CN107992595A - A kind of learning Content recommends method, apparatus and smart machine - Google Patents

A kind of learning Content recommends method, apparatus and smart machine Download PDF

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Publication number
CN107992595A
CN107992595A CN201711318176.1A CN201711318176A CN107992595A CN 107992595 A CN107992595 A CN 107992595A CN 201711318176 A CN201711318176 A CN 201711318176A CN 107992595 A CN107992595 A CN 107992595A
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China
Prior art keywords
examination question
user
degree
difficulty
learning content
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CN201711318176.1A
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Chinese (zh)
Inventor
龙安忠
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广东小天才科技有限公司
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Priority to CN201711318176.1A priority Critical patent/CN107992595A/en
Publication of CN107992595A publication Critical patent/CN107992595A/en

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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90Details of database functions independent of the retrieved data types
    • G06F16/95Retrieval from the web
    • G06F16/953Querying, e.g. by the use of web search engines
    • G06F16/9535Search customisation based on user profiles and personalisation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10Services
    • G06Q50/20Education
    • G06Q50/205Education administration or guidance

Abstract

The present invention is suitable for intelligent recommendation technical field, there is provided and a kind of learning Content recommends method, apparatus and smart machine, the described method includes:Obtain the examination question of user's search;The corresponding answer accuracy of examination question of user's search is searched in large database concept;According to the answer accuracy found, the degree-of-difficulty factor of the examination question is determined by preset algorithm;Recommended and the corresponding learning Content of the degree-of-difficulty factor according to the degree-of-difficulty factor.User can be helped to learn by the above method, improved learning efficiency.

Description

A kind of learning Content recommends method, apparatus and smart machine

Technical field

The invention belongs to intelligent recommendation technical field, more particularly to a kind of learning Content to recommend method, apparatus and intelligence to set It is standby.

Background technology

At present, on the market study class client (also referred to as learning class application) is more and more, and student can pass through viewing Learn class client terminal playing examination question or video classes constantly to carry out Outside Class Studying.Some search topic class APP (Application, application program) after user searches topic, the content for the topic searched for according to user carries out pushing away for other guide Recommend, for example, recommending the similar topic identical with the topic knowledge point, alternatively, recommending to explain regarding for the knowledge point by knowledge point Frequently, but this learning ability and understandability for largely have ignored user itself, because same knowledge point also it is difficult it Point, recommend video classes explanation difficulty it is big, recommendation effect will unobvious, the video classes of recommendation are too simple, and cannot solve The problem of user, influence the study of user.

In conclusion existing recommendation method have ignored learning ability and the understanding of user itself when recommending learning Content Ability, causes the learning Content difficulty recommended too big or learning Content is too simple, it is impossible to help user effectively to learn, so that shadow Ring the learning efficiency of user.

The content of the invention

In view of this, an embodiment of the present invention provides a kind of learning Content to recommend method, apparatus and smart machine, to solve Existing recommendation method have ignored the learning ability and understandability of user itself when recommending learning Content, cause recommend Habit Content Difficulty is too big or learning Content is too simple, it is impossible to helps user effectively to learn, so as to influence the learning efficiency of user The problem of.

First aspect present invention provides a kind of learning Content and recommends method, and the learning Content recommends method to include:

Obtain the examination question of user's search;

The corresponding answer accuracy of examination question of user's search is searched in large database concept;

According to the answer accuracy found, the degree-of-difficulty factor of the examination question is determined by preset algorithm;

Recommended and the corresponding learning Content of the degree-of-difficulty factor according to the degree-of-difficulty factor.

With reference to first aspect, in the first possible implementation of first aspect, described searched in large database concept is used The corresponding accuracy of examination question of family search, including:

The content for the examination question searched for according to user, determines the examination question numbering of the examination question;

The answer situation of the examination question is searched in large database concept according to examination question numbering;

The answer situation of the examination question is counted, determines the answer accuracy of the examination question.

With reference to first aspect, it is described to be pushed away according to the degree-of-difficulty factor in second of possible implementation of first aspect Recommend with the corresponding learning Content of the degree-of-difficulty factor, including:

When the degree-of-difficulty factor is less than the first difficulty value, recommend core curriculum to user;

When the degree-of-difficulty factor is greater than or equal to the first difficulty value, and is less than or equal to the second difficulty value, pushed away to user Recommend raising course;

When the degree-of-difficulty factor is more than three difficulty values, recommend to train excellent course to user.

With reference to first aspect, in the third possible implementation of first aspect, the answer that the basis is found is just True rate, the degree-of-difficulty factor of the examination question is determined by preset algorithm, including:

It is p to define the answer accuracy, and the degree-of-difficulty factor b of the examination question is determined according to equation below:

Wherein, k is positive integer;

At this time, it is described to recommend to include with the corresponding learning Content of the degree-of-difficulty factor according to the degree-of-difficulty factor:

As b < k, recommend core curriculum to user;

As k≤b≤x, recommend to improve course to user, wherein, x is positive integer;

As b > x, recommend to train excellent course to user.

