CN106294491A - Study notes recommend method and device - Google Patents

Abstract

The invention provides a kind of study notes and recommend method and device, wherein, the method includes: record the study notes of one or more first user in the way of audio frequency and video；Select the study notes of the one or more first users conformed to a predetermined condition；The study notes of the one or more first users selected are recommended the second user.Pass through the present invention, solving user present in correlation technique can only cause feedback efficiency low with the form feedback learning gains in depth of comprehension of word, and the loaded down with trivial details very long problem of process of search study notes, and then reached raising study notes feedback efficiency, reduce the effect of the loaded down with trivial details degree of study notes search.

Description

Study notes recommend method and device

Technical field

The present invention relates to the communications field, recommend method and device in particular to a kind of study notes.

Background technology

Now with the development of network multimedia technology, modern distance education can support word, image, audio frequency, video etc. The either synchronously or asynchronously network transmission of media form, but the technology of advanced person differs and successfully completes remote teaching surely, it is achieved High-quality classroom interactions is only the key improving remote teaching quality.

One subject is generally made up of a lot of knowledge points, and therefore each subject is typically divided into several chapters and sections by remote education network, often One chapters and sections divide several having said, each saying to be equivalent to a class, arrange exercises on-line after every class.Exercises on-line is only student Do self-test assessment, after completing having learnt of whole subject, carry out formal examination again examine.But between each knowledge point There is contact, the understanding of certain knowledge point is slightly felt uncertain by user (below be student as a example by illustrate by user), The obstruction of the study of knowledge point after often causing.It is therefore seen that the degree of attentiveness of student is still had by present long-distance educational system Deficiency, mainly based on teachers ' teaching resource, teacher is propagated course in a network in multimedia mode, is then surveyed by student Study situation is estimated by the result that examination is submitted, system generate relevant statistics.By these statistical datas, teacher is only Will appreciate which knowledge point is generally understood more weak by student, it is impossible to learn that from the visual angle of student the student of different levels is in study in detail During encounter which problem on earth, cause the content of courses there is no good specific aim, student the best doubt feedback and Solve path, it is impossible to the problem such as orthetics learning method pointedly.Although remote education network is widely used for E-learning forum in recent years The mode of altar adds the interaction between teachers and students and between student, but is all to carry out interactive learning experience of sharing with the form of word, and Can not effectively feed back student learning gains in depth of comprehension, such as student learning situation and experience, still be difficult in students'learning Solve a problem thinking, derivation etc. embody, and when searching out valuable study notes (such as, learning experience), past Toward will be through very long search procedure.

Feedback efficiency can only be caused low with the form feedback learning gains in depth of comprehension of word for user present in correlation technique, and search The loaded down with trivial details very long problem of process of rope study notes, the most not yet proposes effective solution.

Summary of the invention

The invention provides a kind of study notes and recommend method and device, with at least solve present in correlation technique user can only be with The form feedback learning gains in depth of comprehension of word and cause feedback efficiency low, and the loaded down with trivial details very long problem of process of search study notes.

According to an aspect of the invention, it is provided a kind of study notes recommend method, including: record one in the way of audio frequency and video Individual or the study notes of multiple first user；Select the study notes of the one or more first users conformed to a predetermined condition；Will choosing The study notes of the one or more first user selected recommend the second user.

Alternatively, the study notes selecting the one or more first users conformed to a predetermined condition include at least one of: select The study notes of the one or more first users got good marks, wherein, described in get good marks and exceed for school grade First predetermined condition；Selection school grade progress exceedes the study notes of one or more first users of predetermined threshold；According to institute The match parameter that stating the second user provides selects the study notes of one or more first users.

Alternatively, the study notes selecting the one or more first users got good marks include: to the first pre-defined algorithm Operation result carries out sort descending；Wherein, described first pre-defined algorithm is: $\mathrm{Opereation}11(i,j1)=\underset{k\∈\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\Σ}}{\mathrm{\μ}}_{\mathrm{ik}}({s1}_{j1k}-{s1}_{\mathrm{ik}}),$ I ∈ Set (Students), j1 ∈ Set (ClassA), ${\mathrm{\μ}}_{\mathrm{ik}}=\left\{\begin{array}{cc}\mathrm{\α}& {s1}_{\mathrm{ik}}\∈\mathrm{LevelA}\\ \mathrm{\β}& {s1}_{\mathrm{ik}}\∈\mathrm{LevelB}\\ ...& ...\end{array}\right.,$ Described Set (Students) is the set of user, Students={u₁,u₂,u₃,...,u_n, u_nFor nth user, described Set (ClassA) is the set of the superior users that comprehensive rank is A class of school grade, Set (Knowledge) is the set of knowledge point, Knowledge={k₁,k₂,k₃,...,k_m, k_mFor m-th knowledge point, s1_j1k For the operation achievement of the knowledge point k of user j1, s1_ikBeing the operation achievement of the knowledge point k of the second user i, Level is described Operation achievement s1 of the knowledge point k of two user i_ikCorresponding individual event rank, LevelA is outstanding, and LevelB is good, μ_ikFor Weighting parameter, α is the number more than 1, and β is the number more than α；The first user selecting ranking to be front N；To the second predetermined calculation The operation result of method carries out sort descending, and wherein, described second pre-defined algorithm is: $\mathrm{Opereation}12(i,j2)=\underset{k\∈\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\Σ}}{\mathrm{\μ}}_{\mathrm{ik}}{\mathrm{\θ}}_{j2k},$ J2 ∈ Set (topN (resule (operation11) ↓)), Set (topN (result (operation11) ↓)) is that the operation result to described first pre-defined algorithm carries out sort descending, and ranking is front The set of the first user composition of N, θ_j2kValue parameter for the study notes about knowledge point k that first user j2 uploads； The study notes of the first user of the first predetermined quantity that selected and sorted is the most forward.

Alternatively, the study notes of one or more first users that selection school grade progress exceedes predetermined threshold include: utilize 3rd pre-defined algorithm one screens M study starting stage and differs with the study ability to accept of described second user and be less than the of predetermined value One user, wherein, described 3rd pre-defined algorithm one is: $\mathrm{Opereation}21(i,j3)=\underset{k\∈\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\Σ}}{\mathrm{\μ}}_{\mathrm{ik}}|{s1}_{j3k}-{s1}_{\mathrm{ik}}|,$ I ∈ Set (Students), j3 ∈ Set (Class (i)), ${\mathrm{\μ}}_{\mathrm{ik}}=\left\{\begin{array}{cc}\mathrm{\α}& {s1}_{\mathrm{ik}}\∈\mathrm{LevelA}\\ \mathrm{\β}& {s1}_{\mathrm{ik}}\∈\mathrm{LevelB}\\ ...& ...\end{array}\right.,$ Described Set (Students) is The set of user, Students={u₁,u₂,u₃,...,u_n, u_nFor nth user, Set (Class (i)) is combining of school grade Closing the set of rank and described second user identical for user i, Set (Knowledge) is the set of knowledge point, Knowledge={k₁,k₂,k₃,...,k_m, k_mFor m-th knowledge point, s1_j3kFor the operation achievement of the knowledge point k of user j3, s1_ikBeing the operation achievement of the knowledge point k of the second user i, Level is operation achievement s1 of the knowledge point k of described second user i_ik Corresponding individual event rank, LevelA is outstanding, and LevelB is good, μ_ikFor weighting parameter, α is the number more than 1, and β is Number more than α；The 3rd pre-defined algorithm two is utilized to screen L first user, wherein, described from described M first user Three pre-defined algorithms two are: $\mathrm{Opereation}22(i,j4)=\underset{k\∈\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\Σ}}{\mathrm{\μ}}_{\mathrm{ik}}({s2}_{j4k}-{s2}_{\mathrm{ik}}),$ Wherein, J4 ∈ Set (topM (result (operation 21) ↑)), Set (topM (result (operation 21) ↑)) are to described The operation result of three pre-defined algorithms carries out sort ascending, and ranking is the set of the first user composition of front M, s2_j4kFor user j4's The new operation achievement of knowledge point k, s2_ikIt it is the new operation achievement of the knowledge point k of the second user i；To the 4th pre-defined algorithm Operation result carries out sort descending, and wherein, described 4th pre-defined algorithm is:J5 ∈ Set (topL (resule (operation22) ↓)), θ_j5kFor The value parameter of the study notes about knowledge point k that first user j5 uploads；The second predetermined quantity that selected and sorted is the most forward The study notes of first user.

