CN103942317A - Recommending method and system - Google Patents

Recommending method and system Download PDF

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Publication number
CN103942317A
CN103942317A CN201410171048.9A CN201410171048A CN103942317A CN 103942317 A CN103942317 A CN 103942317A CN 201410171048 A CN201410171048 A CN 201410171048A CN 103942317 A CN103942317 A CN 103942317A
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user
content recommendation
data storehouse
content
recommending data
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CN103942317B (en
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严建军
王恺宁
钟美玲
胡乐梅
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JIANGXI MIISI TECHNOLOGY Co Ltd
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JIANGXI MIISI TECHNOLOGY Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/335—Filtering based on additional data, e.g. user or group profiles
    • G06F16/337—Profile generation, learning or modification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/903—Querying

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

The embodiment of the invention relates to the educational and electronic field and provides a recommending method and system. The recommending method and system enable the learning content learnt by a user through a learning device to be consistent with the learning demand and the learning expectation of the user, and further enables the learning effect of the user by utilizing the learning device to be improved. According to the scheme adopted in the recommending method and system, the user searches for a preset data base according to user identification of a first user; if the preset data base comprises the user identification of the first user and the recommended content corresponding to the user identification, the recommended content corresponding to the user identification in the preset data base is output so that the first user can study the recommended content; if the preset data base does not comprise the user identification of the first user and the recommended content corresponding to the user identification, a first recommended content is acquired according to a characteristic parameter of the first user and first preset strategy analysis; the first recommended content is recommended and output so that the first user can study the first recommended content. The recommending method and system are used for achieving recommendation.

Description

A kind of recommend method and system
Technical field
The present invention relates to educate electronic applications, relate in particular to a kind of recommend method and system.
Background technology
At present, due to the attention of people for study, make facility for study obtain development widely.Facility for study is a kind of for user provides the educational electronic product of learning content, and user is by using facility for study, can comprehensive raising learning ability, cultivate learning interest, exploitation potential.
Facility for study on existing market, conventionally be all learning content is built in facility for study or be built in the matching used learning card of facility for study in, by using the user of facility for study according to the wish of oneself, or according to predefined fixing study plan, voluntarily built-in learning content is learnt.
But inventor finds prior art and at least has following shortcoming: user uses facility for study to learn voluntarily built-in learning content, because learning content does not conform to user's learning demand and Expectation of Learning, causes results of learning limited.
Summary of the invention
The embodiment of the present invention provides a kind of recommend method and system, and the learning content that realization is used the user of facility for study to learn, conforms to user's learning demand and Expectation of Learning, and then makes user use the results of learning of facility for study to improve.
For achieving the above object, the technical scheme that the embodiment of the present invention adopts is,
First aspect, provides a kind of recommend method, comprising:
According to the user ID of first user, search presetting database; Wherein, described first user is the user of current login facility for study; Described presetting database comprises at least one user's user ID and the content recommendation corresponding with described user ID; Described content recommendation comprises the entity content of using the digital content of described facility for study or not using described facility for study;
If comprise the user ID of described first user and the content recommendation corresponding with described user ID in described presetting database, content recommendation corresponding with the user ID of described first user in described presetting database is recommended to output, so that described first user is learnt;
If do not comprise the user ID of described first user and the content recommendation corresponding with described user ID in described presetting database, according to the characteristic parameter of described first user and the first preset strategy, the first content recommendation is obtained in analysis, and described the first content recommendation is recommended to output, so that described first user is learnt.
In conjunction with first aspect, in the possible implementation of the first of first aspect, the characteristic parameter of described first user comprises: the learning content that the stage of growth data of described first user or the study situation of described first user or described first user are selected; Wherein, the study situation of described first user comprises described first user learning content or described first user learning content not.
In conjunction with the possible implementation of the first of first aspect, in the possible implementation of the second of first aspect, describedly according to the characteristic parameter of described first user and the first preset strategy analysis, obtain the first content recommendation, comprising:
If the characteristic parameter of described first user comprises the stage of growth data of described first user, using content corresponding to the characteristic parameter with described first user as the first content recommendation; Or,
If the characteristic parameter of described first user comprises described first user and learned content, the derivative content of the content that described first user characteristic parameter is comprised is as the first content recommendation; Or,
If the characteristic parameter of described first user comprises the described first user learning content that learning content or described first user are not selected, the content that the characteristic parameter of described first user is comprised as the first content recommendation.