With reference to first aspect, in the 4th kind of possible implementation of first aspect, the learning Content recommends method also Including:

Determine the rank of the corresponding learning Content of the degree-of-difficulty factor;

After detecting that the learning Content of rank corresponding with the degree-of-difficulty factor of recommendation finishes, recommend the difficulty The other learning Content of next stage for spending the corresponding rank of coefficient learns for user.

Second aspect of the present invention provides a kind of learning Content recommendation apparatus, and the learning Content recommendation apparatus includes:

Examination question acquiring unit, for obtaining the examination question of user's search;

Accuracy searching unit, for searching the corresponding answer accuracy of examination question of user's search in large database concept;

Difficulty determination unit, for according to the answer accuracy that finds, determining the difficulty of the examination question by preset algorithm Coefficient;

First content recommendation unit, for according in degree-of-difficulty factor recommendation and the corresponding study of the degree-of-difficulty factor Hold.

With reference to second aspect, in the first possible implementation of second aspect, the accuracy searching unit includes:

Numbering determining module, for the content for the examination question searched for according to user, determines that the examination question of the examination question is numbered;

Answer situation searching module, for searching the answer feelings of the examination question in large database concept according to examination question numbering Condition;

Accuracy determining module, for counting the answer situation of the examination question, determines the answer accuracy of the examination question.

With reference to second aspect, in second of possible implementation of second aspect, the first content recommendation unit bag Include:

First recommending module, for when the degree-of-difficulty factor is less than the first difficulty value, recommending core curriculum to user;

Second recommending module, for being greater than or equal to the first difficulty value when the degree-of-difficulty factor, and less than or equal to second During difficulty value, recommend to improve course to user;

3rd recommending module, for when the degree-of-difficulty factor is more than three difficulty values, recommending to train excellent course to user.

With reference to second aspect, in the third possible implementation of second aspect, the learning Content recommendation apparatus is also Including:

Difficulty determining module, is p for defining the answer accuracy, the difficulty of the examination question is determined according to equation below Coefficient b:Wherein, k is positive integer;

At this time, in the first content recommendation unit 34:

First recommending module, is additionally operable to as b < k, recommends core curriculum to user;

First recommending module, is additionally operable to as k≤b≤x, recommends to improve course to user, wherein, x is positive integer;

First recommending module, is additionally operable to as b > x, recommends to train excellent course to user.

With reference to second aspect, in the 4th kind of possible implementation of second aspect, the learning Content recommendation apparatus is also Including:

The learning Content recommendation apparatus further includes:

Level deciding unit, for determining the rank of the corresponding learning Content of the degree-of-difficulty factor;

Second commending contents unit, for when the learning Content for the rank corresponding with the degree-of-difficulty factor for detecting recommendation After finishing, the other learning Content of next stage of the corresponding rank of the degree-of-difficulty factor is recommended to learn for user.

Third aspect present invention provides a kind of smart machine, including:Memory, processor and it is stored in the storage In device and the computer program that can run on the processor, the processor are realized as above when performing the computer program The step of any one of first aspect learning Content recommends method.

Fourth aspect present invention provides a kind of computer-readable recording medium, the computer-readable recording medium storage There is computer program, realize that as above any one of first aspect learning Content pushes away when the computer program is executed by processor The step of recommending method.

Existing beneficial effect is the embodiment of the present invention compared with prior art:The embodiment of the present invention is searched by obtaining user The examination question of rope, searches the corresponding answer accuracy of examination question of user's search in large database concept, then according to definite answer just True rate, the degree-of-difficulty factor of the examination question is determined by preset algorithm, is finally recommended and the degree-of-difficulty factor according to the degree-of-difficulty factor Corresponding learning Content.The difficulty for the examination question searched in this programme according to user is reflected as grasp journey of the user to knowledge point Degree, recommends corresponding learning Content to user, so that in the study recommended according to the degree-of-difficulty factor of the examination question of user's search Accommodate and share the Grasping level of family instantly, the learning demand of user is better met while improving recommendation effect, so as to help User effectively learns, and improves learning efficiency.

Brief description of the drawings

To describe the technical solutions in the embodiments of the present invention more clearly, below will be to embodiment or description of the prior art Needed in attached drawing be briefly described, it should be apparent that, drawings in the following description be only the present invention some Embodiment, for those of ordinary skill in the art, without having to pay creative labor, can also be according to these Attached drawing obtains other attached drawings.

