CN110825960A - Learning content recommendation method and device - Google Patents
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Abstract
The embodiment of the disclosure provides a learning content recommendation method and device, which can be applied to a national staff academic platform, and the method comprises the following steps: acquiring personal information and application characteristic information of a login user; judging whether a recommendation rule of the login user exists in the system or not according to the personal information of the login user; if the personal information and the application characteristic information of the login user exist, acquiring the existing recommendation rule, and if the personal information and the application characteristic information do not exist, generating the recommendation rule of the login user according to the personal information and the application characteristic information of the login user; calculating a content recommendation heat value according to the recommendation rule; and pushing learning content to the login user according to the content recommendation heat value. In this way, more intelligent and personalized learning content push can be realized, so that the learning enthusiasm of the user and the practicability of the push content are improved.
Description
Technical Field
The invention relates to the technical field of information, in particular to a learning content recommendation method and device.
Background
With the development and popularization of internet technology, online learning becomes a common learning mode, and online learning platforms of various contents emerge. For example, in order to promote the state staff to respect, learn and use, a state staff law learning platform is on line, and the platform is used for pushing basic knowledge of laws and regulations to the state staff, so that the capability of the state staff for solving problems by applying law and law is improved. The learning content recommendation algorithm on the existing platform is mainly used for comprehensively evaluating the popularity of a certain learning content on the platform based on the release time, the browsing amount, the evaluation comment and other conditions of the content, and then pushing the content to a user in the platform according to the popularity.
However, the information pushed to the user by adopting the recommendation algorithm is only mechanical calculation recommendation learning content, the recommendation learning content displayed to the user is basically consistent, and the learning requirements of different users cannot be met; moreover, the learning content is too concentrated, and the purpose of knowledge popularization cannot be achieved.
Disclosure of Invention
According to a first aspect of the present disclosure, a learning content recommendation method is provided. The method comprises the following steps:
acquiring personal information and application characteristic information of a login user;
judging whether a recommendation rule of the login user exists according to the personal information of the login user; if the personal information and the application characteristic information of the login user exist, acquiring the existing recommendation rule, and if the personal information and the application characteristic information do not exist, generating the recommendation rule of the login user according to the personal information and the application characteristic information of the login user;
calculating a content recommendation heat value according to the recommendation rule;
and pushing learning content to the login user according to the content recommendation heat value.
Further, the method is applied to a national staff academic platform, wherein:
the application characteristic information comprises user news browsing information, query information and/or geographical location information;
the personal information includes unique identification information.
Further, the obtaining of the existing recommendation rule includes:
and acquiring the recommendation rule of the user according to the unique identification information of the login user.
Further, the air conditioner is provided with a fan,
the recommendation rule is composed of an algorithm rule matrix;
the algorithm rule matrix comprises recommended item category information, information about whether to participate in calculation and/or recommended weight information.
Further, the item category information includes:
post information, law enforcement and service event label information, user preference information, and/or geographic location information.
Further, the calculating the content recommendation heat value according to the recommendation rule includes:
acquiring learning content;
determining unfinished learning content according to personal information of a login user;
and calculating to obtain the learning content recommendation heat value according to the recommendation rule associated with the uncompleted learning content.
Further, the learning content recommendation heat value is calculated using the following formula:
wherein H is a learning content recommendation heat value;
c is whether to participate in the calculated value;
Tkis an item category;
(Ln) is TkSpecific information of the item category;
m is TkNumber of transaction categories;
if participating in the calculation, the C is taken as 1; if not, taking 0 as C, and taking part in calculation under the default condition;
Qkis the item category atThe calculated weights in the learning content recommendation.
Further, the pushing learning content to the login user according to the content recommendation heat value includes:
arranging the unfinished learning contents from large to small according to the content recommendation heat value;
and displaying or sending information to the user according to the arrangement sequence.
Further, after pushing learning content to the login user according to the content recommendation heat value, the method further comprises:
analyzing the push information, if the push information is adopted, increasing the calculation weight Q of the item category according to a preset valuekAnd meanwhile, updating the recommendation rule of the user.
According to a second aspect of the present disclosure, there is provided a learning content recommendation apparatus. The device includes:
the acquisition module is used for acquiring personal information and application characteristic information of a login user;
the judging module is used for judging whether the recommendation rule of the login user exists according to the personal information of the login user; if the personal information and the application characteristic information of the login user exist, acquiring the existing recommendation rule, and if the personal information and the application characteristic information do not exist, generating the recommendation rule of the login user according to the personal information and the application characteristic information of the login user;
the calculation module is used for calculating a content recommendation heat value according to the recommendation rule;
and the pushing module is used for pushing learning content to the login user according to the content recommendation heat value.