The possible implementation of the second in conjunction with the possible implementation of the first of first aspect or first aspect or first aspect, in the third possible implementation of first aspect, described according to the user ID of first user, before searching presetting database, described method also comprises
Receive described first user to the trigger action of content recommendation is set;
According to the user ID of described first user, show that the content recommendation in the individual recommending data storehouse of described first user is selected for described first user; Wherein, the individual recommending data storehouse of described first user comprises at least one the default content recommendation of described first user;
Receive the second content recommendation that described first user is selected; Wherein, described the second content recommendation belongs to the individual recommending data storehouse of described first user;
Described the second content recommendation is saved to presetting database, and corresponding with the user ID of described first user.
The third possible implementation in conjunction with first aspect, in the 4th kind of possible implementation of first aspect, described according to the user ID of described first user, before showing that content recommendation in the individual recommending data storehouse of described first user is selected for described first user, described method also comprises
Receive the 3rd content recommendation of described first user input;
Search and in public recommending data storehouse, whether comprise described the 3rd content recommendation; Wherein, described public recommending data storehouse comprises at least one content recommendation;
If described public recommending data storehouse comprises described the 3rd content recommendation, described the 3rd content recommendation is saved to the individual recommending data storehouse of described first user.
In conjunction with the 4th kind of possible implementation of first aspect, in the 5th kind of possible implementation of first aspect, if do not comprise described the 3rd content recommendation in described public recommending data storehouse, described method also comprises,
Described the 3rd content recommendation is saved to the individual recommending data storehouse of described first user or the individual recommending data storehouse of described first user and described public recommending data storehouse; Or,
According to described first user information and the second preset strategy, the 4th content recommendation is obtained in analysis, and described the 4th content recommendation is saved to the individual recommending data storehouse of described first user or the individual recommending data storehouse of described first user and described public recommending data storehouse.
In conjunction with the 5th kind of possible implementation of first aspect, in the 6th kind of possible implementation of first aspect, describedly according to described first user information and the second preset strategy analysis, obtain the 4th content recommendation, comprising:
According to described first user information, the content that described first user information is comprised is as described the 4th content recommendation.
In conjunction with the 5th kind of possible implementation of first aspect or the 6th kind of possible implementation of first aspect, in the 7th kind of possible implementation of first aspect, described method also comprises,
If do not comprise described the 3rd content recommendation in described public recommending data storehouse, in user interface, show self-built content recommendation or system generating recommendations content, for described first user, select;
Accordingly, described described the 3rd content recommendation is saved to the individual recommending data storehouse of described first user or the individual recommending data storehouse of described first user and described public recommending data storehouse; Or, according to described first user information analysis, obtain the 4th content recommendation, described the 4th content recommendation is saved to the individual recommending data storehouse of described first user or the individual recommending data storehouse of described first user and described public recommending data storehouse, comprise,
Receive the trigger action of described first user to self-built content recommendation, described the 3rd content recommendation is saved to the individual recommending data storehouse of described first user or the individual recommending data storehouse of described first user and described public recommending data storehouse; Or,
Receive the trigger action of described first user to system generating recommendations content, according to described first user information analysis, obtain the 4th content recommendation, and described the 4th content recommendation is saved to the individual recommending data storehouse of described first user or the individual recommending data storehouse of described first user and described public recommending data storehouse.
In conjunction with the 6th kind of possible implementation of the 5th kind of possible implementation of first aspect or first aspect or the 7th kind of possible implementation of first aspect, in the 8th kind of possible implementation of first aspect, described described the 3rd content recommendation is saved to the individual recommending data storehouse of described first user or the individual recommending data storehouse of described first user and described public recommending data storehouse, comprise
Show and keep the privately owned or shared data of data, for described first user, select;
Receive described first user for keeping the privately owned trigger action of data, described the 3rd content recommendation is saved to the individual recommending data storehouse of described first user; Or,
Receive described first user for the trigger action of sharing data, described the 3rd content recommendation is saved to individual recommending data storehouse and the described public recommending data storehouse of described first user.
In conjunction with the possible implementation of the first of first aspect or first aspect, to any one in the 8th kind of possible implementation of first aspect, in the 9th kind of possible implementation of first aspect, described method also comprises:
Record upgrades the characteristic parameter of described first user.
Second aspect, provides a kind of commending system, comprising:
Search unit, for according to the user ID of first user, search presetting database; Wherein, described first user is the user of current login facility for study; Described presetting database comprises at least one user's user ID and the content recommendation corresponding with described user ID; Described content recommendation comprises the entity content of using the digital content of described facility for study or not using described facility for study;
Output unit, if the user ID that comprises described first user for described presetting database and the content recommendation corresponding with described user ID, content recommendation corresponding with the user ID of described first user in described presetting database is recommended to output, so that described first user is learnt;
Analyze acquiring unit, if do not comprise the user ID of described first user and the content recommendation corresponding with described user ID for described presetting database, according to the characteristic parameter of described first user and the first preset strategy, analyze and obtain the first content recommendation;
Described output unit also for, described the first content recommendation is recommended to output, so that described first user is learnt.