Alternatively, described match parameter includes at least one of: the knowledge point interested that described second user specifies, described The user interested that second user specifies, the study notes that described second user specifies are worth threshold value, according to described second user The match parameter provided selects the study notes of one or more first users to include: carry out the operation result of the 5th pre-defined algorithm Sort descending, wherein, described 5th pre-defined algorithm is: $\mathrm{Opereation}31(i,j6)=\underset{k\&Element;\mathrm{SOI}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}({s1}_{j6k}-{s1}_{\mathrm{ik}}),$ Wherein, i ∈ Set (Students), j6 ∈ SOI (Students), ${\mathrm{\&mu;}}_{\mathrm{ik}}=\left\{\begin{array}{cc}\mathrm{\&alpha;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelA}\\ \mathrm{\&beta;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelB}\\ ...& ...\end{array}\right.,$ Described Set (Students) is the set of user, Students={u₁,u₂,u₃,...,u_n, u_nFor nth user, SOI (Knowledge) is the knowledge point interested set that described second user specifies, and described SOI (Students) is described the User's set interested that two users specify, s1_j6kFor the operation achievement of the knowledge point k of user j6, s1_ikIt is the second user i The operation achievement of knowledge point k, Level is operation achievement s1 of the knowledge point k of described second user i_ikCorresponding individual event rank, LevelA is outstanding, and LevelB is good, μ_ikFor weighting parameter, α is the number more than 1, and β is the number more than α；Select The Q name first user that order is the most forward；The operation result of the 6th pre-defined algorithm is carried out sort descending, wherein, described 6th pre- Determining algorithm is: $\mathrm{Opereation}32(i,j7)=\underset{k\&Element;\mathrm{SOI}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{j7k}{\mathrm{\&theta;}}_{j7k},$ Wherein, J7 ∈ Set (topQ (resule (operation31) ↓)), ${\mathrm{\&mu;}}_{j7k}=\left\{\begin{array}{cc}0& {\mathrm{\&theta;}}_{j7k}<{\mathrm{\&theta;}}_{\min }\\ 1& {\mathrm{\&theta;}}_{j7k}>{\mathrm{\&theta;}}_{\min }\end{array}\right.,$ Set (topQ (resule (operation31) ↓)) is that the operation result to described 5th pre-defined algorithm carries out sort descending, and ranking is The set of the first user composition of front Q, μ_j7kFor weight parameter, θ_j7kAbout knowledge point k uploaded for first user j7 Practise the value parameter of gains in depth of comprehension, θ_minThe study notes specified for described second user are worth threshold value；The most forward the 3rd pre-of selected and sorted The study notes of the first user of determined number.

According to a further aspect in the invention, it is provided that a kind of study notes recommendation apparatus, including: logging modle, for regarding with sound The mode of frequency records the study notes of one or more first user；Select module, for select conform to a predetermined condition one or The study notes of multiple first users；Recommending module, for pushing away the study notes of the one or more first user selected Recommend to the second user.

Alternatively, described selection module includes at least one of: first selects unit, for selecting got good marks Individual or the study notes of multiple first user, wherein, described in get good marks as school grade more than the first predetermined condition；The Two select unit, for selecting school grade progress to exceed the study notes of one or more first users of predetermined threshold；3rd Selecting unit, the match parameter for providing according to described second user selects the study notes of one or more first users.

Alternatively, described first unit is selected to include: the operation result of the first pre-defined algorithm is carried out sort descending；Wherein, institute Stating the first pre-defined algorithm is: $\mathrm{Opereation}11(i,j1)=\underset{k\&Element;\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}({s1}_{j1k}-{s1}_{\mathrm{ik}}),$ I ∈ Set (Students), j1 ∈ Set (ClassA), ${\mathrm{\&mu;}}_{\mathrm{ik}}=\left\{\begin{array}{cc}\mathrm{\&alpha;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelA}\\ \mathrm{\&beta;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelB}\\ ...& ...\end{array}\right.,$ Described Set (Students) is user Set, Students={u₁,u₂,u₃,...,u_n, u_nFor nth user, described Set (ClassA) is the comprehensive of school grade Rank is the set of the superior users of A class, and Set (Knowledge) is the set of knowledge point, Knowledge={k₁,k₂,k₃,...,k_m, k_mFor m-th knowledge point, s1_j1kFor the operation achievement of the knowledge point k of user j1, s1_ikBeing the operation achievement of the knowledge point k of the second user i, Level is operation achievement s1 of the knowledge point k of described second user i_ik Corresponding individual event rank, LevelA is outstanding, and LevelB is good, μ_ikFor weighting parameter, α is the number more than 1, and β is Number more than α；The first user selecting ranking to be front N；The operation result of the second pre-defined algorithm is carried out sort descending, wherein, Described second pre-defined algorithm is: $\mathrm{Opereation}12(i,j2)=\underset{k\&Element;\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}{\mathrm{\&theta;}}_{j2k},$ J2 ∈ Set (topN (resule (operation11) ↓)), Set (topN (result (operation11) ↓)) are to make a reservation for described first The operation result of algorithm carries out sort descending, and ranking is the set of the first user composition of front N, θ_j2kUpload for first user j2 The value parameter of the study notes about knowledge point k；The study heart of the first user of the first predetermined quantity that selected and sorted is the most forward ?.

Alternatively, described second unit is selected to include: to utilize the 3rd pre-defined algorithm one to screen M the study starting stage and described the The study ability to accept difference of two users is less than the first user of predetermined value, and wherein, described 3rd pre-defined algorithm one is: $\mathrm{Opereation}21(i,j3)=\underset{k\&Element;\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}|{s1}_{j3k}-{s1}_{\mathrm{ik}}|,$ I ∈ Set (Students), j3 ∈ Set (Class (i)), ${\mathrm{\&mu;}}_{\mathrm{ik}}=\left\{\begin{array}{cc}\mathrm{\&alpha;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelA}\\ \mathrm{\&beta;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelB}\\ ...& ...\end{array}\right.,$ Described Set (Students) is the set of user, Students={u₁,u₂,u₃,...,u_n, u_nFor nth user, Set (Class (i)) is the comprehensive rank set with described second user identical for user i of school grade, Set (Knowledge) is the set of knowledge point, Knowledge={k₁,k₂,k₃,...,k_m, k_mFor m-th knowledge point, s1_j3k For the operation achievement of the knowledge point k of user j3, s1_ikBeing the operation achievement of the knowledge point k of the second user i, Level is described Operation achievement s1 of the knowledge point k of two user i_ikCorresponding individual event rank, LevelA is outstanding, and LevelB is good, μ_ikFor Weighting parameter, α is the number more than 1, and β is the number more than α；Utilize the 3rd pre-defined algorithm two from described M first user Screening L first user, wherein, described 3rd pre-defined algorithm two is: $\mathrm{Opereation}22(i,j4)=\underset{k\&Element;\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}({s2}_{j4k}-{s2}_{\mathrm{ik}}),$ Wherein, J4 ∈ Set (topM (result (operation 21) ↑)), Set (topM (result (operation 21) ↑)) are to described The operation result of three pre-defined algorithms carries out sort ascending, and ranking is the set of the first user composition of front M, s2_j4kFor user j4's The new operation achievement of knowledge point k, s2_ikIt it is the new operation achievement of the knowledge point k of the second user i；To the 4th pre-defined algorithm Operation result carries out sort descending, and wherein, described 4th pre-defined algorithm is:J5 ∈ Set (topL (resule (operation22) ↓)), θ_j5kFor The value parameter of the study notes about knowledge point k that first user j5 uploads；The second predetermined quantity that selected and sorted is the most forward The study notes of first user.

Alternatively, described match parameter includes at least one of: the knowledge point interested that described second user specifies, described The user interested that second user specifies, the study notes that described second user specifies are worth threshold value, and the described 3rd selects unit Including: the operation result of the 5th pre-defined algorithm is carried out sort descending, and wherein, described 5th pre-defined algorithm is: $\mathrm{Opereation}31(i,j6)=\underset{k\&Element;\mathrm{SOI}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}({s1}_{j6k}-{s1}_{\mathrm{ik}}),$ Wherein, i ∈ Set (Students), J6 ∈ SOI (Students), ${\mathrm{\&mu;}}_{\mathrm{ik}}=\left\{\begin{array}{cc}\mathrm{\&alpha;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelA}\\ \mathrm{\&beta;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelB}\\ ...& ...\end{array}\right.,$ Described Set (Students) is the set of user, Students={u₁,u₂,u₃,...,u_n, u_nFor nth user, SOI (Knowledge) is that the sense that described second user specifies is emerging The knowledge point set of interest, described SOI (Students) is user's set interested that described second user specifies, s1_j6kFor with The operation achievement of the knowledge point k of family j6, s1_ikBeing the operation achievement of the knowledge point k of the second user i, Level is described second use Operation achievement s1 of the knowledge point k of family i_ikCorresponding individual event rank, LevelA is outstanding, and LevelB is good, μ_ikFor weights Parameter, α is the number more than 1, and β is the number more than α；The Q name first user that selecting sequence is the most forward；To the 6th predetermined calculation The operation result of method carries out sort descending, and wherein, described 6th pre-defined algorithm is: $\mathrm{Opereation}32(i,j7)=\underset{k\&Element;\mathrm{SOI}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{j7k}{\mathrm{\&theta;}}_{j7k},$ Wherein, j7 ∈ Set (topQ (resule (operation31) ↓)), ${\mathrm{\&mu;}}_{j7k}=\left\{\begin{array}{cc}0& {\mathrm{\&theta;}}_{j7k}<{\mathrm{\&theta;}}_{\min }\\ 1& {\mathrm{\&theta;}}_{j7k}>{\mathrm{\&theta;}}_{\min }\end{array}\right.,$ Set (topQ (resule (operation31) ↓)) is the knot of the computing to described 5th pre-defined algorithm Fruit carries out sort descending, and ranking is the set of the first user composition of front Q, μ_j7kFor weight parameter, θ_j7kFor first user j7 The value parameter of the study notes about knowledge point k uploaded, θ_minThe study notes specified for described second user are worth threshold value； The study notes of the first user of the 3rd predetermined quantity that selected and sorted is the most forward.

By the present invention, use the study notes recording one or more first user in the way of audio frequency and video；Selection meets predetermined The study notes of one or more first users of condition；The study notes of the one or more first user selected are recommended To the second user, solve user present in correlation technique and can only cause feedback efficiency with the form feedback learning gains in depth of comprehension of word Low, and the loaded down with trivial details very long problem of process of search study notes, and then reached raising study notes feedback efficiency, reduce and learn Practise the effect of the loaded down with trivial details degree of gains in depth of comprehension search.