According to the method and the device, the information of the login user is analyzed to obtain the corresponding recommendation rule, the learning content recommendation heat value is calculated according to the recommendation rule, and the learning content is pushed to the user according to the learning content recommendation heat value, so that the pushing of the learning content is more intelligent and personalized, and the learning enthusiasm of the user and the practicability of the pushed content are improved; furthermore, the original law content recommendation algorithm of the national staff law learning platform is improved, the more intelligent and personalized law learning knowledge content push is realized by acquiring the relevant information such as law enforcement and service item label information and duties related to the organization of the national staff and combining the application characteristics on the mobile terminal application, thereby being beneficial to the fulfillment of the duties of the national institutes and promoting the deep development of the national institutes and law work. .
It should be understood that the statements herein reciting aspects are not intended to limit the critical or essential features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
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The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, like or similar reference characters designate like or similar elements, and wherein:
fig. 1 shows a flow diagram of a learning content recommendation method according to an embodiment of the present disclosure;
fig. 2 shows a block diagram of the structure of a learning content recommendation apparatus according to an embodiment of the present disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are some, but not all embodiments of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
Fig. 1 shows a flowchart of a learning content recommendation method 100 according to embodiment 1 of the present disclosure, as shown in fig. 1, including:
s110, acquiring personal information and application characteristic information of a login user;
s120, judging whether a recommendation rule of the login user exists according to the personal information of the login user; if the personal information and the application characteristic information of the login user exist, acquiring the existing recommendation rule, and if the personal information and the application characteristic information do not exist, generating the recommendation rule of the login user according to the personal information and the application characteristic information of the login user;
s130, calculating a content recommendation heat value according to the recommendation rule;
and S140, pushing learning content to the login user according to the content recommendation heat value.
Optionally, the learning content recommendation method 100 is applied to a national staff academic platform, where:
in step S110, acquiring personal information and application feature information of a login user through a mobile application, where the personal information includes unique identification information (user name) and the like; the application characteristic information comprises user news browsing information, query information and/or geographical position information and the like;
further, the acquired information may be composed of: loginnid, personal job information, a list of law enforcement and service event label information related to information of the organization to which the user belongs, a list of personal preference labels, geographic location information, and the like.
In step S120, it is determined whether the recommendation rule associated with the user is stored or generated in the platform by the personal information of the logged-in user. If yes, directly calling the existing recommendation rule according to the unique identification information of the login user, and if not, generating a new recommendation rule according to the personal information and the application characteristic information of the login user;
further, the recommendation rule is composed of a recommendation algorithm rule matrix, and the algorithm rule matrix includes recommendation item category information, information about whether to participate in calculation and/or recommendation weight information, and the like;
further, the event category information may be divided into categories of post information (t1), law enforcement and service event tags (t2), user preferences (t3), and geographic location (t 4).
In step S130, the latest and hottest learning content in the platform is obtained, the content which is learned by the login user is eliminated according to the information of the login user, and then the content recommendation heat value is calculated according to the following formula;
wherein H is a learning content recommendation heat value;
c is whether to participate in the calculated value;
Tkis an item category;
(Ln) is TkSpecific information of the item category;
m is TkNumber of transaction categories;
if participating in the calculation, the C is taken as 1; if not, taking 0 as C, and taking part in calculation under the default condition;
Qkcalculating weights for the event categories in the learning content recommendation.
In step S140, the content recommendation heat values obtained in step S130 are arranged in descending order, and information is displayed or pushed to the user according to the arrangement order;
further, still include:
analyzing the push information, if the push information is adopted, increasing the calculation weight Q of the item category according to a preset valuekUpdating the recommendation rule of the user;
after recommending the recommended learning content to the user, the system follows up the pushed information, further judges whether the pushed information is adopted by the user, if so, the weight of the item category T (n) in the recommendation rule corresponding to the user is increased (for example, 0.1) according to a preset value, and meanwhile, the learning content recommendation algorithm rule matrix information of the user is updated;
further, when the user logs in again or refreshes the learning content, the heat value of each learning content is calculated according to the latest learning content recommendation algorithm rule matrix information, the heat values are arranged according to the descending order, and the learning content is pushed or displayed to the user according to the arrangement order.