In conjunction with second aspect, in the possible implementation of the first of second aspect, the characteristic parameter of described first user comprises: the learning content that the stage of growth data of described first user or the study situation of described first user or described first user are selected; Wherein, the study situation of described first user comprises described first user learning content or described first user learning content not.
In conjunction with the possible implementation of the first of second aspect, in the possible implementation of the second of second aspect, described analysis acquiring unit specifically for,
If the characteristic parameter of described first user comprises the stage of growth data of described first user, using content corresponding to the characteristic parameter with described first user as the first content recommendation; Or,
If the characteristic parameter of described first user comprises described first user and learned content, the derivative content of the content that described first user characteristic parameter is comprised is as the first content recommendation; Or,
If the characteristic parameter of described first user comprises the described first user learning content that learning content or described first user are not selected, the content that the characteristic parameter of described first user is comprised as the first content recommendation.
In conjunction with the possible implementation of the second of the possible implementation of the first of second aspect or second aspect or second aspect, in the third possible implementation of second aspect, described system also comprises,
Receiving element, for receiving described first user to the trigger action of content recommendation is set;
Display unit, for according to the user ID of described first user, shows that the content recommendation in the individual recommending data storehouse of described first user is selected for described first user; Wherein, the individual recommending data storehouse of described first user comprises at least one the default content recommendation of described first user;
Described receiving element also for, receive the second content recommendation that described first user is selected; Wherein, described the second content recommendation belongs to the individual recommending data storehouse of described first user;
Storage unit, for described the second content recommendation is saved to presetting database, and corresponding with the user ID of described first user.
In conjunction with the third possible implementation of second aspect, in the 4th kind of possible implementation of second aspect,
Described receiving element also for, receive the 3rd content recommendation of described first user input;
Described search unit also for, search in public recommending data storehouse, whether to comprise described the 3rd content recommendation; Wherein, described public recommending data storehouse comprises at least one content recommendation;
Described storage unit also for, if described public recommending data storehouse comprises described the 3rd content recommendation, described the 3rd content recommendation is saved to the individual recommending data storehouse of described first user.
In conjunction with the 4th kind of possible implementation of second aspect, in the 5th kind of possible implementation of second aspect, if do not comprise described the 3rd content recommendation in described public recommending data storehouse, described storage unit also for,
Described the 3rd content recommendation is saved to the individual recommending data storehouse of described first user or the individual recommending data storehouse of described first user and described public recommending data storehouse; Or,
Described analysis acquiring unit also for, according to described first user information and the second preset strategy, analyze and to obtain the 4th content recommendation;
Described storage unit also for, described the 4th content recommendation is saved to the individual recommending data storehouse of described first user or the individual recommending data storehouse of described first user and described public recommending data storehouse.
In conjunction with the 5th kind of possible implementation of second aspect, in the 6th kind of possible implementation of second aspect, described analysis acquiring unit also for,
According to described first user information, the content that described first user information is comprised is as described the 4th content recommendation.
In conjunction with the 5th kind of possible implementation of second aspect or the 6th kind of possible implementation of second aspect, in the 7th kind of possible implementation of second aspect, described display unit also for,
If do not comprise described the 3rd content recommendation in described public recommending data storehouse, in user interface, show self-built content recommendation or system generating recommendations content, for described first user, select;
Accordingly,
Described receiving element also for, receive the trigger action of described first user to self-built content recommendation;
Described storage unit also for, described the 3rd content recommendation is saved to the individual recommending data storehouse of described first user or the individual recommending data storehouse of described first user and described public recommending data storehouse; Or,
Described receiving element also for, receive the trigger action of described first user to system generating recommendations content;
Described analysis acquiring unit also for, according to described first user information analysis, obtain the 4th content recommendation;
Described storage unit also for, described the 4th content recommendation is saved to the individual recommending data storehouse of described first user or the individual recommending data storehouse of described first user and described public recommending data storehouse.