Accompanying drawing explanation

Accompanying drawing described herein is used for providing a further understanding of the present invention, constitutes the part of the application, and the present invention shows Meaning property embodiment and explanation thereof are used for explaining the present invention, are not intended that inappropriate limitation of the present invention.In the accompanying drawings:

Fig. 1 is the flow chart that study notes according to embodiments of the present invention recommend method；

Fig. 2 is the structured flowchart of study notes recommendation apparatus according to embodiments of the present invention；

Fig. 3 is the structured flowchart selecting module 24 in study notes recommendation apparatus according to embodiments of the present invention；

Fig. 4 is the structural representation that intelligent remote educational system according to embodiments of the present invention forms substantially；

Fig. 5 be middle school student's recording of growing up administrative unit 22 according to embodiments of the present invention be the growth that each student sets up and safeguards Log set schematic diagram.

Detailed description of the invention

Below with reference to accompanying drawing and describe the present invention in detail in conjunction with the embodiments.It should be noted that in the case of not conflicting, Embodiment in the application and the feature in embodiment can be mutually combined.

It should be noted that term " first " in description and claims of this specification and above-mentioned accompanying drawing, " second " etc. are to use In the object that difference is similar, without being used for describing specific order or precedence.

Providing a kind of study notes in the present embodiment and recommend method, Fig. 1 is that study notes according to embodiments of the present invention are recommended The flow chart of method, as it is shown in figure 1, this flow process comprises the steps:

Step S102, records the study notes of one or more first user in the way of audio frequency and video；

Step S104, selects the study notes of the one or more first users conformed to a predetermined condition；

The study notes of the one or more first users selected are recommended the second user by step S106.

By above-mentioned steps, when recording learning gains in depth of comprehension, record can be carried out in the way of audio frequency and video, say, that Yong Hu During feedback learning gains in depth of comprehension, be to carry out feeding back in the way of audio frequency and video, thus overcome be only capable of present in correlation technique with The study notes of written form feedback user and the low problem of the feedback efficiency that causes, and, need to search for study notes user Time, study notes can be recommended according to some predetermined conditions to user, thus reduce the loaded down with trivial details degree of search study notes, Thus improve record efficiency and the search efficiency of study notes.

In an optional embodiment, the study notes of the one or more first users conformed to a predetermined condition are selected to include following At least one: select the study notes of one or more first users got good marks, wherein, this get good marks into School grade is more than the first predetermined condition；Selection school grade progress exceedes the study of one or more first users of predetermined threshold Gains in depth of comprehension；The match parameter provided according to the second user selects the study notes of one or more first users.It is to say, in choosing When selecting study notes, can select polytype study notes, certainly, the above-mentioned three kinds of systems of selection be given are only several showing Example, it is also possible to use other mode to select study notes.

In an optional embodiment, the study notes of the one or more first users got good marks are selected to include: right The operation result of the first pre-defined algorithm carries out sort descending；Wherein, this first pre-defined algorithm is: $\mathrm{Opereation}11(i,j1)=\underset{k\&Element;\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}({s1}_{j1k}-{s1}_{\mathrm{ik}}),$ I ∈ Set (Students), j1 ∈ Set (ClassA), ${\mathrm{\&mu;}}_{\mathrm{ik}}=\left\{\begin{array}{cc}\mathrm{\&alpha;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelA}\\ \mathrm{\&beta;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelB}\\ ...& ...\end{array}\right.,$ Set (Students) is the set of user, Students={u₁,u₂,u₃,...,u_n, u_n For nth user, Set (ClassA) is the set of the superior users that comprehensive rank is A class of school grade, Set (Knowledge) is the set of knowledge point, Knowledge={k₁,k₂,k₃,...,k_m, k_mFor m-th knowledge point, s1_j1k For the operation achievement of the knowledge point k of user j1, s1_ikBeing the operation achievement of the knowledge point k of the second user i, Level is the second use Operation achievement s1 of the knowledge point k of family i_ikCorresponding individual event rank, LevelA is outstanding, and LevelB is good, μ_ikFor weights Parameter, α is the number more than 1, and β is the number more than α；The first user selecting ranking to be front N；To the second pre-defined algorithm Operation result carries out sort descending, and wherein, this second pre-defined algorithm is: $\mathrm{Opereation}12(i,j2)=\underset{k\&Element;\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}{\mathrm{\&theta;}}_{j2k},$ J2 ∈ Set (topN (resule (operation11) ↓)), Set (topN (result (operation11) ↓)) is to the first pre-defined algorithm Operation result carry out sort descending, ranking be front N first user composition set, θ_j2kThe pass uploaded for first user j2 Value parameter in the study notes of knowledge point k；The study notes of the first user of the first predetermined quantity that selected and sorted is the most forward.

In an optional embodiment, selection school grade progress exceedes the study of one or more first users of predetermined threshold Gains in depth of comprehension include: utilize the 3rd pre-defined algorithm one to screen M study starting stage and differ with the study ability to accept of the second user and be less than The first user of predetermined value, wherein, the 3rd pre-defined algorithm one is: $\mathrm{Opereation}21(i,j3)=\underset{k\&Element;\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}|{s1}_{j3k}-{s1}_{\mathrm{ik}}|,$ I ∈ Set (Students), j3 ∈ Set (Class (i)), ${\mathrm{\&mu;}}_{\mathrm{ik}}=\left\{\begin{array}{cc}\mathrm{\&alpha;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelA}\\ \mathrm{\&beta;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelB}\\ ...& ...\end{array}\right.,$ Set (Students) is the set of user, Students={u₁,u₂,u₃,...,u_n, u_n For nth user, Set (Class (i)) is the set of the user that the comprehensive rank of school grade is identical for user i with second, Set (Knowledge) is the set of knowledge point, Knowledge={k₁,k₂,k₃,...,k_m, k_mFor m-th knowledge point, s1_j3k For the operation achievement of the knowledge point k of user j3, s1_ikBeing the operation achievement of the knowledge point k of the second user i, Level is the second use Operation achievement s1 of the knowledge point k of family i_ikCorresponding individual event rank, LevelA is outstanding, and LevelB is good, μ_ikFor weights Parameter, α is the number more than 1, and β is the number more than α；The 3rd pre-defined algorithm two is utilized to screen L from M first user First user, wherein, the 3rd pre-defined algorithm two is: $\mathrm{Opereation}22(i,j4)=\underset{k\&Element;\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}({s2}_{j4k}-{s2}_{\mathrm{ik}}),$ Wherein, j4 ∈ Set (topM (result (operation 21) ↑)), Set (topM (result (operation 21) ↑)) is right The operation result of the 3rd pre-defined algorithm carries out sort ascending, and ranking is the set of the first user composition of front M, s2_j4kFor user j4 The new operation achievement of knowledge point k, s2_ikIt it is the new operation achievement of the knowledge point k of the second user i；To the 4th pre-defined algorithm Operation result carry out sort descending, wherein, the 4th pre-defined algorithm is: J5 ∈ Set (topL (resule (operation22) ↓)), θ_j5kThe study notes about knowledge point k uploaded for first user j5 Value parameter；The study notes of the first user of the second predetermined quantity that selected and sorted is the most forward.

In an optional embodiment, above-mentioned match parameter includes at least one of: interested the knowing that the second user specifies Knowing point, the user interested that the second user specifies, the study notes that the second user specifies are worth threshold value, according to this second user The match parameter provided selects the study notes of one or more first users to include: carry out the operation result of the 5th pre-defined algorithm Sort descending, wherein, the 5th pre-defined algorithm is: $\mathrm{Opereation}31(i,j6)=\underset{k\&Element;\mathrm{SOI}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}({s1}_{j6k}-{s1}_{\mathrm{ik}}),$ Wherein, i ∈ Set (Students), j6 ∈ SOI (Students), ${\mathrm{\&mu;}}_{\mathrm{ik}}=\left\{\begin{array}{cc}\mathrm{\&alpha;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelA}\\ \mathrm{\&beta;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelB}\\ ...& ...\end{array}\right.,$ Set(Students) For the set of user, Students={u₁,u₂,u₃,...,u_n, u_nFor nth user, SOI (Knowledge) is the second user The knowledge point set interested specified, SOI (Students) is user's set interested that the second user specifies, s1_j6kFor The operation achievement of the knowledge point k of user j6, s1_ikBeing the operation achievement of the knowledge point k of the second user i, Level is the second user i Operation achievement s1 of knowledge point k_ikCorresponding individual event rank, LevelA is outstanding, and LevelB is good, μ_ikJoin for weights Number, α is the number more than 1, and β is the number more than α；The Q name first user that selecting sequence is the most forward；To the 6th pre-defined algorithm Operation result carry out sort descending, wherein, the 6th pre-defined algorithm is: $\mathrm{Opereation}32(i,j7)=\underset{k\&Element;\mathrm{SOI}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{j7k}{\mathrm{\&theta;}}_{j7k},$ Wherein, j7 ∈ Set (topQ (resule (operation31) ↓)), ${\mathrm{\&mu;}}_{j7k}=\left\{\begin{array}{cc}0& {\mathrm{\&theta;}}_{j7k}<{\mathrm{\&theta;}}_{\min }\\ 1& {\mathrm{\&theta;}}_{j7k}>{\mathrm{\&theta;}}_{\min }\end{array}\right.,$ Set (topQ (resule (operation31) ↓)) is that the operation result to the 5th pre-defined algorithm enters Row sort descending, ranking is the set of the first user composition of front Q, μ_j7kFor weight parameter, θ_j7kUpload for first user j7 The value parameter of the study notes about knowledge point k, θ_minIt is that the study notes that the second user specifies are worth threshold value；Selected and sorted The study notes of the first user of the 3rd the most forward predetermined quantity.