Fig. 1 be a kind of learning Content provided in an embodiment of the present invention recommend method realize flow chart;

Fig. 2 be another learning Content provided in an embodiment of the present invention recommend method realize flow chart;

Fig. 3 is a kind of structure diagram of learning Content recommendation apparatus provided in an embodiment of the present invention;

Fig. 4 is a kind of schematic diagram of smart machine provided in an embodiment of the present invention.

Embodiment

In being described below, in order to illustrate rather than in order to limit, it is proposed that such as tool of particular system structure, technology etc Body details, to understand thoroughly the embodiment of the present invention.However, it will be clear to one skilled in the art that these are specific The present invention can also be realized in the other embodiments of details.In other situations, omit to well-known system, device, electricity Road and the detailed description of method, in case unnecessary details hinders description of the invention.

The embodiment of the present invention is too big or too simple in order to avoid the learning Content difficulty of recommendation, there is provided a kind of Learning Content recommends method, apparatus and smart machine, wherein the examination question mainly searched for by obtaining user, in large database concept The corresponding answer accuracy of examination question of user's search is searched, according to the answer accuracy found, is determined by preset algorithm described The degree-of-difficulty factor of examination question, finally recommends and the corresponding learning Content of the degree-of-difficulty factor according to the degree-of-difficulty factor.In order to have Body illustrates that above-mentioned learning Content recommends method, apparatus and smart machine, is illustrated below by specific embodiment.

Embodiment one

Fig. 1 shows that a kind of learning Content provided in an embodiment of the present invention recommends the flow chart of method, and details are as follows:

Step S101, obtains the examination question of user's search.

Specifically, examination question to be searched is inputted in input frame of the user on smart machine, or examination question to be searched Keyword, clicks on search, smart machine is after search instruction is detected, according to examination question to be searched or examination question to be searched Keyword, search corresponding examination question.Further, in embodiments of the present invention, in smart machine (or smart machine connection In Cloud Server) examination question of storage has corresponding id examination questions to number,

Step S102, searches the corresponding answer accuracy of examination question of user's search in large database concept.

Alternatively, the corresponding answer accuracy of examination question of user's search is searched for convenience, improves search efficiency, the step Rapid S102 is specifically included:

The content of A1, the examination question searched for according to user, determine the examination question numbering of the examination question.

A2, number the answer situation that the examination question is searched in large database concept according to the examination question.

The answer situation of A3, the statistics examination question, determine the answer accuracy of the examination question.

In embodiments of the present invention, the keyword of the content for each examination question or each examination question sets examination question to compile in advance Number, examination question numbering has uniqueness, and each examination question is corresponded with its examination question numbering.Smart machine detect search instruction it Afterwards, according to the content of examination question to be searched or the keyword of examination question to be searched, determine the examination question numbering of the examination question, pass through Examination question numbering searches different user in big data database and, for the answer situation of the examination question of identical examination question numbering, improves and search effect Rate.

In embodiments of the present invention, smart machine connection big data database, has study in the big data database Content, includes but not limited to examination question and study video, meanwhile, big data database collects the answer situation of different user, answers Topic situation carry out statistical analysis, using different user for same examination question answer situation as an answer set, for the collection Total answer accuracy for calculating same examination question.Wherein, answer set is corresponding with examination question numbering, is numbered to examination question and compiled according to examination question Answer situation in number corresponding answer set carries out statistical analysis, determines that examination question numbers the answer accuracy of corresponding examination question. Further, the average answer accuracy of the examination question in answer set is calculated, the average accuracy of the examination question is that the examination question is compiled The accuracy of number corresponding examination question.

Alternatively, the answer accuracy of cycle renewal examination question.Specifically, when the set of same examination question in big data database In increase the answer situations of other users newly, and/or, during the renewal of the answer situation of existing subscriber, for the answer set of the examination question Recalculate the answer accuracy of examination question.The answer accuracy of examination question is updated by the cycle, the accurate of answer accuracy can be improved Property, so as to more accurately hold the current learning ability of user.

Step S103, according to the answer accuracy found, the degree-of-difficulty factor of the examination question is determined by preset algorithm.

Usually, answer accuracy is inversely proportional with item difficulty, and answer accuracy is higher, then item difficulty is lower, answers questions The user of the examination question is more after all.Alternatively, in embodiments of the present invention, the difficulty of the examination question is determined according to item response theory Coefficient.

Alternatively, in embodiments of the present invention, accurately to determine the difficulty of examination question as far as possible, by the answer found just True rate is defined as p, and the step S103 is specifically included:

The degree-of-difficulty factor b of the examination question is determined according to equation below:Wherein, k is positive integer.

Specifically, when user does answer training, answer accuracy dynamic during according to user's answer updates the difficulty of examination question Spend coefficient.At this time, it is described to recommend to include with the corresponding learning Content of the degree-of-difficulty factor according to the degree-of-difficulty factor:As b < k When, recommend core curriculum to user;As k≤b≤x, recommend to improve course to user, wherein, x is the positive integer more than k;When During b > x, recommend to train excellent course to user.