In conjunction with the 6th kind of possible implementation of the 5th kind of possible implementation of second aspect or second aspect or the 7th kind of possible implementation of second aspect, in the 8th kind of possible implementation of second aspect,
Described display unit also for, show to keep the privately owned or shared data of data, for described first user, select;
Described receiving element also for, receive described first user for keeping the privately owned trigger action of data;
Described storage unit also for, described the 3rd content recommendation is saved to the individual recommending data storehouse of described first user; Or,
Described receiving element also for, receive described first user for the trigger action of sharing data;
Described storage unit also for, described the 3rd content recommendation is saved to individual recommending data storehouse and the described public recommending data storehouse of described first user.
In conjunction with the possible implementation of the first of second aspect or second aspect, to any one in the 8th kind of possible implementation of second aspect, in the 9th kind of possible implementation of second aspect, described system also comprises:
Record updating block, for recording the characteristic parameter that upgrades described first user.
The invention provides a kind of recommend method and system, by according to the user ID of first user, search presetting database; If comprise user ID and the content recommendation corresponding with user ID of first user in presetting database, content recommendation corresponding with the user ID of first user in presetting database is recommended to output, so that first user is learnt; If do not comprise user ID and the content recommendation corresponding with user ID of first user in presetting database, according to the characteristic parameter of first user and the first preset strategy, analyze and obtain the first content recommendation; And the first content recommendation is recommended to output, so that first user is learnt.The learning content that realization is used the user of facility for study to learn, conforms to user's learning demand and Expectation of Learning, and then makes user use the results of learning of facility for study to improve.
Accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, to the accompanying drawing of required use in embodiment or description of the Prior Art be briefly described below, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skills, do not paying under the prerequisite of creative work, can also obtain according to these accompanying drawings other accompanying drawing.
The schematic flow sheet of a kind of recommend method that Fig. 1 provides for the embodiment of the present invention;
The schematic flow sheet of the method in the individual recommending data of a kind of default generation storehouse that Fig. 2 provides for the embodiment of the present invention;
The schematic flow sheet of the another kind of recommend method that Fig. 3 provides for the embodiment of the present invention;
The schematic flow sheet of another recommend method that Fig. 4 provides for the embodiment of the present invention;
The structural representation of a kind of commending system that Fig. 5 provides for the embodiment of the present invention;
The structural representation of the another kind of commending system that Fig. 6 provides for the embodiment of the present invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is clearly and completely described, obviously, described embodiment is only the present invention's part embodiment, rather than whole embodiment.Embodiment based in the present invention, those of ordinary skills, not making the every other embodiment obtaining under creative work prerequisite, belong to the scope of protection of the invention.
Embodiment mono-
The embodiment of the present invention one provides a kind of recommend method, is applied to facility for study, and referring to Fig. 1, described method can comprise:
101, according to the user ID of first user, search presetting database;
Wherein, described first user is the user of current login facility for study;
It should be noted that, described user can include but not limited to use the head of a family, child and the organization user of facility for study; The all embodiment of the present invention all specifically do not limit for user's type.
The user name that is different from other users that the user ID of first user can be used for first user in facility for study, other identification informations that use in the time of can registering in facility for study for first user, all embodiment of the present invention specifically do not limit for user ID yet.
Wherein, described presetting database can comprise at least one user's user ID and the content recommendation corresponding with described user ID; Content recommendation can comprise the entity content of using the digital content of facility for study or not using facility for study;
It should be noted that, the content recommendation described in all embodiment of the present invention, can include but not limited to use facility for study e-learning content, do not use entity learning content, the activity description of facility for study;
Wherein, the e-learning content of described use facility for study, can comprise and built-inly in facility for study write, read, listen to the music, draw a picture, take exercise, see animation, listen story, play games etc.;
The described entity learning content that does not use facility for study, can comprise and in reality, not use writing, read, listen to the music, draw a picture, take exercise, see animation, listen story, playing games etc. of facility for study;
Described activity description can comprise, outdoor activity, extracurricular activities, has a rest, plays games etc.
Exemplary, presetting database can be stored in the form of form in facility for study, referring to table 1, and a kind of presetting database of example;
Table 1
User ID Content recommendation
zhangsan Read < < small red cap > >
lisi Study < < alphabet > >
wangwu Outdoor activity is played soccer
…… ……
At least one user ID that presetting database comprises and the content recommendation corresponding with user ID are by user's default generation at any time, and all embodiment of the present invention did not specifically limit for the default moment generating.