Through the above description of the embodiments, those skilled in the art is it can be understood that arrive the side according to above-described embodiment Method can add the mode of required general hardware platform by software and realize, naturally it is also possible to by hardware, but a lot of in the case of before Person is more preferably embodiment.Based on such understanding, prior art is made tribute by technical scheme the most in other words The part offered can embody with the form of software product, this computer software product be stored in a storage medium (as ROM/RAM, magnetic disc, CD) in, including some instructions with so that a station terminal equipment (can be mobile phone, computer, Server, or the network equipment etc.) perform the method described in each embodiment of the present invention.

Additionally providing a kind of study notes recommendation apparatus in the present embodiment, this device is used for realizing above-described embodiment and being preferable to carry out Mode, had carried out repeating no more of explanation.As used below, term " module " can realize the soft of predetermined function Part and/or the combination of hardware.Although the device described by following example preferably realizes with software, but hardware, or soft The realization of the combination of part and hardware also may and be contemplated.

Fig. 2 is the structured flowchart of study notes recommendation apparatus according to embodiments of the present invention, as in figure 2 it is shown, this device includes note Record module 22, selection module 24 and recommending module 26, illustrate this device below.

Logging modle 22, for recording the study notes of one or more first user in the way of audio frequency and video；Select module 24, It is connected to above-mentioned logging modle 22, for selecting the study notes of the one or more first users conformed to a predetermined condition；Recommend mould Block 26, is connected to above-mentioned selection module 24, for the study notes of the one or more first users selected are recommended the second use Family.

Fig. 3 is the structured flowchart selecting module 24 in study notes recommendation apparatus according to embodiments of the present invention, as it is shown on figure 3, This selection module 24 include the first selection unit 32, second select unit 34 and the 3rd select in unit 36 at least one, under In the face of this selection module 24 illustrates:

First selects unit 32, for selecting the study notes of the one or more first users got good marks, wherein, learns Practise in good standing for school grade more than the first predetermined condition；Second selects unit 34, is used for selecting school grade progress to exceed pre- Determine the study notes of one or more first users of threshold value；3rd selects unit 36, for the coupling provided according to the second user Parameter selects the study notes of one or more first users.

Alternatively, above-mentioned first unit 32 is selected to include: the operation result of the first pre-defined algorithm is carried out sort descending；Wherein, This first pre-defined algorithm is: $\mathrm{Opereation}11(i,j1)=\underset{k\&Element;\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}({s1}_{j1k}-{s1}_{\mathrm{ik}}),$ I ∈ Set (Students), j1 ∈ Set (ClassA), ${\mathrm{\&mu;}}_{\mathrm{ik}}=\left\{\begin{array}{cc}\mathrm{\&alpha;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelA}\\ \mathrm{\&beta;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelB}\\ ...& ...\end{array}\right.,$ Set (Students) is the collection of user Close, Students={u₁,u₂,u₃,...,u_n, u_nFor nth user, Set (ClassA) be the comprehensive rank of school grade be A The set of the superior users of class, Set (Knowledge) is the set of knowledge point, Knowledge={k₁,k₂,k₃,...,k_m, k_m For m-th knowledge point, s1_j1kFor the operation achievement of the knowledge point k of user j1, s1_ikIt it is the operation of the knowledge point k of the second user i Achievement, Level is operation achievement s1 of the knowledge point k of the second user i_ikCorresponding individual event rank, LevelA is outstanding, LevelB For well, μ_ikFor weighting parameter, α is the number more than 1, and β is the number more than α；The first user selecting ranking to be front N； The operation result of the second pre-defined algorithm is carried out sort descending, and wherein, the second pre-defined algorithm is: $\mathrm{Opereation}12(i,j2)=\underset{k\&Element;\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}{\mathrm{\&theta;}}_{j2k},$ J2 ∈ Set (topN (resule (operation11) ↓)), Set (topN (result (operation11) ↓)) is that the operation result to the first pre-defined algorithm carries out sort descending, and ranking is front N First user composition set, θ_j2kValue parameter for the study notes about knowledge point k that first user j2 uploads；Choosing Select the study notes of the first user of the first the most forward predetermined quantity of sequence.

Alternatively, above-mentioned second unit 34 is selected to include: to utilize the 3rd pre-defined algorithm one to screen M study starting stage and second The study ability to accept difference of user is less than the first user of predetermined value, and wherein, the 3rd pre-defined algorithm one is: $\mathrm{Opereation}21(i,j3)=\underset{k\&Element;\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}|{s1}_{j3k}-{s1}_{\mathrm{ik}}|,$ I ∈ Set (Students), j3 ∈ Set (Class (i)), ${\mathrm{\&mu;}}_{\mathrm{ik}}=\left\{\begin{array}{cc}\mathrm{\&alpha;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelA}\\ \mathrm{\&beta;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelB}\\ ...& ...\end{array}\right.,$ Set (Students) is the set of user, Students={u₁,u₂,u₃,...,u_n, u_n For nth user, Set (Class (i)) is the set of the user that the comprehensive rank of school grade is identical for user i with second, Set (Knowledge) is the set of knowledge point, Knowledge={k₁,k₂,k₃,...,k_m, k_mFor m-th knowledge point, s1_j3k For the operation achievement of the knowledge point k of user j3, s1_ikBeing the operation achievement of the knowledge point k of the second user i, Level is the second use Operation achievement s1 of the knowledge point k of family i_ikCorresponding individual event rank, LevelA is outstanding, and LevelB is good, μ_ikFor weights Parameter, α is the number more than 1, and β is the number more than α；The 3rd pre-defined algorithm two is utilized to screen L from M first user First user, wherein, the 3rd pre-defined algorithm two is: $\mathrm{Opereation}22(i,j4)=\underset{k\&Element;\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}({s2}_{j4k}-{s2}_{\mathrm{ik}}),$ Wherein, j4 ∈ Set (topM (result (operation 21) ↑)), Set (topM (result (operation 21) ↑)) is right The operation result of the 3rd pre-defined algorithm carries out sort ascending, and ranking is the set of the first user composition of front M, s2_j4kFor user j4 The new operation achievement of knowledge point k, s2_ikIt it is the new operation achievement of the knowledge point k of the second user i；To the 4th pre-defined algorithm Operation result carry out sort descending, wherein, the 4th pre-defined algorithm is: J5 ∈ Set (topL (resule (operation22) ↓)), θ_j5kThe study notes about knowledge point k uploaded for first user j5 Value parameter；The study notes of the first user of the second predetermined quantity that selected and sorted is the most forward.

Alternatively, above-mentioned match parameter includes at least one of: the knowledge point interested that the second user specifies, the second user The user interested specified, study notes that the second user specifies are worth threshold value, and the above-mentioned 3rd selects unit 36 to include: to the The operation result of five pre-defined algorithms carries out sort descending, and wherein, the 5th pre-defined algorithm is: $\mathrm{Opereation}31(i,j6)=\underset{k\&Element;\mathrm{SOI}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}({s1}_{j6k}-{s1}_{\mathrm{ik}}),$ Wherein, i ∈ Set (Students), J6 ∈ SOI (Students), ${\mathrm{\&mu;}}_{\mathrm{ik}}=\left\{\begin{array}{cc}\mathrm{\&alpha;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelA}\\ \mathrm{\&beta;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelB}\\ ...& ...\end{array}\right.,$ Set (Students) is the set of user, Students={u₁,u₂,u₃,...,u_n, u_nFor nth user, SOI (Knowledge) be the second user specify interested Knowledge point is gathered, and SOI (Students) is user's set interested that the second user specifies, s1_j6kKnowledge point k for user j6 Operation achievement, s1_ikBeing the operation achievement of the knowledge point k of the second user i, Level is the operation of the knowledge point k of the second user i Achievement s1_ikCorresponding individual event rank, LevelA is outstanding, and LevelB is good, μ_ikFor weighting parameter, α is more than 1 Number, β is the number more than α；The Q name first user that selecting sequence is the most forward；The operation result of the 6th pre-defined algorithm is passed Reducing discharging sequence, wherein, the 6th pre-defined algorithm is: $\mathrm{Opereation}32(i,j7)=\underset{k\&Element;\mathrm{SOI}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{j7k}{\mathrm{\&theta;}}_{j7k},$ Wherein, J7 ∈ Set (topQ (resule (operation31) ↓)), ${\mathrm{\&mu;}}_{j7k}=\left\{\begin{array}{cc}0& {\mathrm{\&theta;}}_{j7k}<{\mathrm{\&theta;}}_{\min }\\ 1& {\mathrm{\&theta;}}_{j7k}>{\mathrm{\&theta;}}_{\min }\end{array}\right.,$ Set (topQ (resule (operation31) ↓)) is that the operation result to the 5th pre-defined algorithm carries out sort descending, and ranking is front Q First user composition set, μ_j7kFor weight parameter, θ_j7kThe study heart about knowledge point k uploaded for first user j7 The value parameter obtained, θ_minIt is that the study notes that the second user specifies are worth threshold value；The 3rd predetermined quantity that selected and sorted is the most forward The study notes of first user.