Step S104, recommends and the corresponding learning Content of the degree-of-difficulty factor according to the degree-of-difficulty factor.

Specifically, the item difficulty intelligent recommendation for the examination question that smart machine is searched for according to user and matched of item difficulty Practise content.In the embodiment of the present invention, the knowledge point explanation according to involved in learning Content, which is drawn, is divided into learning Content to knowledge Put " core curriculum " that understands, " the raising course " that flexibly uses knowledge point and " excellent class is trained to knowledge point breakthrough of difficulties Journey " three classes.Learning Content includes video explanation and topic contact.The item difficulty for the examination question searched for by user reflects To the Grasping level of knowledge point, then the Grasping level for user is done targetedly recommends user, meets the needs of user.

Alternatively, the step S104 includes:

B1, when the degree-of-difficulty factor is less than the first difficulty value, to user recommend core curriculum.Wherein, core curriculum is fitted Close the user for wanting to know about knowledge point.

B2, be greater than or equal to the first difficulty value when the degree-of-difficulty factor, and when being less than or equal to the second difficulty value, to user Recommend to improve course.Wherein, course is improved to be adapted to wish the user that can flexibly use knowledge point substantially.

B3, when the degree-of-difficulty factor is more than three difficulty values, recommend to train excellent course to user.Wherein, excellent course is trained to fit Close the user to knowledge point breakthrough of difficulties.

In embodiments of the present invention, the difficulty of examination question is divided into 0-10 grades, therefore, degree-of-difficulty factor b:0≤b≤10. In the embodiment of the present invention, k=5, x=2 are taken, at this time,Specifically, degree-of-difficulty factor b<5 are defined as to knowing Know point to understand, 5≤b≤7 are defined as the flexible utilization of knowledge point, b>The breakthrough of difficulties of 7 pairs of knowledge points.At this time, the step S104 includes:As b < 5, recommend core curriculum to user;As 5≤b≤7, recommend to improve course to user;As b > 7, Recommend to train excellent course to user.Grasping level of the user to knowledge-ID is held according to the difficulty of the examination question of user's search, The learning Content for targetedly recommending the Grasping level with user to match, to meet the learning demand of user.

In first embodiment of the invention, by obtaining the examination question of user's search, user's search is searched in large database concept The corresponding answer accuracy of examination question, then according to definite answer accuracy, the difficulty system of the examination question is determined by preset algorithm Number, finally recommends and the corresponding learning Content of the degree-of-difficulty factor according to the degree-of-difficulty factor.Searched in this programme according to user The difficulty of the examination question of rope is reflected as Grasping level of the user to knowledge point, is recommended according to the degree-of-difficulty factor of the examination question of user's search Corresponding learning Content is to user, so that the learning Content recommended is adapted to the Grasping level of user instantly, improves recommendation effect While better meet the learning demand of user, so as to help user effectively to learn, improve learning efficiency.

Embodiment two

Fig. 2 shows that another learning Content provided in an embodiment of the present invention recommends the flow chart of method, and details are as follows:

Step S201, obtains the examination question of user's search.

Step S202, searches the corresponding answer accuracy of examination question of user's search in large database concept.

Step S203, according to the answer accuracy found, the degree-of-difficulty factor of the examination question is determined by preset algorithm.

Step S204, recommends and the corresponding learning Content of the degree-of-difficulty factor according to the degree-of-difficulty factor.

In the present embodiment, the specific steps of step S201 to step S204 are referring to one step S101 of embodiment to step S104, details are not described herein.

Step S205, determines the rank of the corresponding learning Content of the degree-of-difficulty factor.

Specifically, the degree-of-difficulty factor for the topic searched for according to user determines the rank of corresponding learning Content.Degree-of-difficulty factor Higher, the rank of corresponding learning Content is higher.In embodiments of the present invention, by learning Content by the grasp feelings to knowledge point Condition classification is other, is divided into knowledge point understands, knowledge point flexibly uses and three ranks of knowledge point breakthrough of difficulties.Wherein, knowledge The rank of the learning Content of point breakthrough of difficulties is better than the rank for the learning Content that knowledge point flexibly uses, knowledge point flexibly Practise rank of the rank better than the learning Content that knowledge point understands of content.

Alternatively, the step S205 includes:

C1, when the degree-of-difficulty factor is less than the first difficulty value, the course that true directional user recommends is first level.

C2, be greater than or equal to the first difficulty value when the degree-of-difficulty factor, and when being less than or equal to the second difficulty value, to user The course of recommendation is second level.

C3, when the degree-of-difficulty factor is more than three difficulty values, to user recommend course be third level.