For each user of facility for study, the method of the user ID in default generation presetting database and the content recommendation corresponding with user ID is identical, the present invention only be take first user as example, the method of the user ID that user preset generation presetting database is comprised and the content recommendation corresponding with user ID describes, and specifically can comprise following a～d step:
A, reception first user are to arranging the trigger action of content recommendation;
Wherein, first user refers to the trigger action of content recommendation is set, when first user need to arrange the content recommendation that meets expectation or demand, and the operation of first user to facility for study; Described trigger action can be to press button operation, can be also the other forms of trigger action modes such as touch operation;
By first user, to the trigger action of content recommendation is set, facility for study is entered arrange the interface of recommendation.
Trigger action can comprise that first user is entered the trigger action of recommending interface is set by the mechanical key selection in facility for study, can comprise that first user is entered the trigger action of recommending interface is set by the electronic key selection in facility for study, all embodiment of the present invention specifically do not limit this yet;
Optionally, described mechanical key can comprise physical button; Described electronic key can comprise touch key-press.
B, according to the user ID of first user, show that the content recommendation in the individual recommending data storehouse of first user is selected for first user;
Wherein, the individual recommending data storehouse of first user can comprise at least one the content recommendation that first user is default;
The individual recommending data storehouse of first user can be by first user default generation at any time, and the present invention did not specifically limit for the default moment that generates first user of first user;
Exemplary, the default method that generates the individual recommending data storehouse of first user of first user, referring to Fig. 2, specifically can comprise the steps b1～b3:
The 3rd content recommendation of b1, the input of reception first user;
Wherein, the 3rd content recommendation is first user default while generating the individual recommending data storehouse of first user, any one in the learning content that meets own demand or expectation of input.
B2, search in public recommending data storehouse, whether to comprise the 3rd content recommendation;
Wherein, described public recommending data storehouse comprises at least one content recommendation; Described public recommending data storehouse is the set of content recommendation built-in in facility for study.
If the public recommending data of b3 storehouse comprises the 3rd content recommendation, the 3rd content recommendation is saved to the individual recommending data storehouse of first user.
Optionally, if do not comprise the 3rd content recommendation in public recommending data storehouse, can be in the system of facility for study default configuration, directly the 3rd content recommendation is saved to the individual recommending data storehouse of first user or the individual recommending data storehouse of first user and public recommending data storehouse; Or, according to first user information and the second preset strategy analysis, obtain the 4th content recommendation, the 4th content recommendation is saved to the individual recommending data storehouse of first user or the individual recommending data storehouse of first user and public recommending data storehouse.
Wherein, first user information can include but not limited to user's learning objective information, user's courses taken information, user's the information of buying books, hobby information of user etc.;
Wherein, for obtaining of first user information, can include but not limited to obtain or system is obtained by internet by first user input, the present invention does not specifically limit this;
Exemplary, when obtaining the user profile of first user by internet, can adopt with the connection of educational institution, dock with Books Marketing quotient data storehouse, the mode of docking with friend-making site databases is obtained first user information, and the present invention does not specifically limit this.
Wherein, according to first user information and the second preset strategy analysis, obtain the 4th content recommendation, specifically can comprise:
According to first user information, the content that first user information is comprised is as the 4th content recommendation.
Exemplary, if get first user, there is study the first course, can analyze and obtain the 4th content recommendation for " reviewing the first course "; Or, if get first user, there are first books of purchase, can analyze and obtain the 4th content recommendation for " reading the first books "; Or, if the hobby that gets first user, for playing soccer, can be analyzed, obtain the 4th content recommendation for " playing soccer " etc.;
It should be noted that; above-mentioned according to first user information analysis to obtain the 4th content recommendation be the formal specification of example; the process that the 4th content recommendation is obtained in information analysis according to first user is not specifically limited, every process of obtaining content recommendation according to first user information and preset strategy analysis all belongs to protection scope of the present invention.
Preferably, if do not comprise the 3rd content recommendation in public recommending data storehouse, default configuration in the system of facility for study, does not determine it is self-built content recommendation or system generating recommendations content according to user's selection; Referring to Fig. 2, the default method that generates the individual recommending data storehouse of first user of described first user can also comprise:
B4, in user interface, show self-built content recommendation or system generating recommendations content, for first user, select;
Wherein, user interface can comprise the interactive interface for user's operation that facility for study provides by screen.
Wherein, if first user is selected self-built content recommendation, can receive the trigger action of first user to self-built content recommendation, and in the system of facility for study default configuration, the 3rd content recommendation is saved to the individual recommending data storehouse of first user or the individual recommending data storehouse of first user and public recommending data storehouse;
Optionally, in all embodiment of the present invention, when first user is selected self-built content recommendation, first user can also add explanation to the 3rd content recommendation, and described explanation can comprise the information such as the label, evaluation of the 3rd content recommendation;
Accordingly, the 3rd content recommendation is saved to the individual recommending data storehouse of first user or the individual recommending data storehouse of first user and public recommending data storehouse, can comprise, the explanation of the 3rd content recommendation and the 3rd content recommendation is saved to the individual recommending data storehouse of first user or the individual recommending data storehouse of first user and public recommending data storehouse.