It is noted that while for simplicity of explanation, the foregoing method embodiments have been described as a series of acts or combination of acts, it will be appreciated by those skilled in the art that the present disclosure is not limited by the order of acts, as some steps may, in accordance with the present disclosure, occur in other orders and concurrently. Further, those skilled in the art should also appreciate that the embodiments described in the specification are exemplary embodiments and that acts and modules referred to are not necessarily required by the disclosure.
The above is a description of embodiments of the method, and the embodiments of the apparatus are further described below.
Fig. 2 shows a block diagram of a learning content recommendation apparatus 200 according to embodiment 2 of the present disclosure. As shown in fig. 2, the apparatus 200 includes:
an obtaining module 210, configured to obtain personal information and application feature information of a login user;
the judging module 220 is configured to judge whether a recommendation rule of the login user exists in the system according to the personal information of the login user; if the personal information and the application characteristic information of the login user exist, acquiring the existing recommendation rule, and if the personal information and the application characteristic information do not exist, generating the recommendation rule of the login user according to the personal information and the application characteristic information of the login user;
a calculating module 230, configured to calculate a content recommendation heat value according to the recommendation rule;
a pushing module 240, configured to push learning content to the login user according to the content recommendation heat value.
Optionally, the device 200 is applied to a national staff academic platform.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the described module may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
According to still another embodiment of the present disclosure, there is provided a learning content recommendation method of a national staff academic platform, the method including the steps of;
a. when a national worker logs in the platform, acquiring personal information and application characteristic information of the national worker;
personal information and application characteristic information after national staff login are obtained through a mobile phone APP: { loginnId _1, UT1 (Korea), UT2 (justice; law; assistance), UT3 (greedy; twin; environmental protection), UT4 (Changsha) }, i.e. loginnId is loginnId _ 1; the country staff is responsible for the chief job in the unit; the study attributes concerned by the organization mainly relate to judicial, notarial and legal assistance; recently browsing news in marital, greedy, double-rule, and environmental related aspects; the geographical location information is sand.
b. Judging whether a recommendation rule exists according to the unique identification information in the personal information, if so, calling the recommendation rule, otherwise, generating the recommendation rule of the national staff according to the personal information and the application characteristic information;
if the national staff logs in the national staff academic platform before, the recommendation rule associated with the national staff is stored in the platform, and the recommendation rule associated with the national staff can be directly called through the unique identification information in the acquired personal information;
if the set or stored recommendation rule is not read, rule generation is carried out, the loginId information of the national staff is loginId _1, a learning recommendation rule related to the loginId _1 is obtained according to the loginId _1, and { loginId _1, a job t1 participates in calculation, 1.2; loginnid — 1, law enforcement and service event label t2, participation in recommendation calculation, 0.9 (recommendation weight information); loginnid — 1, personal preferences t3, participation in recommendation calculation, 1.1; loginnid _1, geographic location t4, not involved in the calculation, recommendation algorithm rule matrix of 0 (recommendation weight information) }.
c. Calculating a content recommendation heat value according to the recommendation rule;
assume that there are contents C1, C2, C3, C4, C5 in the platform. The C1-C5 may be the latest on-line content or recommended content obtained according to an existing algorithm. Firstly, learning contents of C1, C2, C3, C4 and C5 are obtained, and then contents C2 and C3 which are learned by the staff in the country are removed according to the login information of the staff in the country, so that the learning contents needing to be recommended to the staff in the country are listed as C1, C4 and C5. Obtaining rule tag information of each piece of learning content in the content list, and forming a two-dimensional tag data matrix of the learning content, namely [ { C1: t1{ L11}, t2{ L21, L22}, t3{ L31}, t4{ L41} }, { C4: t1{ L12}, t2{ L21, L23}, t3{ L33}, t4{ L42} }, { C5: t1{ L11}, t2{ L23, L25}, t3{ L35}, t4{ L41} } ] information, taking C4 learning content as an example, obtaining a two-dimensional tag data matrix L (n) of the learning content, wherein the L (n) information is: { C4: t1{ department grade; department }, t2{ notarization; judicial }, t3{ marital }, t4{ sand } }.
And substituting the obtained learning content two-dimensional label data matrix into the obtained recommendation algorithm rule, and performing matching calculation to obtain a content recommendation heat value.
Calculating the content recommendation heat value by taking learning content C4 as an example, where the heat value H is count [ ' department ' in ' department; department '] × 1 × 1+ count [' justice; notarization; legal assistance 'in' notarization; judicial '] × 1 × 1.2+ count [' greedy pollution; double gauge; environment-friendly ' in ' marriage ' × 1 × 1+ count [ ' Changsha ' in ' Changsha ' ] × 1 × 0.9 ═ 4.3. For example, the content popularity recommendation values of C1 and C5 may be calculated according to the same rule, and will not be described herein again.
d. And pushing learning content to the login user according to the content recommendation heat value.