It is illustrated with above-mentioned user for student below.For the problems referred to above present in correlation technique, the embodiment of the present invention In additionally provide a kind of based on student's gains in depth of comprehension feedback and recommend intelligent remote educational system.This system is by using video-with-audio recording Student learning process and gains in depth of comprehension also carry out binding management with knowledge point, it is possible to directly effectively feed back the situation of student.It is simultaneously Effectively utilizing the learning process resource that magnanimity student is huge, native system also proposed three kinds of proposed algorithms, by students'growth track Coupling, provide personalized gains in depth of comprehension to recommend for the student of different levels.

Should be based on student's gains in depth of comprehension feedback and the intelligent remote educational system recommended, including teaching subsystem and learning management subsystem, Wherein:

Teaching subsystem is mainly responsible for the plan of long-distance education mechanism side course, is designed and realize, including course management unit, matchmaker Body service unit.

Course management unit, management training coarse relevant information, mainly include the appointment of curriculum schedule, update and predict, course is relevant to be known Know foundation and the maintenance of point.

Media services unit, teaching system mainly propagates educational information, media services list by the multimedia bearer such as text, phonotape and videotape Unit is responsible for creating and safeguarding the media services environment of teaching system.

Above-mentioned text refers mainly to the Courseware Resource of textual form, each says corresponding a Courseware Resource, downloads for student or online Browse study.

Phonotape and videotape refers to the network classroom that synchronizes and asynchronous two kinds of audio frequency and video of video request program are given lessons mode.

Network classroom mode refers to that teacher teaches in special configuration has the network classroom of audio frequency, video acquisition and coding and decoding device, And by network system, audio-video signal is sent in real time each station terminal of the network classroom.

Video request program mode refers to, by the recording of above-mentioned process of giving lessons in real time and compression, utilize stream media technology, be available for student and carry out Program request learns.

Learning management subsystem, is responsible for record and management student learning situation, provides personalized study growth service for student, It is made up of student's test cell, recording of growing up administrative unit, personalized recommendation unit.

Student's test and management unit, the main management being responsible for exam pool and test and appraisal.Wherein exam pool is divided into operation exam pool and examination pool.

Operation exam pool refers to that each subject is maintain course correlated knowledge point set A, set A by above-mentioned course management unit and has one The individual operation exam pool B mapped one by one, the most each knowledge point is to having a set of operation exam pool for surveying after having learnt a knowledge point Comment.

Examination pool refers to, subject set C has an examination pool set D mapped one by one, corresponding one of the most each subject Examination pool, for carrying out comprehensive examination test and appraisal after complete subject of study.

Growth management record unit, sets up and maintains a recording of growing up table set for each student, including with knowledge point set Close the subject that interior element is major key to learn track table, gather the interior element gains in depth of comprehension information table as major key with knowledge point, with subject set Interior element is the comprehensive test table of major key.

The attribute of subject study track table includes, knowledge point identifies, operation trials achievement 1 is correlated with in knowledge point, operation is correlated with in knowledge point Test result 2.

The attribute of gains in depth of comprehension information table includes, knowledge point mark, gains in depth of comprehension link, gains in depth of comprehension key word, gains in depth of comprehension value coefficient.Particularly, The gains in depth of comprehension link of one knowledge point mark correspondence can be empty, and gains in depth of comprehension value coefficient is defaulted as 0；When first renewal gains in depth of comprehension link, Gains in depth of comprehension value coefficient is initialized as 1.

The attribute of comprehensive test table includes, subject mark, subject total marks of the examination, subject examination make-up examination achievement.

Growth management record unit is also responsible for carrying out capability evaluation, long-distance education mechanism formulate category level and standard thereof, one Corresponding individual event rank Level of the operation achievement of one knowledge point of student, the operation achievement collection of all knowledge points of a student Close and one comprehensive rank Class of subject assessment of examination result.

Personalized recommendation unit (recommending module 26 with above-mentioned), it is provided that three kinds of proposed algorithms, recommends to calculate including Ontario Scholar's gains in depth of comprehension Method, progressive obvious student's gains in depth of comprehension proposed algorithm, retrieval proposed algorithm, and recommendation feedback is provided.

Ontario Scholar's gains in depth of comprehension proposed algorithm, when referring to recommend gains in depth of comprehension into any one student i (the second user i with above-mentioned), with Student i is reference, and the Ontario Scholar's collection with comprehensive rank Class as A is combined into set to be matched, mates outstanding targetedly Raw gains in depth of comprehension are recommended.The strong and weak situation that knowledge point is understood with student i by Ontario Scholar's gains in depth of comprehension proposed algorithm as reference, main point two Point: the assessment (the first pre-defined algorithm with above-mentioned) of Ontario Scholar, the assessment of Ontario Scholar's gains in depth of comprehension (are made a reservation for above-mentioned second Algorithm).

Wherein, with student i for reference to the assessment such as following formula to Ontario Scholar:

$\mathrm{Opereation}11(i,j1)=\underset{k\&Element;\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}({s1}_{j1k}-{s1}_{\mathrm{ik}})$

i∈Set(Students),j1∈Set(ClassA)

${\mathrm{\&mu;}}_{\mathrm{ik}}=\left\{\begin{array}{cc}\mathrm{\&alpha;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelA}\\ \mathrm{\&beta;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelB}\\ ...& ...\end{array}\right.,1<\mathrm{\&alpha;}<\mathrm{\&beta;}<...$

Computing Opereation 11 (i, j1), refers to the subject study track table knowledge point set with reference to student i Element in Set (Knowledge) is accumulated variables, to the arbitrary student j1 in Ontario Scholar's set that comprehensive rank is A class, Take corresponding operation achievement s1 is weighted difference summation operation, wherein weights μ_ikThe individual event rank corresponding by the operation achievement of student i Level determines, rank is the lowest, and weights are the biggest, so can lay particular emphasis on the knowledge point set with reference to student i is more weak and screen.Right Computing Opereation 11 result presses sort descending, and the most forward is poor relative to the knowledge point grasp situation forward with reference to student i The student that different value is the biggest, takes the most forward some Ontario Scholars, carries out the screening of gains in depth of comprehension.

Wherein, in above-mentioned filtered out Ontario Scholar, the assessment of gains in depth of comprehension such as following formula:

$\mathrm{Opereation}12(i,j2)=\underset{k\&Element;\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}{\mathrm{\&theta;}}_{j2k}$

Computing Opereation 12 (i, j2) refers to the subject study track table knowledge point set with reference to student i Element in Set (Knowledge) is accumulated variables, to appointing in the Ontario Scholar's set filtered out by Opereation 11 One student j2, takes corresponding gains in depth of comprehension value coefficient θ_jkDo sum operation with coefficient, wherein weights μ_ikBy the operation achievement pair of student i Individual event rank Level answered determines, rank is the lowest, and weights are the biggest.Result is pressed sort descending, takes the most forward some students' Gains in depth of comprehension are as being pushed to student i.

Above-mentioned progress obvious student gains in depth of comprehension proposed algorithm, when referring to recommend gains in depth of comprehension into any one student i, with student i as reference, Being combined into set to be matched with student's collection that comprehensive rank Class is identical, the gains in depth of comprehension of the progressive obvious student of coupling are recommended targetedly. The strong and weak situation that knowledge point is understood with student i by the gains in depth of comprehension proposed algorithm of progressive obvious student, as reference, mainly divides two parts: to The raw progressive assessment (the 3rd pre-defined algorithm the one and the 3rd pre-defined algorithm two with above-mentioned) of situation, outstanding gains in depth of comprehension assessment (with above-mentioned The 4th pre-defined algorithm).

Wherein, with student i for the assessment such as following formula with reference to situation progressive to student:

$\mathrm{Opereation}21(i,j3)=\underset{k\&Element;\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}|{s1}_{j3k}-{s1}_{\mathrm{ik}}|$

i∈Set(Students),j3∈Set(Class(i))

$\mathrm{Opereation}22(i,j4)=\underset{k\&Element;\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}({s2}_{j4k}-{s2}_{\mathrm{ik}})$

i∈Set(Students),j4∈Set(topM(result(operation 21)↑))

It can be seen that the assessment of situation progressive to student can be divided into two parts, first screening study starting stage to have with reference to student The student of similar study receiving ability, can press sort ascending by appeal set operation Opereation 21 result, the most forward For relative to the knowledge point with reference to student i grasp the least student of situation absolute value difference value, i.e. initial learn situation closer to Student；Then take the most forward some students, then operation achievement s2 of being correlated with knowledge point be set operation Opereation 22, The result of computing Opereation 22 is pressed sort descending, and the most forward is close with Students ' Learning reception degree and progressive the brightest Aobvious student, takes some students and is appeal gains in depth of comprehension assessment computing Opereation 23, $\mathrm{Opereation}23(i,j5)=\underset{k\&Element;\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}{\mathrm{\&theta;}}_{j5k},$ J5 ∈ Set (topL (resule (operation22) ↓)), θ_j5kFor The value parameter of the study notes about knowledge point k that first user j5 uploads；Of several students that selected and sorted is the most forward Practise gains in depth of comprehension to recommend.

Above-mentioned retrieval proposed algorithm is provided knowledge point interested to gather by student, student's set, gains in depth of comprehension sieveing coeffecient is (with above-mentioned Study notes be worth threshold value), replace subset carry out coupling recommend.This retrieval proposed algorithm is by utilizing the 5th pre-defined algorithm and Six pre-defined algorithms realize.Specifically refer to aforesaid about the 5th pre-defined algorithm and the description of the 6th pre-defined algorithm, go to live in the household of one's in-laws on getting married the most one by one at this State.

