outputting the recommendation information.
In the implementation process, the method can also acquire the user basic information input by the user in the target login account when the target login account is judged to be the account for the first login; determining grade information and learning information of the user according to the basic information of the user; acquiring a third recommended learning package matched with the grade information; determining weak subject information of the user according to the learning information; acquiring a fourth recommended learning packet according to the weak subject information; and finally, generating recommendation information according to the third recommended learning packet and the fourth recommended learning packet and outputting the recommendation information. Therefore, by implementing the implementation mode, the grade information and the learning information can be determined for the new user, and the recommended learning package is determined according to the two information, so that course recommendation for the new user is completed, and targeted preferential recommendation is realized.
Further, the obtaining a first recommended learning package according to the historical operating record includes:
determining high-frequency browsing information and finished course information according to the historical operation records;
acquiring an initial recommended learning package according to the high-frequency browsing information;
and filtering the initial recommended learning package according to the finished course information to obtain a first recommended learning package.
In the implementation process, the method can preferentially determine the high-frequency browsing information and the finished course information according to the historical operation record in the process of acquiring the first recommended learning package according to the historical operation record; then, acquiring an initial recommended learning package according to the high-frequency browsing information; and finally, filtering the initial recommended learning package according to the finished course information to obtain a first recommended learning package. Therefore, by implementing the implementation mode, the learning packets can be acquired step by step according to the high-frequency browsing information and the finished course information, so that the first recommended learning packet with high quality is obtained, and the recommendation quality of the learning packets can be improved.
Further, the obtaining a second recommended learning package according to the user learning information includes:
determining the current learning level of the user according to the user learning information;
predicting a target learning level of the user after completing learning of the first recommended learning package according to the current learning level;
and acquiring a promoted learning package corresponding to the first recommended learning package according to the target learning level, wherein the promoted learning package is a second recommended learning package.
In the implementation process, the method can determine the current learning level of the user according to the user learning information preferentially in the process of acquiring the second recommended learning package according to the first recommended learning package and the user learning information; then predicting the target learning level of the user after finishing the learning of the first recommended learning package according to the current learning level; and finally, acquiring a lifting learning package corresponding to the first recommended learning package according to the target learning level, wherein the lifting learning package is a second recommended learning package. Therefore, by the implementation of the implementation mode, the reasonable learning package recommendation can be performed according to the learning level of the user, so that the reasonable acquisition of the second recommended learning package is realized, and the acquisition reasonability of the second recommended learning package can be improved.
Further, the generating and outputting recommendation information according to the first recommended learning package and the second recommended learning package includes:
generating first recommendation information for the first recommended learning package;
generating second recommendation information for the second recommended learning package;
generating linkage recommendation information of the first recommended learning package and the second recommended learning package;
generating recommendation information according to the first recommendation information, the second recommendation information and the linkage recommendation information;
and outputting the recommendation information.
In the implementation process, in the process of generating and outputting recommendation information according to the first recommendation learning package and the second recommendation learning package, the method can preferentially generate the first recommendation information for the first recommendation learning package; generating second recommendation information for the second recommended learning package; then, linkage recommendation information of the first recommended learning package and the second recommended learning package is generated; generating recommendation information according to the first recommendation information, the second recommendation information and the linkage recommendation information; and finally, outputting recommendation information. Therefore, by implementing the implementation mode, the first recommendation information, the second recommendation information and the linkage recommendation information can be generated, and the set of the recommendation information is finally output, so that more preference choices are given to the user, and the richness of the pushed content is further improved.
A second aspect of the embodiments of the present application provides an information recommendation apparatus, including:
the device comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is used for acquiring a target login account included in an account login instruction when the account login instruction is detected;
the second acquisition unit is used for acquiring the historical operation record and the user learning information corresponding to the target login account;
the third acquisition unit is used for acquiring a first recommended learning package according to the historical operation record and acquiring a second recommended learning package according to the user learning information;
and the generating unit is used for generating and outputting recommendation information according to the first recommendation learning package and the second recommendation learning package.