For example, knowledge point understands corresponding core curriculum (i.e. the course of first level), knowledge point is flexibly improved with corresponding Course (i.e. the course of second level), the breakthrough of difficulties of knowledge point, which corresponds to, trains excellent course (i.e. the course of third level).It is exemplary Ground, degree-of-difficulty factor b<When 5, the core curriculum recommended to user is first level, as 5≤b≤7, the raising recommended to user Course is second level;As b > 7, the excellent course of training recommended to user is third level.

Step S206, after detecting that the learning Content of rank corresponding with the degree-of-difficulty factor of recommendation finishes, The other learning Content of next stage of the corresponding rank of the degree-of-difficulty factor is recommended to learn for user.The corresponding level of current degree-of-difficulty factor After other learning Content finishes, recommend the other learning Content of subordinate to user.For example, the difficulty of the topic when user's search When degree coefficient correspondence recommendation learning Content is core curriculum, if detecting, the core curriculum of recommendation finishes, and continues to recommend The other learning Content of next stage such as improves course, so that user continues to learn for same knowledge point.Further, when user searches It is that if detecting, the excellent course of training of recommendation plays when training excellent course that the degree-of-difficulty factor of the topic of rope, which corresponds to recommendation learning Content, Finish, then recommend the learning Content with other associated knowledge points of the excellent course of the training, facilitate user development to learn, improve recommendation effect While improve user learning efficiency.

In second embodiment of the invention, by obtaining the examination question of user's search, user's search is searched in large database concept The corresponding answer accuracy of examination question, then according to definite answer accuracy, the difficulty system of the examination question is determined by preset algorithm Number, further according to degree-of-difficulty factor recommendation and the corresponding learning Content of the degree-of-difficulty factor, and determines the degree-of-difficulty factor pair The rank for the learning Content answered, finally, when the learning Content for the rank corresponding with the degree-of-difficulty factor for detecting recommendation plays After, recommend the other learning Content of next stage of the corresponding rank of the degree-of-difficulty factor to learn for user.Basis in this programme The difficulty of the examination question of user's search is reflected as Grasping level of the user to knowledge point, the degree-of-difficulty factor for the examination question searched for according to user To recommend corresponding learning Content to user, so that the learning Content recommended is adapted to the Grasping level of user instantly, preferably Meet the learning demand of user, help user effectively to learn while improving recommendation effect, in the learning Content of the rank of recommendation Continue to recommend the other learning Content of next stage after finishing, facilitate user to continue deeper into study, the grasp to knowledge point is completeer It is whole, improve learning efficiency.

It is to be understood that the size of the sequence number of each step is not meant to the priority of execution sequence, each process in above-described embodiment Execution sequence should determine that the implementation process without tackling the embodiment of the present invention forms any limit with its function and internal logic It is fixed.

Embodiment three

Recommend method corresponding to the learning Content described in foregoing embodiments, Fig. 3 shows provided in an embodiment of the present invention one The structure diagram of kind learning Content recommendation apparatus, the device can be applied to smart machine, which can be included through wireless Access net RAN and the user equipment that communicates of one or more core nets, the user's equipment can be that mobile phone (or is " honeycomb " phone), the computer with mobile equipment etc., for example, user equipment can also be portable, pocket, hand-held, Built-in computer or vehicle-mounted mobile device, they exchange voice and/or data with wireless access network.In another example the shifting Dynamic equipment can include smart mobile phone, tablet computer, personal digital assistant PDA or vehicle-mounted computer etc..For convenience of description, only show Go out and the relevant part of the embodiment of the present invention.

With reference to Fig. 3, which includes:Examination question acquiring unit 31, accuracy searching unit 32, difficulty is true Order member 33, first content recommendation unit 34, wherein:

Examination question acquiring unit 31, for obtaining the examination question of user's search;

Accuracy searching unit 32, for searching the corresponding answer accuracy of examination question of user's search in large database concept;

Difficulty determination unit 33, for according to the answer accuracy that finds, determining the difficulty of the examination question by preset algorithm Spend coefficient;

First content recommendation unit 34, for being recommended and the corresponding study of the degree-of-difficulty factor according to the degree-of-difficulty factor Content.

Alternatively, the accuracy searching unit 32 includes:

Numbering determining module, for the content for the examination question searched for according to user, determines that the examination question of the examination question is numbered;

Answer situation searching module, for searching the answer feelings of the examination question in large database concept according to examination question numbering Condition;

Accuracy determining module, for counting the answer situation of the examination question, determines the answer accuracy of the examination question.

Alternatively, the first content recommendation unit 34 includes:

First recommending module, for when the degree-of-difficulty factor is less than the first difficulty value, recommending core curriculum to user;

Second recommending module, for being greater than or equal to the first difficulty value when the degree-of-difficulty factor, and less than or equal to second During difficulty value, recommend to improve course to user;

3rd recommending module, for when the degree-of-difficulty factor is more than three difficulty values, recommending to train excellent course to user.