Arranging the calculated content recommendation heat values of C1, C4 and C5 in a descending order, and pushing information to the staff in the country according to the arrangement order;
further, after the push information is adopted by the national staff, the weight of the item category T (n) in the corresponding recommendation rule is increased by 0.1, and the learning content recommendation algorithm rule matrix information of the national staff is updated; the "adoption" includes operations of browsing or collecting.
Further, when the country staff logs in again or refreshes the learning content, the heat value of each learning content is calculated according to the latest learning content recommendation algorithm rule matrix information, the heat values are arranged according to the descending order, and the learning content is pushed to the country staff according to the arrangement order.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a load programmable logic device (CPLD), and the like.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Further, while operations are depicted in a particular order, this should be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
Claims (10)
1. A learning content recommendation method, comprising:
acquiring personal information and application characteristic information of a login user;
judging whether a recommendation rule of the login user exists according to the personal information of the login user; if the personal information and the application characteristic information of the login user exist, acquiring the existing recommendation rule, and if the personal information and the application characteristic information do not exist, generating the recommendation rule of the login user according to the personal information and the application characteristic information of the login user;
calculating a content recommendation heat value according to the recommendation rule;
and pushing learning content to the login user according to the content recommendation heat value.
2. The method of claim 1, applied to a national staff academic platform, wherein:
the application characteristic information comprises user news browsing information, query information and/or geographical location information;
the personal information includes unique identification information.
3. The method of claim 2, wherein obtaining the existing recommendation rule comprises:
and acquiring the recommendation rule of the user according to the unique identification information of the login user.
4. The method of claim 3,
the recommendation rule is composed of an algorithm rule matrix;
the algorithm rule matrix comprises recommended item category information, information about whether to participate in calculation and/or recommended weight information.
5. The method of claim 4, wherein the transaction category information comprises:
post information, law enforcement and service event label information, user preference information, and/or geographic location information.
6. The method of claim 5, wherein the calculating a content recommendation hotness value according to the recommendation rule comprises:
acquiring learning content;
determining unfinished learning content according to personal information of a login user;
and calculating to obtain the learning content recommendation heat value according to the recommendation rule associated with the uncompleted learning content.
7. The method according to claim 6, wherein the learning content recommendation heat value is calculated using the following formula:
wherein H is a learning content recommendation heat value;
c is whether to participate in the calculated value;
Tkis an item category;
(Ln) is TkSpecific information of the item category;
m is TkNumber of transaction categories;
if participating in the calculation, the C is taken as 1; if not, taking 0 as C, and taking part in calculation under the default condition;
Qkcalculating weights for the event categories in the learning content recommendation.
8. The method of claim 7, wherein pushing learning content to the logged on user according to the content recommendation heat value comprises:
arranging the unfinished learning contents from large to small according to the content recommendation heat value;
and displaying or sending information to the user according to the arrangement sequence.
9. The method of claim 8, further comprising, after said pushing learning content to said logged on user according to said content recommendation popularity value:
analyzing the push information, if the push information is adopted, increasing the calculation weight Q of the item category according to a preset valuekAnd meanwhile, updating the recommendation rule of the user.
10. A learning content recommendation apparatus characterized by comprising:
the acquisition module is used for acquiring personal information and application characteristic information of a login user;
the judging module is used for judging whether the recommendation rule of the login user exists according to the personal information of the login user; if the personal information and the application characteristic information of the login user exist, acquiring the existing recommendation rule, and if the personal information and the application characteristic information do not exist, generating the recommendation rule of the login user according to the personal information and the application characteristic information of the login user;
the calculation module is used for calculating a content recommendation heat value according to the recommendation rule;
and the pushing module is used for pushing learning content to the login user according to the content recommendation heat value.
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| CN111460280A (en) * | 2020-02-25 | 2020-07-28 | 中通服创发科技有限责任公司 | Legal service personnel recommendation method based on public legal service platform |
| CN111460280B (en) * | 2020-02-25 | 2023-10-24 | 中通服创发科技有限责任公司 | Legal service personnel recommendation method based on public legal service platform |
| CN117807323A (en) * | 2024-03-01 | 2024-04-02 | 广东烟草汕头市有限责任公司 | Online interactive smart Prime big data platform |
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