In the implementation process, the information recommendation device can detect a river and lake login instruction through the first acquisition unit, and acquire a target login account included in the account login instruction when the account login instruction is detected; acquiring a historical operation record and user learning information corresponding to the target login account by a second acquisition unit; acquiring a first recommended learning package according to the historical operation record through a third acquisition unit, and acquiring a second recommended learning package according to the user learning information; and generating and outputting recommendation information by the generating unit according to the first recommendation learning package and the second recommendation learning package. Therefore, by the implementation of the implementation mode, when the user login is detected, the learning package which the user may need to learn is determined according to the user operation record and the learning information, and the learning package is packaged, so that targeted preferential recommendation is completed.
Further, the information recommendation apparatus further includes:
the judging unit is used for judging whether the target login account is a first-time login account or not after the target login account included in the account login instruction is obtained; and when the target login account is judged not to be the account for the first login, triggering the first acquisition unit to acquire the historical operation record and the user learning information corresponding to the target login account.
In the implementation process, the information recommendation device can also judge whether the target login account is the account for the first login through the judgment unit; and triggering the first acquisition unit to acquire the historical operation record and the user learning information corresponding to the target login account when the target login account is not the account for the first login. Therefore, by implementing the implementation mode, the selection of the subsequent operation can be carried out according to whether the user logs in for the first time, so that the recognition degree of the user is improved, and the effectiveness of the recommendation process is further improved.
A third aspect of the embodiments of the present application provides an electronic device, including a memory and a processor, where the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the information recommendation method according to any one of the first aspect of the embodiments of the present application.
A fourth aspect of the embodiments of the present application provides a computer-readable storage medium, which stores computer program instructions, and when the computer program instructions are read and executed by a processor, the computer program instructions perform the information recommendation method according to any one of the first aspect of the embodiments of the present application.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic flowchart of an information recommendation method according to an embodiment of the present application;
fig. 2 is a schematic flowchart of another information recommendation method according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an information recommendation device according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of another information recommendation device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Referring to fig. 1, fig. 1 is a schematic flow chart of an information recommendation method according to an embodiment of the present application. The method is applied to the scene of course preferential recommendation of online education, and particularly can be applied to the course of course preferential recommendation of courses to users by a course platform when the users visit the course platform. The information recommendation method comprises the following steps:
s101, when the account login instruction is detected, a target login account included in the account login instruction is obtained.
In this embodiment, the account login instruction may be a login instruction issued or generated when the user logs in the system. Specifically, the login can be performed in any manner such as APP, applet, and web page, which is not limited in any way.
In this embodiment, the account login instruction may include account information (such as account number information) of the target login account.
S102, obtaining historical operation records and user learning information corresponding to the target login account.
In this embodiment, the historical operation records may include historical purchase records and historical learning records of the account (corresponding to the user).
Since each user may correspond to multiple accounts, the user learning information or the historical operation records in this embodiment are for one account, not for one user, and are not described in detail herein.
In this embodiment, the user learning information may include course information already learned by the user or learned knowledge point information, and attributes of the user, for example, the user has learned a second-grade primary school chinese course, has learned ancient poetry and a retrieval technique, and the user is a second-grade Beijing student.
S103, acquiring a first recommended learning package according to the historical operation record, and acquiring a second recommended learning package according to the user learning information.
And S104, generating recommendation information according to the first recommendation learning package and the second recommendation learning package, and outputting the recommendation information.
In this embodiment, the recommendation information is generated based on two learning packages, i.e., the first recommended learning package and the second recommended learning package.
In the embodiment of the present application, the recommendation information may be preferential information for purchasing a learning package, and the embodiment of the present application is not limited thereto.