Alternatively, the difficulty determination unit 33 includes:

Difficulty determining module, is p for defining the answer accuracy, the difficulty of the examination question is determined according to equation below Coefficient b:Wherein, k is positive integer.

At this time, in the first content recommendation unit 34:

First recommending module, is additionally operable to as b < k, recommends core curriculum to user;

First recommending module, is additionally operable to as k≤b≤x, recommends to improve course to user, wherein, x is positive integer;

First recommending module, is additionally operable to as b > x, recommends to train excellent course to user.

Alternatively, the learning Content recommendation apparatus further includes:

Level deciding unit 35, for determining the rank of the corresponding learning Content of the degree-of-difficulty factor;

Second commending contents unit 36, detects in the study of rank corresponding with the degree-of-difficulty factor of recommendation for working as After appearance finishes, the other learning Content of next stage of the corresponding rank of the degree-of-difficulty factor is recommended to learn for user.

In third embodiment of the invention, by obtaining the examination question of user's search, user's search is searched in large database concept The corresponding answer accuracy of examination question, then according to definite answer accuracy, the difficulty system of the examination question is determined by preset algorithm Number, further according to degree-of-difficulty factor recommendation and the corresponding learning Content of the degree-of-difficulty factor, and determines the degree-of-difficulty factor pair The rank for the learning Content answered, finally, when the learning Content for the rank corresponding with the degree-of-difficulty factor for detecting recommendation plays After, recommend the other learning Content of next stage of the corresponding rank of the degree-of-difficulty factor to learn for user.Basis in this programme The difficulty of the examination question of user's search is reflected as Grasping level of the user to knowledge point, the degree-of-difficulty factor for the examination question searched for according to user To recommend corresponding learning Content to user, so that the learning Content recommended is adapted to the Grasping level of user instantly, preferably Meet the learning demand of user, help user effectively to learn while improving recommendation effect, in the learning Content of the rank of recommendation Continue to recommend the other learning Content of next stage after finishing, facilitate user to continue deeper into study, the grasp to knowledge point is completeer It is whole, improve learning efficiency.

Example IV:

Fig. 4 is the schematic diagram for the smart machine that one embodiment of the invention provides.As shown in figure 4, the intelligence of the embodiment is set Standby 4 include:Processor 40, memory 41 and it is stored in the meter that can be run in the memory 41 and on the processor 40 Calculation machine program 42, such as learning Content recommended program.The processor 40 is realized above-mentioned each when performing the computer program 42 A learning Content recommends the step in embodiment of the method, such as the step 101 shown in Fig. 1 is to 104.Alternatively, the processor 40 The function of each module/unit in above-mentioned each device embodiment, such as unit shown in Fig. 3 are realized when performing the computer program 42 31 to 34 function.

Exemplary, the computer program 42 can be divided into one or more module/units, it is one or Multiple module/units are stored in the memory 41, and are performed by the processor 40, to complete the present invention.Described one A or multiple module/units can be the series of computation machine programmed instruction section that can complete specific function, which is used for Implementation procedure of the computer program 42 in the smart machine 4 is described.For example, the computer program 42 can be divided Examination question acquiring unit, accuracy searching unit, difficulty determination unit 33, first content recommendation unit 34 are cut into, each unit is specific Function is as follows:

Examination question acquiring unit, for obtaining the examination question of user's search;

Accuracy searching unit, for searching the corresponding answer accuracy of examination question of user's search in large database concept;

Difficulty determination unit, for according to the answer accuracy that finds, determining the difficulty of the examination question by preset algorithm Coefficient;

First content recommendation unit, for according in degree-of-difficulty factor recommendation and the corresponding study of the degree-of-difficulty factor Hold.

The smart machine 4 can be that the calculating such as desktop PC, notebook, palm PC and cloud server are set It is standby.The smart machine 4 may include, but be not limited only to, processor 40, memory 41.It will be understood by those skilled in the art that figure 4 be only the example of smart machine 4, does not form the restriction to smart machine 4, can be included than illustrating more or fewer portions Part, either combines some components or different components, such as the smart machine can also include input-output equipment, net Network access device, bus etc..

Alleged processor 40 can be central processing unit (Central Processing Unit, CPU), can also be Other general processors, digital signal processor (Digital Signal Processor, DSP), application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), ready-made programmable gate array (Field- Programmable Gate Array, FPGA) either other programmable logic device, discrete gate or transistor logic, Discrete hardware components etc..General processor can be microprocessor or the processor can also be any conventional processor Deng.

The memory 41 can be the internal storage unit of the smart machine 4, such as the hard disk of smart machine 4 or interior Deposit.The memory 41 can also be the External memory equipment of the smart machine 4, such as be equipped with the smart machine 4 Plug-in type hard disk, intelligent memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card, dodges Deposit card (Flash Card) etc..Further, the memory 41 can also both include the storage inside list of the smart machine 4 Member also includes External memory equipment.The memory 41 is used to store needed for the computer program and the smart machine Other programs and data.The memory 41 can be also used for temporarily storing the data that has exported or will export.