Above-mentioned recommendation feedback, it is provided that questionnaire survey interface, useful with the gains in depth of comprehension pushed of Students ' Feedback according to instructor Degree, recalculates the gains in depth of comprehension value coefficient in above-mentioned gains in depth of comprehension information table, with the priority of institute's content recommendation in renewal system.

Fig. 4 is the structural representation that intelligent remote educational system according to embodiments of the present invention forms substantially, as shown in Figure 4, and should System includes impart knowledge to students subsystem 1 and learning management subsystem 2.Existing long-distance educational system is improved by the embodiment of the present invention, By using video-with-audio recording student learning process and gains in depth of comprehension and carrying out binding management with knowledge point, feed back the feelings of student intuitively Condition, and propose three kinds of gains in depth of comprehension proposed algorithms, the student for different levels provides personalized gains in depth of comprehension to recommend, learning management subsystem The responsible record of system 2 and management student learning situation, provide personalized study to grow up for student and service, by student's test and management Unit 20, students'growth record management unit 22, personalized recommendation unit 24 form.Student's test and management unit 20, mainly It is responsible for management and the test and appraisal of exam pool.Wherein exam pool is divided into operation exam pool and examination pool.It is different from existing long-distance educational system to carry It is only used for student's selftest, itemized record of the present invention and the operation performance of analysis student for operation topic.The embodiment of the present invention For the corresponding a set of operation exam pool in each knowledge point that each subject curriculum is relevant, and provide twice test logging machine meeting, with instead Reflect the progressive situation of student.Especially, operation trials number of times is not limited by the embodiment of the present invention, and student can ask repeatedly Operation trials, the test result of twice before system record student.The knowledge point operation trials result of student and subject examination result by Students'growth record management unit 22 manages.Growth management record unit 22 is also responsible for carrying out capability evaluation, by long-distance education machine Category level and standard thereof are determined in body plan, corresponding individual event rank Level of the operation achievement of a knowledge point of a student, one The operation achievement set of all knowledge points of student and one comprehensive rank Class of subject assessment of examination result.

The knowledge point that the optional test of having done one's assignment of student is relevant, uploads oneself audio frequency and video to the understanding of this knowledge point, and by learning The raw word summary providing its audio frequency and video gains in depth of comprehension and key word, managed by students'growth record management unit 22.

Fig. 5 be middle school student's recording of growing up administrative unit 22 according to embodiments of the present invention be the growth that each student sets up and safeguards Log set schematic diagram, as it is shown in figure 5, this set include the comprehensive test table T1 with subject set interior element as major key, The gains in depth of comprehension information table set S12 that subject learns track table set S10, all subjects are corresponding that all subjects are corresponding.Wherein in T1 One subject mark all learns track table to a subject with subject knowledge point set interior element as major key in should having set S10 A gains in depth of comprehension information table T12 with subject knowledge point set interior element as major key in T10, and set S12.

In order to effectively utilize the huge study notes resource of magnanimity student, native system proposes three kinds of proposed algorithms, for different levels Student provide personalized gains in depth of comprehension to recommend.Recommend 2400 including Ontario Scholar's gains in depth of comprehension, progressive obvious student's gains in depth of comprehension recommend 2402, Retrieval recommendation 2404.For being more fully described the proposed algorithm of the present invention, the master data of the embodiment of the present invention will be first described below Definition.

If student's collection is combined into:

Students={u₁,u₂,u₃,...,u_n}

If the knowledge point collection that subject is relevant is combined into:

Knowledge={k₁,k₂,k₃,...,k_m}

If the relevant operation achievement 1 of the knowledge point k of a student i is s1_ik, operation achievement 2 be s2_ik。

If operation achievement s of a student i knowledge point k_ikA corresponding individual event rank is expressed as Level, wherein LevelA is outstanding, and LevelB is good, by that analogy.

If the operation achievement set of all knowledge points of a student i and one comprehensive rank of subject assessment of examination result are expressed as Class, wherein ClassA is outstanding, and ClassB is good, by that analogy.

If the value coefficient of the gains in depth of comprehension about knowledge point k that student i uploads is θ_ik。

If collection share Set and represents, as Set (Knowledge) be knowledge point set, Set (ClassA) be overall merit be outstanding Student set.Especially, interesting set share SOI and represents, if SOI (Knowledge) is knowledge point set interested, SOI (Knowledge) is the subset of Set (Knowledge).

The personalized Ontario Scholar's gains in depth of comprehension proposed algorithm used the intelligent remote educational system in the embodiment of the present invention below is said Bright: when recommending gains in depth of comprehension for any one student i, first, learn track table T10 as reference with the subject of student i, take comprehensive level Other Class is that Ontario Scholar's collection of A class is combined into set to be matched, and the subject taking each student j in set to be matched learns track Table, with the corresponding set operation Operation11 that is of student i, the unit in i.e. gathering with subject study track table T10 knowledge point Element is accumulated variables, corresponding operation achievement s1 is done weighted difference summation operation, wherein by weights μ_ikBecome by the operation of student i Individual event rank Level that achievement is corresponding determines, rank is the lowest, and weights are the biggest, so can lay particular emphasis on knowledge point more weak for student i and do Personalized coupling is recommended.Then, set operation Operation11 result being pressed sort descending, the most forward variable j1 is corresponding Student is to learn track for reference to the most outstanding student, taking the most forward N number of student and form new collection to be matched with the subject of student i Close.Take the gains in depth of comprehension value coefficient θ of the gains in depth of comprehension information table T12 of each student j2 in new set_j2kSet operation is done in the set of composition Operation12, is i.e. weighted sum computing, the most each weights μ to the element in the set of knowledge point gains in depth of comprehension value coefficient_ikBy This knowledge point corresponding for student i individual event rank corresponding to operation achievement s1 of being correlated with determines, rank is the lowest, and weights are the biggest.Finally to collection Closing computing Operation12 result and press sort descending, the most forward student corresponding for variable j is to learn track with the subject of student i For reference, gains in depth of comprehension are worth the highest student, and the gains in depth of comprehension of the student taking the first the most forward predetermined quantity push as recommendation results R To student i.

Below the personalization progress obvious student gains in depth of comprehension proposed algorithm used in embodiment of the present invention intelligent remote educational system is carried out Explanation.When recommending gains in depth of comprehension for any one student i, first, learn track table T10 as reference with the subject of student i, with Student's collection that raw comprehensive rank Class is identical is combined into set to be matched, and the subject taking each student j in set to be matched learns Track table, with student i to being corresponding set operation Operation21, i.e. knowledge point dependence test achievement s1 is done weighting absolutely To difference summation operation.Then, set operation Operation12 result being pressed sort ascending, the most forward variable j3 is corresponding Student be with the subject of student i learn track for reference learning situation closer to student, take M the most forward student and form newly Set to be matched, take the subject study track table of each student j4 in new set, with student i corresponding gather fortune to doing Calculate Operation22, i.e. with the element in subject study track table T10 knowledge point set as accumulated variables, to corresponding operation Achievement s2 do weighted difference summation operation, the result of computing Operation22 is pressed sort descending, the most forward is and student learns Practise reception degree close to and the most obvious progressive student, take the most forward some students (L student can be selected), with gains in depth of comprehension letter Breath table T12 is coupling set, then is gains in depth of comprehension matching operation Operation23, finally takes the knot of set operation Operation23 Really the most forward after sort descending student is as recommendation.

The retrieval gains in depth of comprehension proposed algorithm used embodiment of the present invention intelligent remote educational system below illustrates.Thered is provided by student Knowledge point set SOI (Knowledge), student interested gather SOI (Students), gains in depth of comprehension sieveing coeffecient θ_min, replace Subset carries out coupling and recommends.

The content pushed can be evaluated, according to instructor and student by student by recommending the questionnaire survey interface of feedback unit The useful degree of the gains in depth of comprehension pushed of feedback, recalculates the gains in depth of comprehension value coefficient θ of above-mentioned gains in depth of comprehension information table T12, with more The priority of institute's content recommendation in new system.

The technical scheme provided by above-described embodiment, it is possible to achieve with the intelligent recommendation long-distance educational system of gains in depth of comprehension feedback.Religion Teacher and student carry out telecommunication either synchronously or asynchronously by teaching subsystem, it is achieved remotely give lessons.Each hall is given lessons and is terminated junior scholar The study situation of courses taken is tested and assessed by life by test and appraisal module, optionally will learn situation, including solving a problem after test and appraisal Process, study notes, record into audio frequency and video and bind its correlated knowledge point and be uploaded to growth management record Single Component Management.Learning management Subsystem is that each student creates and maintain recording of growing up table set, itemized record and tracking student learning situation.Logical Cross three kinds of proposed algorithms of personalized recommendation unit, it is possible to strong and weak situation knowledge point understood according to the student of record, for its amount Body mates study notes targetedly, and by recommending feedback with evaluation gains in depth of comprehension to be worth, real-time update system recommendation content preferential Level.

It should be noted that above-mentioned modules can be by software or hardware realizes, for the latter, can by with Under type realizes, but is not limited to this: above-mentioned module is respectively positioned in same processor；Or, above-mentioned module lays respectively at multiple place In reason device.

Embodiments of the invention additionally provide a kind of storage medium.Alternatively, in the present embodiment, above-mentioned storage medium can be by It is set to storage for the program code performing following steps:

S1, records the study notes of one or more first user in the way of audio frequency and video；

S2, selects the study notes of the one or more first users conformed to a predetermined condition；

The study notes of the one or more first users selected are recommended the second user by S3.