For example, when the user generates a course browsing record or a purchasing record in the system, the background data changes with the operation. Based on the point characteristics, the method can preferentially screen the subjects as dimensions to filter out the subjects which are registered by the user, then analyzes the subjects which are frequently browsed by the user and background data, and generates a corresponding learning package (namely a first recommended learning package) to recommend; then, a large amount of coupons are issued for the learning packages, for example, designated coupons (such as large amount of Chinese-English union report coupons) are issued for the user, so that the purpose of stimulating the user to register multiple subjects is achieved.
In the above example process, the method may further perform another screening with the difficulty as a dimension, predict a level that the quarterly course should reach after completion according to the current level of the user, and determine the second recommended learning package according to the level. And then, issuing a designated coupon for the second recommended learning package, thereby realizing the purpose of stimulating the user to register the courses with the same subject and multiple depths.
In the embodiment of the present application, the execution subject of the method may be a computing device such as a computer and a server, and is not limited in this embodiment.
In this embodiment, an execution subject of the method may also be an intelligent device such as a smart phone and a tablet computer, which is not limited in this embodiment.
It can be seen that, by implementing the information recommendation method described in fig. 1, it can be preferentially determined whether an account login instruction is detected, and when the account login instruction is detected, a target login account included in the account login instruction is obtained; then, acquiring a historical operation record and user learning information corresponding to the target login account; acquiring a first recommended learning package according to the historical operation record, and acquiring a second recommended learning package according to the user learning information; and finally, generating recommendation information according to the first recommendation learning package and the second recommendation learning package and outputting the recommendation information. Therefore, by the implementation of the implementation mode, when the user login is detected, the learning package which the user may need to learn is determined according to the user operation record and the learning information, and the learning package is packaged, so that targeted preferential recommendation is completed.
Referring to fig. 2, fig. 2 is a schematic flow chart of another information recommendation method according to an embodiment of the present application. As shown in fig. 2, the information recommendation method includes:
s201, when the account login instruction is detected, acquiring a target login account included in the account login instruction.
S202, judging whether the target login account is the account for the first login, and if so, ending the process; if not, steps S203 to S214 are executed.
In this embodiment, the account logged in for the first time does not have a history operation record, and the account not logged in for the first time does not have a history operation record.
As an alternative embodiment, the step of determining whether the target login account is the first login account may include:
detecting whether the target login account has a frequent visitor tag for indicating non-first login;
if so, indicating that the user does not log in for the first time; if not, the user logs in for the first time.
In some possible embodiments, the account ID or identification of the user login may also be queried from the user database to determine whether the user is a new user for manual login.
As an optional implementation manner, when the target login account is an account that is logged in for the first time, the method may further include:
acquiring user basic information input by a user in a target login account;
determining grade information and learning information of the user according to the basic information of the user;
acquiring a third recommended learning package matched with the grade information;
determining weak subject information of the user according to the learning information;
acquiring a fourth recommended learning packet according to the weak subject information;
and generating recommendation information according to the third recommended learning packet and the fourth recommended learning packet and outputting the recommendation information.
For example, the user may preferentially fill in his/her actual personal information (such as the user's grade, age, sex, and region), and may assess the learning level of the user through testing. The background can record the data so as to facilitate the system or the platform to carry out corresponding preferential issue according to the information.
In some possible embodiments, the fifth recommended learning package may be further obtained according to the subject in which the user is interested, so that the recommendation information is generated according to the third recommended learning package and the fifth recommended learning package and is output, which is not described herein.
It should be noted that, when generating recommendation information according to at least two different recommended learning packages, an intersection or a union of at least two different recommended learning packages, or an intersection or a union of one or some recommended learning packages of at least two different recommended learning packages may be taken, which is not limited in any way.
In this embodiment, the method can preferentially perform corresponding matching according to the grade registered by the user and the mass data in the background, and screen out grade courses (i.e., third recommended learning packages) conforming to the age of the user; then, corresponding analysis is carried out according to the learning level of the user, and subjects with relatively weak learning level of the user (namely, a fourth recommended learning package) are screened out; the method can issue the preferential release according to the third recommended learning package and the fourth recommended learning package, so that the effect of assisting the user in registering the corresponding course is achieved.