Further alternative, if first user is selected self-built content recommendation, can receive the trigger action of first user to self-built content recommendation, but default configuration the 3rd content recommendation is not preserved position in the system of facility for study, but according to the selection of first user, determine the preservation position of the 3rd content recommendation; Referring to Fig. 2, the default method that generates the individual recommending data storehouse of first user of described first user can also comprise:
B5, demonstration keep the privately owned or shared data of data, for first user, select;
B6, reception first user, for keeping the privately owned trigger action of data, are saved to the 3rd content recommendation in the individual recommending data storehouse of first user;
B7, reception first user, for the trigger action of sharing data, are saved to the 3rd content recommendation in individual recommending data storehouse and the public recommending data storehouse of first user.
Further, in step b4, if first user selective system generating recommendations content receives the trigger action of first user to system generating recommendations content, and performs step b8～b9:
B8, according to first user, the 4th content recommendation is obtained in information analysis;
Wherein, the 4th content recommendation is according to any one in first user Information generation content recommendation.
B9, the 4th content recommendation is saved to the individual recommending data storehouse of first user or the individual recommending data storehouse of first user and described public recommending data storehouse.
Wherein, for the 4th content recommendation being saved to the individual recommending data storehouse of first user or the individual recommending data storehouse of first user and described public recommending data storehouse, can by the system default of facility for study, be configured the preservation position of the 4th content recommendation, also can determine according to the selection of first user the preservation position of the 4th content recommendation; For determine the preservation position of the 4th content recommendation according to the selection of first user, according to the selection of first user, determine that the method for the 3rd content recommendation is similar to b5～b7, at this, no longer repeat.
The second content recommendation that c, reception first user are selected;
Wherein, the second content recommendation belongs to the individual recommending data storehouse of first user;
D, the second content recommendation is saved to presetting database, and corresponding with the user ID of first user.
Exemplary, if first user is before this uses facility for study, in facility for study, preset the individual recommending data storehouse of first user and selected, the user ID and the content recommendation corresponding with user ID that in presetting database, comprise so first user, perform step 102;
If first user is used this facility for study for the first time, or first user is before this uses facility for study, in facility for study, do not preset the individual recommending data storehouse of first user and select, the user ID and the content recommendation corresponding with user ID that in presetting database, do not comprise so first user, perform step 103.
If comprise user ID and the content recommendation corresponding with user ID of first user in 102 presetting database, content recommendation corresponding with the user ID of first user in presetting database is recommended to output, so that first user is learnt;
Wherein, recommend the form of output can include but not limited to word, sound, picture, video, animation, interaction etc.; The form of concrete recommendation output can determine according to the actual requirements, and the present invention is for recommending the form of output specifically not limit.
If do not comprise user ID and the content recommendation corresponding with user ID of first user in 103 presetting database, according to the characteristic parameter of first user and the first preset strategy, analyze and obtain the first content recommendation; And the first content recommendation is recommended to output, so that first user is learnt.
Wherein, the first content recommendation is any one in the content recommendation obtaining according to the characteristic parameter of first user and the first preset strategy analysis.
Wherein, the characteristic parameter of described first user can include but not limited to the study stage of growth of first user, study situation of first user, learning content that first user is selected in system etc.; Wherein, the study situation of described first user can comprise first user learning content or first user learning content not.
Preferably, the content comprising according to the characteristic parameter of first user, according to the characteristic parameter of first user and the first preset strategy, analyzes and obtains the first content recommendation, can comprise,
If the characteristic parameter of first user comprises the stage of growth data of first user, using content corresponding to the characteristic parameter with first user as the first content recommendation; Or,
If the characteristic parameter of first user comprises first user and learned content, the derivative content of the content that first user characteristic parameter is comprised is as the first content recommendation;
Wherein, the derivative content of the content that first user characteristic parameter comprises can comprise: the next chapter content of the content that first user characteristic parameter comprises, or the content that the key word of the content comprising with first user characteristic parameter is identical, or the identic content of the content comprising with first user characteristic parameter; The present invention does not specifically limit this.
Or,
If the characteristic parameter of first user comprises the first user learning content that learning content or first user are not selected, the content that the characteristic parameter of first user is comprised is as the first content recommendation.