It is apparent to those skilled in the art that for convenience of description and succinctly, only with above-mentioned each work( Can unit, module division progress for example, in practical application, can be as needed and by above-mentioned function distribution by different Functional unit, module are completed, i.e., the internal structure of described device are divided into different functional units or module, more than completion The all or part of function of description.Each functional unit, module in embodiment can be integrated in a processing unit, also may be used To be that unit is individually physically present, can also two or more units integrate in a unit, it is above-mentioned integrated Unit can both be realized in the form of hardware, can also be realized in the form of SFU software functional unit.In addition, each function list Member, the specific name of module are not limited to the protection domain of the application also only to facilitate mutually distinguish.Said system The specific work process of middle unit, module, may be referred to the corresponding process in preceding method embodiment, details are not described herein.

In the above-described embodiments, the description to each embodiment all emphasizes particularly on different fields, and is not described in detail or remembers in some embodiment The part of load, may refer to the associated description of other embodiments.

Those of ordinary skill in the art may realize that each exemplary list described with reference to the embodiments described herein Member and algorithm steps, can be realized with the combination of electronic hardware or computer software and electronic hardware.These functions are actually Performed with hardware or software mode, application-specific and design constraint depending on technical solution.Professional technician Described function can be realized using distinct methods to each specific application, but this realization is it is not considered that exceed The scope of the present invention.

In embodiment provided by the present invention, it should be understood that disclosed apparatus and method, can pass through others Mode is realized.For example, system embodiment described above is only schematical, for example, the division of the module or unit, Only a kind of division of logic function, can there is an other dividing mode when actually realizing, such as multiple units or component can be with With reference to or be desirably integrated into another system, or some features can be ignored, or not perform.It is another, it is shown or discussed Mutual coupling or direct-coupling or communication connection can be by some interfaces, the INDIRECT COUPLING of device or unit or Communication connection, can be electrical, machinery or other forms.

The unit illustrated as separating component may or may not be physically separate, be shown as unit The component shown may or may not be physical location, you can with positioned at a place, or can also be distributed to multiple In network unit.Some or all of unit therein can be selected to realize the mesh of this embodiment scheme according to the actual needs 's.

In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, can also That unit is individually physically present, can also two or more units integrate in a unit.Above-mentioned integrated list Member can both be realized in the form of hardware, can also be realized in the form of SFU software functional unit.

If the integrated unit is realized in the form of SFU software functional unit and is used as independent production marketing or use When, it can be stored in a computer read/write memory medium.Based on such understanding, the present invention realizes above-described embodiment side All or part of flow in method, can also instruct relevant hardware to complete, the computer by computer program Program can be stored in a computer-readable recording medium, and the computer program is when being executed by processor, it can be achieved that above-mentioned each The step of a embodiment of the method.Wherein, the computer program includes computer program code, and the computer program code can Think source code form, object identification code form, executable file or some intermediate forms etc..The computer-readable medium can be with Including:Any entity or device, recording medium, USB flash disk, mobile hard disk, magnetic disc, the light of the computer program code can be carried Disk, computer storage, read-only storage (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium etc..It should be noted that the computer The content that computer-readable recording medium includes can carry out appropriate increase and decrease according to legislation in jurisdiction and the requirement of patent practice, such as In some jurisdictions, according to legislation and patent practice, computer-readable medium does not include being electric carrier signal and telecommunications letter Number.

Embodiment described above is merely illustrative of the technical solution of the present invention, rather than its limitations;Although with reference to foregoing reality Example is applied the present invention is described in detail, it will be understood by those of ordinary skill in the art that:It still can be to foregoing each Technical solution described in embodiment is modified, or carries out equivalent substitution to which part technical characteristic;And these are changed Or replace, the essence of appropriate technical solution is departed from the spirit and scope of various embodiments of the present invention technical solution, should all Within protection scope of the present invention.

Claims (10)