Alternatively, in the present embodiment, above-mentioned storage medium can include but not limited to: USB flash disk, read only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), mobile hard The various media that can store program code such as dish, magnetic disc or CD.

Alternatively, the concrete example in the present embodiment is referred to the example described in above-described embodiment and optional embodiment, The present embodiment does not repeats them here.

The embodiment of the present invention is by using video-with-audio recording student learning process and gains in depth of comprehension and carrying out binding management, energy with knowledge point Enough situations directly effectively feeding back student, rely on the student's quantity considerably beyond teacher in long-distance educational system, it is possible to form Pang Big education resource.Three kinds of proposed algorithms that the present invention proposes simultaneously, it is possible to effectively utilize the learning process that magnanimity student is huge Resource, by learning the coupling of track, it is possible to the strong and weak situation understood knowledge point according to the student of record, mates for its amount body Study notes targetedly, for its study for reference, improve the learning efficiency significantly.In addition different levels Students ' Learning During the knowledge point paid close attention to, the problem run in derivation, is also a kind of feedback to teacher, it is possible to allow teacher in achievement Outside understand in teaching process the part needing to improve, make adjustment in time.

Obviously, those skilled in the art should be understood that each module of the above-mentioned present invention or each step can be with general calculating Device realizes, and they can concentrate on single calculating device, or is distributed on the network that multiple calculating device is formed, Alternatively, they can realize with calculating the executable program code of device, it is thus possible to be stored in storing device In perform by calculating device, and in some cases, can with the order being different from herein perform shown or described by step Suddenly, or they are fabricated to respectively each integrated circuit modules, or the multiple modules in them or step are fabricated to single Integrated circuit modules realizes.So, the present invention is not restricted to the combination of any specific hardware and software.

The foregoing is only the preferred embodiments of the present invention, be not limited to the present invention, for those skilled in the art For, the present invention can have various modifications and variations.All within the spirit and principles in the present invention, any amendment of being made, etc. With replacement, improvement etc., should be included within the scope of the present invention.

Claims (10)

1. study notes recommend method, it is characterised in that including:

The study notes of one or more first user are recorded in the way of audio frequency and video；

Select the study notes of the one or more first users conformed to a predetermined condition；

The study notes of the one or more first user selected are recommended the second user.

Method the most according to claim 1, it is characterised in that one or more first users that selection conforms to a predetermined condition Study notes include at least one of:

Select the study notes of one or more first users got good marks, wherein, described in get good marks into School grade is more than the first predetermined condition；

Selection school grade progress exceedes the study notes of one or more first users of predetermined threshold；

The match parameter provided according to described second user selects the study notes of one or more first users.

Method the most according to claim 2, it is characterised in that one or more first users that selection gets good marks Study notes include:

The operation result of the first pre-defined algorithm is carried out sort descending；Wherein, described first pre-defined algorithm is: $\mathrm{Opereation}11(i,j1)=\underset{k\&Element;\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}({s1}_{j1k}-{s1}_{\mathrm{ik}}),i\&Element;\mathrm{Set}\left(\mathrm{Students}\right),j1\&Element;\mathrm{Set}\left(\mathrm{ClassA}\right),$ ${\mathrm{\&mu;}}_{\mathrm{ik}}=\left\{\begin{array}{cc}\mathrm{\&alpha;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelA}\\ \mathrm{\&beta;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelB}\\ ...& ...\end{array}\right.,$ Described Set (Students) is the set of user, Students={u₁,u₂,u₃,...,u_n, u_nFor nth user, described Set (ClassA) is that the comprehensive rank of school grade is The set of the superior users of A class, Set (Knowledge) is the set of knowledge point, Knowledge={k₁,k₂,k₃,...,k_m, k_mFor m-th knowledge point, s1_j1kOperation for the knowledge point k of user j1 Achievement, s1_ikBeing the operation achievement of the knowledge point k of the second user i, Level is the work of the knowledge point k of described second user i Industry achievement s1_ikCorresponding individual event rank, LevelA is outstanding, and LevelB is good, μ_ikFor weighting parameter, α is for being more than The number of 1, β is the number more than α；

The first user selecting ranking to be front N；

The operation result of the second pre-defined algorithm is carried out sort descending, and wherein, described second pre-defined algorithm is: $\mathrm{Opereation}12(i,j2)=\underset{k\&Element;\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}{\mathrm{\&theta;}}_{j2k},j2\&Element;\mathrm{Set}\left(\mathrm{topN}(\mathrm{resule}\left(\mathrm{operation}11\right)\&DownArrow;)\right),$ $\mathrm{Set}\left(\mathrm{topN}(\mathrm{result}\left(\mathrm{operation}11\right)\&DownArrow;)\right)$ For the operation result of described first pre-defined algorithm is carried out sort descending, ranking is The set of the first user composition of front N, θ_j2kValue parameter for the study notes about knowledge point k that first user j2 uploads； The study notes of the first user of the first predetermined quantity that selected and sorted is the most forward.

Method the most according to claim 2, it is characterised in that select school grade progress to exceed of predetermined threshold or many The study notes of individual first user include:

Utilize the 3rd pre-defined algorithm one screen M study the starting stage differ with the study ability to accept of described second user Less than the first user of predetermined value, wherein, described 3rd pre-defined algorithm one is: $\mathrm{Opereation}21(i,j3)=\underset{k\&Element;\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}|{s1}_{j3k}-{s1}_{\mathrm{ik}}|,i\&Element;\mathrm{Set}\left(\mathrm{Students}\right),$ $j3\&Element;\mathrm{Set}\left(\mathrm{Class}\left(i\right)\right),$ ${\mathrm{\&mu;}}_{\mathrm{ik}}=\left\{\begin{array}{cc}\mathrm{\&alpha;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelA}\\ \mathrm{\&beta;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelB}\\ ...& ...\end{array}\right.,$ Described Set (Students) is the set of user, Students={u₁,u₂,u₃,...,u_n, u_nFor nth user, Set (Class (i)) is comprehensive rank and the institute of school grade Stating the set of the second user identical for user i, Set (Knowledge) is the set of knowledge point, Knowledge={k₁,k₂,k₃,...,k_m, k_mFor m-th knowledge point, s1_j3kOperation for the knowledge point k of user j3 Achievement, s1_ikBeing the operation achievement of the knowledge point k of the second user i, Level is the work of the knowledge point k of described second user i Industry achievement s1_ikCorresponding individual event rank, LevelA is outstanding, and LevelB is good, μ_ikFor weighting parameter, α is for being more than The number of 1, β is the number more than α；

The 3rd pre-defined algorithm two is utilized to screen L first user from described M first user, wherein, described 3rd pre- Determining algorithm two is: $\mathrm{Opereation}22(i,j4)=\underset{k\&Element;\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}({s2}_{j4k}-{s2}_{\mathrm{ik}}),$ Wherein, J4 ∈ Set (topM (result (operation 21) ↑)), Set (topM (result (operation 21) ↑)) are The operation result of described 3rd pre-defined algorithm is carried out sort ascending, and ranking is the set of the first user composition of front M, s2_j4kFor The new operation achievement of the knowledge point k of user j4, s2_ikIt it is the new operation achievement of the knowledge point k of the second user i；

The operation result of the 4th pre-defined algorithm is carried out sort descending, and wherein, described 4th pre-defined algorithm is: $\mathrm{Opereation}23(i,j5)=\underset{k\&Element;\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}{\mathrm{\&theta;}}_{j5k},j5\&Element;\mathrm{Set}\left(\mathrm{topL}(\mathrm{resule}\left(\mathrm{operation}22\right)\&DownArrow;)\right),$ θ_j5kValue parameter for the study notes about knowledge point k that first user j5 uploads；

The study notes of the first user of the second predetermined quantity that selected and sorted is the most forward.

Method the most according to claim 2, it is characterised in that described match parameter includes at least one of: described second uses The knowledge point interested that family is specified, the user interested that described second user specifies, the study heart that described second user specifies Must be worth threshold value, the match parameter provided according to described second user selects the study notes of one or more first users to include:

The operation result of the 5th pre-defined algorithm is carried out sort descending, and wherein, described 5th pre-defined algorithm is: $\mathrm{Opereation}31(i,j6)=\underset{k\&Element;\mathrm{SOI}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}({s1}_{j6k}-{s1}_{\mathrm{ik}}),$ Wherein, i ∈ Set (Students), J6 ∈ SOI (Students), ${\mathrm{\&mu;}}_{\mathrm{ik}}=\left\{\begin{array}{cc}\mathrm{\&alpha;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelA}\\ \mathrm{\&beta;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelB}\\ ...& ...\end{array}\right.,$ Described Set (Students) is the set of user, Students={u₁,u₂,u₃,...,u_n, u_nFor nth user, SOI (Knowledge) is that described second user specifies Knowledge point set interested, described SOI (Students) is user's set interested that described second user specifies, s1_j6k For the operation achievement of the knowledge point k of user j6, s1_ikBeing the operation achievement of the knowledge point k of the second user i, Level is institute State operation achievement s1 of the knowledge point k of the second user i_ikCorresponding individual event rank, LevelA is outstanding, and LevelB is good Good, μ_ikFor weighting parameter, α is the number more than 1, and β is the number more than α；

The Q name first user that selecting sequence is the most forward；

The operation result of the 6th pre-defined algorithm is carried out sort descending, and wherein, described 6th pre-defined algorithm is: $\mathrm{Opereation}32(i,j7)=\underset{k\&Element;\mathrm{SOI}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{j7k}{\mathrm{\&theta;}}_{j7k},$ Wherein, J7 ∈ Set (topQ (resule (operation31) ↓)), ${\mathrm{\&mu;}}_{j7k}=\left\{\begin{array}{cc}0& {\mathrm{\&theta;}}_{j7k}<{\mathrm{\&theta;}}_{\min }\\ 1& {\mathrm{\&theta;}}_{j7k}>{\mathrm{\&theta;}}_{\min }\end{array}\right.,$ Set (topQ (resule (operation31) ↓)) is that the operation result to described 5th pre-defined algorithm carries out sort descending, and ranking is The set of the first user composition of front Q, μ_j7kFor weight parameter, θ_j7kFor first user j7 upload about knowledge point k's The value parameter of study notes, θ_minThe study notes specified for described second user are worth threshold value；

The study notes of the first user of the 3rd predetermined quantity that selected and sorted is the most forward.