As an optional implementation, the method may further include:
and matching the maximum preferential item corresponding to the recommendation information when the user payment information is detected.
S203, obtaining the historical operation record and the user learning information corresponding to the target login account.
In this embodiment, the historical operation records may include historical purchase records and historical learning records of the user.
In this embodiment, the user learning information may include course information learned by the user or learned knowledge point information, and the like.
And S204, determining high-frequency browsing information and finished course information according to the historical operation records.
In this embodiment, the high-frequency browsing information is information frequently browsed by the user. Specifically, a threshold value may be set, and information that the browsing number is higher than the threshold value may be used as frequently-browsed information.
And S205, acquiring an initial recommended learning package according to the high-frequency browsing information.
In this embodiment, the initial recommended learning package may be understood as course information in which the user is interested.
And S206, filtering the initial recommended learning package according to the finished course information to obtain a first recommended learning package.
In this embodiment, the first recommended learning package is a future learning package in which the course has been already learned is removed from the initial recommended learning package.
And S207, determining the current learning level of the user according to the user learning information.
In the present embodiment, the current learning level of the user is used to represent the current mastered knowledge and the not-yet-mastered knowledge of the user.
In some possible embodiments, the test questions may also be recommended to the user first, and the current learning level of the user is determined according to the test result of the test questions, which is not described herein again.
And S208, predicting the target learning level of the user after the learning of the first recommended learning package is finished according to the current learning level.
In this embodiment, the target learning level is used to indicate a user learning level after the user learns a certain knowledge.
S209, acquiring a lifting learning package corresponding to the first recommended learning package according to the target learning level, wherein the lifting learning package is a second recommended learning package.
In this embodiment, the second recommended learning package is used to represent the course content suitable for the user to learn to achieve the enhancement of the knowledge level.
S210, generating first recommendation information aiming at the first recommendation learning package.
And S211, generating second recommendation information aiming at the second recommendation learning packet.
And S212, generating linkage recommendation information of the first recommended learning package and the second recommended learning package.
And S213, generating recommendation information according to the first recommendation information, the second recommendation information and the linkage recommendation information.
In this embodiment, the recommendation information may be understood as an information set of the first recommendation information, the second recommendation information, and the linkage recommendation information.
And S214, outputting recommendation information.
As an optional implementation manner, the step of generating linkage recommendation information of the first recommended learning package and the second recommended learning package includes:
and combining the first recommended course and the second recommended course to obtain linkage recommendation information.
By implementing the implementation mode, the linkage recommendation information can be further determined to be the final recommendation information so as to be convenient for the user to select.
In this embodiment, the method may preferentially generate the first recommendation information, and then generate the linkage recommendation information according to the first recommendation information, so that the final recommendation information includes the first recommendation information and the linkage recommendation information, thereby pushing the first recommendation information and facilitating selection by the user.
It can be seen that, by implementing the information recommendation method described in fig. 2, when the user login is detected, the learning package that the user may need to learn is determined according to the user operation record and the learning information, and the learning package is packaged, so that targeted preferential recommendation is completed.
Please refer to fig. 3, fig. 3 is a schematic structural diagram of an information recommendation device according to an embodiment of the present application. As shown in fig. 3, the information recommendation apparatus includes:
a first obtaining unit 310, configured to, when an account login instruction is detected, obtain a target login account included in the account login instruction;
a second obtaining unit 320, configured to obtain a history operation record and user learning information corresponding to the target login account;
a third obtaining unit 330, configured to obtain the first recommended learning package according to the historical operation record, and obtain the second recommended learning package according to the user learning information;
and a generating unit 340, configured to generate and output recommendation information according to the first recommended learning package and the second recommended learning package.
It can be seen that, with the information recommendation device described in fig. 3, when the user login is detected, the learning package that the user may need to learn is determined according to the user operation record and the learning information, and the learning package is packaged, so that targeted preferential recommendation is completed.