1. a kind of learning Content recommends method, it is characterised in that the learning Content recommends method to include:
Obtain the examination question of user's search;
The corresponding answer accuracy of examination question of user's search is searched in large database concept;
According to the answer accuracy found, the degree-of-difficulty factor of the examination question is determined by preset algorithm;
Recommended and the corresponding learning Content of the degree-of-difficulty factor according to the degree-of-difficulty factor.
2. learning Content as claimed in claim 1 recommends method, it is characterised in that the lookup user in large database concept searches The corresponding accuracy of examination question of rope, including:
The content for the examination question searched for according to user, determines the examination question numbering of the examination question;
The answer situation of the examination question is searched in large database concept according to examination question numbering;
The answer situation of the examination question is counted, determines the answer accuracy of the examination question.
3. learning Content as claimed in claim 1 recommends method, it is characterised in that it is described according to the degree-of-difficulty factor recommend with The corresponding learning Content of degree-of-difficulty factor, including:
When the degree-of-difficulty factor is less than the first difficulty value, recommend core curriculum to user;
When the degree-of-difficulty factor is greater than or equal to the first difficulty value, and is less than or equal to the second difficulty value, recommend to carry to user High course;
When the degree-of-difficulty factor is more than three difficulty values, recommend to train excellent course to user.
4. learning Content as claimed in claim 1 recommends method, it is characterised in that the answer that the basis is found is correct Rate, the degree-of-difficulty factor of the examination question is determined by preset algorithm, including:
It is p to define the answer accuracy, and the degree-of-difficulty factor b of the examination question is determined according to equation below:
Wherein, k is positive integer;
At this time, it is described to recommend to include with the corresponding learning Content of the degree-of-difficulty factor according to the degree-of-difficulty factor:
As b < k, recommend core curriculum to user;
As k≤b≤x, recommend to improve course to user, wherein, x is positive integer;
As b > x, recommend to train excellent course to user.
5. as Claims 1-4 any one of them learning Content recommends method, it is characterised in that described according to the difficulty Spend after coefficient recommendation and the corresponding learning Content of the degree-of-difficulty factor, including:
Determine the rank of the corresponding learning Content of the degree-of-difficulty factor;
After detecting that the learning Content of rank corresponding with the degree-of-difficulty factor of recommendation finishes, recommend the difficulty system The other learning Content of next stage of the corresponding rank of number learns for user.
6. a kind of learning Content recommendation apparatus, it is characterised in that the learning Content recommendation apparatus includes:
Examination question acquiring unit, for obtaining the examination question of user's search;
Accuracy searching unit, for searching the corresponding answer accuracy of examination question of user's search in large database concept;
Difficulty determination unit, for according to the answer accuracy that finds, determining the degree-of-difficulty factor of the examination question by preset algorithm;
First content recommendation unit, for being recommended and the corresponding learning Content of the degree-of-difficulty factor according to the degree-of-difficulty factor.
7. learning Content recommendation apparatus as claimed in claim 6, it is characterised in that the accuracy searching unit includes:
Numbering determining module, for the content for the examination question searched for according to user, determines that the examination question of the examination question is numbered;
Answer situation searching module, for searching the answer situation of the examination question in large database concept according to examination question numbering;
Accuracy determining module, for counting the answer situation of the examination question, determines the answer accuracy of the examination question.
8. learning Content recommendation apparatus as claimed in claim 6, it is characterised in that the first content recommendation unit includes:
First recommending module, for when the degree-of-difficulty factor is less than the first difficulty value, recommending core curriculum to user;
Second recommending module, for being greater than or equal to the first difficulty value when the degree-of-difficulty factor, and is less than or equal to the second difficulty During value, recommend to improve course to user;
3rd recommending module, for when the degree-of-difficulty factor is more than three difficulty values, recommending to train excellent course to user.
9. a kind of smart machine, including memory, processor and it is stored in the memory and can be on the processor The computer program of operation, it is characterised in that the processor realizes such as claim 1 to 5 when performing the computer program The step of any one learning Content recommends method.
10. a kind of computer-readable recording medium, the computer-readable recording medium storage has computer program, its feature exists In realizing that learning Content recommends method as described in any one of claim 1 to 5 when the computer program is executed by processor Step.
CN201711318176.1A 2017-12-12 2017-12-12 A kind of learning Content recommends method, apparatus and smart machine CN107992595A (en)

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Publication number Priority date Publication date Assignee Title
CN104143163A (en) * 2014-08-04 2014-11-12 兴天通讯技术(天津)有限公司 Generating method and device for knowledge map
CN105976282A (en) * 2016-05-05 2016-09-28 广东小天才科技有限公司 Test question difficulty quantification method and system
CN106202453A (en) * 2016-07-13 2016-12-07 网易(杭州)网络有限公司 A kind of multimedia resource recommends method and apparatus
CN106599089A (en) * 2016-11-23 2017-04-26 广东小天才科技有限公司 Test question recommendation method and device based on knowledge points and user equipment

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104143163A (en) * 2014-08-04 2014-11-12 兴天通讯技术(天津)有限公司 Generating method and device for knowledge map
CN105976282A (en) * 2016-05-05 2016-09-28 广东小天才科技有限公司 Test question difficulty quantification method and system
CN106202453A (en) * 2016-07-13 2016-12-07 网易(杭州)网络有限公司 A kind of multimedia resource recommends method and apparatus
CN106599089A (en) * 2016-11-23 2017-04-26 广东小天才科技有限公司 Test question recommendation method and device based on knowledge points and user equipment

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