6. a study notes recommendation apparatus, it is characterised in that including:

Logging modle, for recording the study notes of one or more first user in the way of audio frequency and video；

Select module, for selecting the study notes of the one or more first users conformed to a predetermined condition；

Recommending module, for recommending the second user by the study notes of the one or more first user selected.

Device the most according to claim 6, it is characterised in that described selection module includes at least one of:

First selects unit, for selecting the study notes of the one or more first users got good marks, wherein, Described get good marks as school grade more than the first predetermined condition；

Second selects unit, for selecting school grade progress to exceed the study of one or more first users of predetermined threshold Gains in depth of comprehension；

3rd selects unit, selects one or more first users for the match parameter provided according to described second user Study notes.

Device the most according to claim 7, it is characterised in that described first selects unit to include:

The operation result of the first pre-defined algorithm is carried out sort descending；Wherein, described first pre-defined algorithm is: $\mathrm{Opereation}11(i,j1)=\underset{k\&Element;\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}({s1}_{j1k}-{s1}_{\mathrm{ik}}),i\&Element;\mathrm{Set}\left(\mathrm{Students}\right),j1\&Element;\mathrm{Set}\left(\mathrm{ClassA}\right),$ ${\mathrm{\&mu;}}_{\mathrm{ik}}=\left\{\begin{array}{cc}\mathrm{\&alpha;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelA}\\ \mathrm{\&beta;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelB}\\ ...& ...\end{array}\right.,$ Described Set (Students) is the set of user, Students={u₁,u₂,u₃,...,u_n, u_nFor nth user, described Set (ClassA) is that the comprehensive rank of school grade is The set of the superior users of A class, Set (Knowledge) is the set of knowledge point, Knowledge={k₁,k₂,k₃,...,k_m, k_mFor m-th knowledge point, s1_j1kOperation for the knowledge point k of user j1 Achievement, s1_ikBeing the operation achievement of the knowledge point k of the second user i, Level is the work of the knowledge point k of described second user i Industry achievement s1_ikCorresponding individual event rank, LevelA is outstanding, and LevelB is good, μ_ikFor weighting parameter, α is for being more than The number of 1, β is the number more than α；

The first user selecting ranking to be front N；

The operation result of the second pre-defined algorithm is carried out sort descending, and wherein, described second pre-defined algorithm is: $\mathrm{Opereation}12(i,j2)=\underset{k\&Element;\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}{\mathrm{\&theta;}}_{j2k},j2\&Element;\mathrm{Set}\left(\mathrm{topN}(\mathrm{resule}\left(\mathrm{operation}11\right)\&DownArrow;)\right),$ $\mathrm{Set}\left(\mathrm{topN}(\mathrm{result}\left(\mathrm{operation}11\right)\&DownArrow;)\right)$ For the operation result of described first pre-defined algorithm is carried out sort descending, ranking is The set of the first user composition of front N, θ_j2kValue parameter for the study notes about knowledge point k that first user j2 uploads；

The study notes of the first user of the first predetermined quantity that selected and sorted is the most forward.

Device the most according to claim 7, it is characterised in that described second selects unit to include:

Utilize the 3rd pre-defined algorithm one screen M study the starting stage differ with the study ability to accept of described second user Less than the first user of predetermined value, wherein, described 3rd pre-defined algorithm one is: $\mathrm{Opereation}21(i,j3)=\underset{k\&Element;\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}|{s1}_{j3k}-{s1}_{\mathrm{ik}}|,i\&Element;\mathrm{Set}\left(\mathrm{Students}\right),$ $j3\&Element;\mathrm{Set}\left(\mathrm{Class}\left(i\right)\right),$ ${\mathrm{\&mu;}}_{\mathrm{ik}}=\left\{\begin{array}{cc}\mathrm{\&alpha;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelA}\\ \mathrm{\&beta;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelB}\\ ...& ...\end{array}\right.,$ Described Set (Students) is the set of user, Students={u₁,u₂,u₃,...,u_n, u_nFor nth user, Set (Class (i)) is comprehensive rank and the institute of school grade Stating the set of the second user identical for user i, Set (Knowledge) is the set of knowledge point, Knowledge={k₁,k₂,k₃,...,k_m, k_mFor m-th knowledge point, s1_j3kOperation for the knowledge point k of user j3 Achievement, s1_ikBeing the operation achievement of the knowledge point k of the second user i, Level is the work of the knowledge point k of described second user i Industry achievement s1_ikCorresponding individual event rank, LevelA is outstanding, and LevelB is good, μ_ikFor weighting parameter, α is for being more than The number of 1, β is the number more than α；

The 3rd pre-defined algorithm two is utilized to screen L first user from described M first user, wherein, described 3rd pre- Determining algorithm two is: $\mathrm{Opereation}22(i,j4)=\underset{k\&Element;\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}({s2}_{j4k}-{s2}_{\mathrm{ik}}),$ Wherein, J4 ∈ Set (topM (result (operation 21) ↑)), Set (topM (result (operation 21) ↑)) are The operation result of described 3rd pre-defined algorithm is carried out sort ascending, and ranking is the set of the first user composition of front M, s2_j4kFor The new operation achievement of the knowledge point k of user j4, s2_ikIt it is the new operation achievement of the knowledge point k of the second user i；

The operation result of the 4th pre-defined algorithm is carried out sort descending, and wherein, described 4th pre-defined algorithm is: $\mathrm{Opereation}23(i,j5)=\underset{k\&Element;\mathrm{Set}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}{\mathrm{\&theta;}}_{j5k},j5\&Element;\mathrm{Set}\left(\mathrm{topL}(\mathrm{resule}\left(\mathrm{operation}22\right)\&DownArrow;)\right),$ θ_j5kValue parameter for the study notes about knowledge point k that first user j5 uploads；

The study notes of the first user of the second predetermined quantity that selected and sorted is the most forward.

Device the most according to claim 7, it is characterised in that described match parameter includes at least one of: described second uses The knowledge point interested that family is specified, the user interested that described second user specifies, the study heart that described second user specifies Must be worth threshold value, the described 3rd selects unit to include:

The operation result of the 5th pre-defined algorithm is carried out sort descending, and wherein, described 5th pre-defined algorithm is: $\mathrm{Opereation}31(i,j6)=\underset{k\&Element;\mathrm{SOI}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{\mathrm{ik}}({s1}_{j6k}-{s1}_{\mathrm{ik}}),$ Wherein, i ∈ Set (Students), J6 ∈ SOI (Students), ${\mathrm{\&mu;}}_{\mathrm{ik}}=\left\{\begin{array}{cc}\mathrm{\&alpha;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelA}\\ \mathrm{\&beta;}& {s1}_{\mathrm{ik}}\&Element;\mathrm{LevelB}\\ ...& ...\end{array}\right.,$ Described Set (Students) is the set of user, Students={u₁,u₂,u₃,...,u_n, u_nFor nth user, SOI (Knowledge) is that described second user specifies Knowledge point set interested, described SOI (Students) is user's set interested that described second user specifies, s1_j6k For the operation achievement of the knowledge point k of user j6, s1_ikBeing the operation achievement of the knowledge point k of the second user i, Level is institute State operation achievement s1 of the knowledge point k of the second user i_ikCorresponding individual event rank, LevelA is outstanding, and LevelB is good Good, μ_ikFor weighting parameter, α is the number more than 1, and β is the number more than α；

The Q name first user that selecting sequence is the most forward；

The operation result of the 6th pre-defined algorithm is carried out sort descending, and wherein, described 6th pre-defined algorithm is: $\mathrm{Opereation}32(i,j7)=\underset{k\&Element;\mathrm{SOI}\left(\mathrm{Knowledge}\right)}{\mathrm{\&Sigma;}}{\mathrm{\&mu;}}_{j7k}{\mathrm{\&theta;}}_{j7k},$ Wherein, J7 ∈ Set (topQ (resule (operation31) ↓)), ${\mathrm{\&mu;}}_{j7k}=\left\{\begin{array}{cc}0& {\mathrm{\&theta;}}_{j7k}<{\mathrm{\&theta;}}_{\min }\\ 1& {\mathrm{\&theta;}}_{j7k}>{\mathrm{\&theta;}}_{\min }\end{array}\right.,$ Set (topQ (resule (operation31) ↓)) is that the operation result to described 5th pre-defined algorithm carries out sort descending, and ranking is The set of the first user composition of front Q, μ_j7kFor weight parameter, θ_j7kFor first user j7 upload about knowledge point k's The value parameter of study notes, θ_minThe study notes specified for described second user are worth threshold value；

The study notes of the first user of the 3rd predetermined quantity that selected and sorted is the most forward.