Exemplary, if first user is used facility for study for the first time, the age bracket in the study stage of growth data that the characteristic parameter that can get first user is first user; According to the age bracket in the study stage of growth data of first user, analyze the child's who obtains applicable this age bracket learning content as the first content recommendation;
If the non-facility for study that uses for the first time of first user, so, has recorded the study situation of first user in facility for study, obtain and do not learn content as the characteristic parameter of first user in the study situation of first user; According to the not association content in the study situation of first user, the content that this is not learned is as the first content recommendation;
Or, if the non-facility for study that uses for the first time of first user so, has recorded the study situation of first user in facility for study, obtain and learn content as the characteristic parameter of first user in the study situation of first user; According to the content of association in the study situation of first user, the associated learning content that this has been learned to content is as the first content recommendation.
Above-mentioned example just illustrates according to the characteristic parameter of first user and the first preset strategy, analyzes the optional mode obtain the first content recommendation mating with the characteristic parameter of first user, but mode in practical application is not limited to this; In actual applications, the content of the first preset strategy can determine according to the actual requirements, and the present invention does not specifically limit this.
It should be noted that, step 102 and step 103 are coordination, there is no the restriction of sequencing.
It should be noted that, the recommend method that all embodiment of the present invention provide can be used at any time when user uses facility for study, and the present invention does not specifically limit this;
Also it should be noted that, the output time of content recommendation in the recommend method that all embodiment of the present invention provide can be while learning next time, and in the time of can being next stage study, the present invention limit this yet; The output time of concrete content recommendation, can determine, the present invention does not specifically limit this according to the actual requirements.
The invention provides a kind of recommend method, by according to the user ID of first user, search presetting database; If comprise user ID and the content recommendation corresponding with user ID of first user in presetting database, content recommendation corresponding with the user ID of first user in presetting database is recommended to output, so that first user is learnt; If do not comprise user ID and the content recommendation corresponding with user ID of first user in presetting database, according to the characteristic parameter of first user and the first preset strategy analysis, obtain the first content recommendation; And the first content recommendation is recommended to output, so that first user is learnt.The learning content that realization is used the user of facility for study to learn, conforms to user's learning demand and Expectation of Learning, and then makes user use the results of learning of facility for study to improve.
Embodiment bis-
The embodiment of the present invention two provides another kind of recommend method, with user A, uses facility for study, and user A Non-precondition content recommendation is example, and the recommend method shown in Fig. 1 is elaborated, and referring to Fig. 3, described method can comprise:
301, according to the user ID of user A, search presetting database;
For example, user A is used facility for study for the first time, and the individual recommending data storehouse of default user A selecting not is before there is no the user ID of first user and the content recommendation corresponding with user ID so in presetting database; If user A triggers facility for study and recommends, according to the user ID of user A, during inquiry presetting database, in presetting database, do not comprise user ID and the content recommendation corresponding with user ID of user A so, perform step 302.
302, obtain the characteristic parameter of user A;
For example, the subscriber data of filling according to user A, the age of known user A is 2 years old, meets characteristic parameter " age bracket 1～3 ", the characteristic parameter that gets user A is " age bracket 1～3 ".
303,, according to the characteristic parameter of user A, analyze and obtain the first content recommendation mating with the characteristic parameter of user A.
Suppose, according to data statistics, the age bracket high learning content of child's interest-degree of 1 years old～3 years old is followed successively by listens story, nursery rhymes etc.;
For example, according to the characteristic parameter of user A " age bracket 1～3 ", can analyze and get the content recommendation mating with user A characteristic parameter and " listen story ".
304, the first content recommendation is recommended to output, so that user A learns.
For example, by animation form, by showing that in the screen of facility for study " child, we come together to listen story! " and this language of speech play, content recommendation " is listened to story " and recommend output, so that user A learns.
305, record upgrades the characteristic parameter of user A.
Concrete, user's characteristic parameter record is updated in user characteristics parameter database, guarantee that, when recommending next time, the user's who obtains characteristic parameter is the most accurately, to improve the accuracy of recommending next time.
The invention provides a kind of recommend method, by according to the user ID of first user, search presetting database; According to the characteristic parameter of first user and the first preset strategy analysis, obtain the first content recommendation; And the first content recommendation is recommended to output, so that first user is learnt.The learning content that realization is used the user of facility for study to learn, conforms to user's learning demand and Expectation of Learning, and then makes user use the results of learning of facility for study to improve.