Referring to fig. 4, fig. 4 is a schematic structural diagram of an information recommendation device according to an embodiment of the present application. The information recommendation apparatus shown in fig. 4 is optimized by the information recommendation apparatus shown in fig. 3. As shown in fig. 4, the information recommendation apparatus further includes:
the determining unit 350 is configured to determine whether the target login account is an account that is logged in for the first time after the target login account included in the account login instruction is acquired; when it is determined that the target login account is not the first login account, the second obtaining unit 320 is triggered to obtain the historical operation record and the user learning information corresponding to the target login account.
As an optional implementation manner, the first obtaining unit 310 is further configured to obtain, when the target login account is an account that is logged in for the first time, user basic information input by the user in the target login account;
the second obtaining unit 320 is further configured to determine grade information and learning information of the user according to the user basic information;
a third obtaining unit 330, configured to obtain a third recommended learning package matched with the grade information;
the third obtaining unit 330 is further configured to determine weak subject information of the user according to the learning information;
the third obtaining unit 330 is further configured to obtain a fourth recommended learning package according to the weak subject information;
the generating unit 340 is further configured to generate and output recommendation information according to the third recommended learning package and the fourth recommended learning package.
As an optional implementation, the third obtaining unit 330 includes:
a determination subunit 331 configured to determine high-frequency browsing information and completed course information from the historical operation record;
an obtaining subunit 332, configured to obtain an initial recommended learning package according to the high-frequency browsing information;
and a filtering subunit 333, configured to perform filtering processing on the initial recommended learning package according to the completed course information, so as to obtain a first recommended learning package.
As an optional implementation manner, the third obtaining unit 330 further includes:
a determining subunit 331 configured to determine a current learning level of the user from the user learning information;
a prediction subunit 334 configured to predict, according to the current learning level, a target learning level of the user after completion of learning of the first recommended learning package;
the obtaining subunit 332 is configured to obtain, according to the target learning level, a boosted learning packet corresponding to the first recommended learning packet, where the boosted learning packet is a second recommended learning packet.
As an optional implementation, the generating unit 340 includes:
a generation subunit 341 configured to generate first recommendation information for the first recommended learning package;
a generation subunit 341, configured to generate second recommendation information for the second recommended learning package;
the generating subunit 341 is further configured to generate linkage recommendation information of the first recommended learning package and the second recommended learning package;
the generating subunit 341 is further configured to generate recommendation information according to the first recommendation information, the second recommendation information, and the linkage recommendation information;
the output subunit 342 is configured to output recommendation information.
In the embodiment of the present application, for explanation of the information recommendation apparatus, reference may be made to the description in embodiment 1 or embodiment 2, and details are not repeated in this embodiment.
It can be seen that, with the information recommendation device described in fig. 4, when the user login is detected, the learning package that the user may need to learn is determined according to the user operation record and the learning information, and the learning package is packaged, so that targeted preferential recommendation is completed.
The embodiment of the application provides an electronic device, which comprises a memory and a processor, wherein the memory is used for storing a computer program, and the processor runs the computer program to enable the electronic device to execute an information recommendation method in the embodiment of the application.
The embodiment of the present application provides a computer-readable storage medium, which stores computer program instructions, and when the computer program instructions are read and executed by a processor, the computer program instructions execute the information recommendation method in the embodiment of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. An information recommendation method, comprising:
when an account login instruction is detected, acquiring a target login account included in the account login instruction;
acquiring historical operation records and user learning information corresponding to the target login account;
acquiring a first recommended learning package according to the historical operation record, and acquiring a second recommended learning package according to the user learning information;
and generating recommendation information according to the first recommended learning package and the second recommended learning package and outputting the recommendation information.
2. The information recommendation method according to claim 1, wherein after the obtaining of the target login account included in the account login instruction, the method further comprises:
judging whether the target login account is the account for the first login;
and if not, executing the step of acquiring the historical operation record and the user learning information corresponding to the target login account.
3. The information recommendation method of claim 1, further comprising:
when the target login account is judged to be the first login account, acquiring user basic information input by a user in the target login account;
determining grade information and learning information of the user according to the user basic information;
acquiring a third recommended learning package matched with the grade information;
determining weak subject information of the user according to the learning information;
acquiring a fourth recommended learning packet according to the weak subject information;
and generating recommendation information according to the third recommended learning package and the fourth recommended learning package, and outputting the recommendation information.
4. The information recommendation method according to claim 1, wherein said obtaining a first recommended learning package according to the historical operation record comprises:
determining high-frequency browsing information and finished course information according to the historical operation records;
acquiring an initial recommended learning package according to the high-frequency browsing information;
and filtering the initial recommended learning package according to the finished course information to obtain a first recommended learning package.
5. The information recommendation method according to claim 1, wherein said obtaining a second recommended learning package according to the user learning information comprises:
determining the current learning level of the user according to the user learning information;
predicting a target learning level of the user after completing learning of the first recommended learning package according to the current learning level;
and acquiring a promoted learning package corresponding to the first recommended learning package according to the target learning level, wherein the promoted learning package is a second recommended learning package.
6. The information recommendation method according to claim 1, wherein generating and outputting recommendation information from the first recommended learning package and the second recommended learning package includes:
generating first recommendation information for the first recommended learning package;
generating second recommendation information for the second recommended learning package;
generating linkage recommendation information of the first recommended learning package and the second recommended learning package;
generating recommendation information according to the first recommendation information, the second recommendation information and the linkage recommendation information;
and outputting the recommendation information.
7. An information recommendation apparatus characterized by comprising:
the device comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is used for acquiring a target login account included in an account login instruction when the account login instruction is detected;
the second acquisition unit is used for acquiring the historical operation record and the user learning information corresponding to the target login account;
the third acquisition unit is used for acquiring a first recommended learning package according to the historical operation record and acquiring a second recommended learning package according to the user learning information;
and the generating unit is used for generating and outputting recommendation information according to the first recommendation learning package and the second recommendation learning package.
8. The information recommendation device according to claim 7, further comprising:
the judging unit is used for judging whether the target login account is a first-time login account or not after the target login account included in the account login instruction is obtained; and when the target login account is judged not to be the account for the first login, triggering the first acquisition unit to acquire the historical operation record and the user learning information corresponding to the target login account.
9. An electronic device, characterized in that the electronic device comprises a memory for storing a computer program and a processor for executing the computer program to cause the electronic device to perform the information recommendation method of any one of claims 1 to 6.
10. A readable storage medium, in which computer program instructions are stored, which, when read and executed by a processor, perform the information recommendation method of any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011410608.3A CN112434223A (en) | 2020-12-04 | 2020-12-04 | Information recommendation method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011410608.3A CN112434223A (en) | 2020-12-04 | 2020-12-04 | Information recommendation method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112434223A true CN112434223A (en) | 2021-03-02 |
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Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011410608.3A Withdrawn CN112434223A (en) | 2020-12-04 | 2020-12-04 | Information recommendation method and device |
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| Country | Link |
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| CN (1) | CN112434223A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114202237A (en) * | 2021-12-23 | 2022-03-18 | 泰康保险集团股份有限公司 | Course recommendation method, device, equipment and medium |
| CN114661195A (en) * | 2022-04-18 | 2022-06-24 | 北京高途云集教育科技有限公司 | Method and device for creating network course, computer equipment and storage medium |
-
2020
- 2020-12-04 CN CN202011410608.3A patent/CN112434223A/en not_active Withdrawn
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114202237A (en) * | 2021-12-23 | 2022-03-18 | 泰康保险集团股份有限公司 | Course recommendation method, device, equipment and medium |
| CN114661195A (en) * | 2022-04-18 | 2022-06-24 | 北京高途云集教育科技有限公司 | Method and device for creating network course, computer equipment and storage medium |
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