Embodiment tri-
The embodiment of the present invention three provides another kind of recommend method, similar with embodiment bis-, all to take user A to use facility for study be example, recommend method shown in Fig. 1 is elaborated, but in the present embodiment, user A according to child's demand and the head of a family's expectation, presets personalized content recommendation in facility for study; Referring to Fig. 4, described method can comprise:
401, receive user A to the trigger action of content recommendation is set;
For example, when user A is used facility for study, according to the demand of user A and expectation, default personalized content recommendation in facility for study, now, user A is by arranging the trigger action of content recommendation, and the recommendation that enters facility for study arranges interface.
402, receive the 3rd content recommendation of user A input;
For example, the book < < lobo > > that has a papery in user A family, and user A expectation child used after one section of facility for study, can leave this this book of facility for study study < < lobo > >, so, the 3rd content recommendation of user A input is for reading < < lobo > >.
403, search in public recommending data storehouse, whether to comprise the 3rd content recommendation;
For example, the awfully hot door of this this book of < < lobo > >, everybody can read, so, in public database, will comprise this this book of < < lobo > >, perform step 404;
Or, this this book of < < lobo > > has just gone on the market or other reasons, the few read, so, in public database, just can not comprise this this book of < < lobo > >, perform step 405.
404, the 3rd content recommendation is saved to the individual recommending data storehouse of user A.
Wherein, if public recommending data storehouse comprises the 3rd content recommendation, the 3rd content recommendation is saved to the individual recommending data storehouse of user A.
For example, the awfully hot door of this this book of < < lobo > >, everybody can read, in step 403, find public database and comprise this this book of < < lobo > >, so the 3rd content recommendation is read to the individual recommending data storehouse that < < lobo > > is saved to user A.
405, in the screen of facility for study, show self-built content recommendation or system generating recommendations content, for user A, select;
For example, this this book of < < lobo > > has just gone on the market or other reasons, the few read, and search and in public database, do not comprise this this book of < < lobo > > in step 403; Now, can in the screen of facility for study, show self-built content recommendation or system generating recommendations content, for user A, select.
If user A selects self-built content recommendation, perform step 406; If user A selective system generating recommendations content, performs step 409.
406, show and keep the privately owned or shared data of data, for user A, select;
For example, after user A selects self-built content recommendation, can determine that user A need to be saved to the 3rd content recommendation the individual recommending data storehouse of user A;
Now, can show and keep the privately owned or shared data of data, for user A, select the 3rd content recommendation to be only saved to the individual recommending data storehouse of user A, still both the 3rd content recommendation is saved to the individual recommending data storehouse of user A, also the 3rd content recommendation is saved to public database and shares with other users.
If user A selects to keep data privately owned, perform step 407; If user A selects to share data, perform step 408.
407, the 3rd content recommendation is saved to the individual recommending data storehouse of user A;
408, the 3rd content recommendation is saved to individual recommending data storehouse and the public recommending data storehouse of first user.
409, according to the information analysis of user A, obtain the 4th content recommendation;
For example, this this book of < < lobo > > has just gone on the market or other reasons, the few read, in step 403, search and in public database, do not comprise this this book of < < lobo > >, and user A is selective system generating recommendations content in step 405, so, system is obtained the 4th content recommendation according to the information analysis of user A.
Again for example, by the connection with Books Marketing website, get user A and bought books < < lobo > >, according to this information, system generates the 4th content recommendation reading < < lobo > > that meets user's request automatically so.
410, the 4th content recommendation is saved to the individual recommending data storehouse of user A.
Suppose, by step 402～step 410, user A has preserved following content recommendation in individual recommending data storehouse: read < < lobo > >, the < < that listens to the music and only have the good > > of mother in the world, play games etc.
411, the content recommendation in the individual recommending data storehouse of demonstration user A, selects for user A;
412, receive the second content recommendation that user A selects;
Suppose, the next learning content that user A selects is: the < < that listens to the music only has the good > > of mother in the world.
413, the second content recommendation is saved to presetting database, and corresponding with the user ID of user A.
So far, user A has completed presetting personalized recommendation content.When the account of child user A is logined facility for study, perform step 414～step 415.
414, according to the user ID of user A, search presetting database
415, content recommendation corresponding with the user ID of user A in presetting database is recommended to output, so that first user is learnt.
For example, in presetting database, preserved user ID and the content recommendation corresponding with user ID of user A: the < < that listens to the music only has the good > > of mother in the world.
So, the < < that content recommendation corresponding with the user ID of user A in presetting database listened to the music only has the good > > of mother to recommend output in the world, so that first user is learnt.
416, record upgrades the characteristic parameter of user A.
Concrete, user's characteristic parameter record is updated in user characteristics parameter database, guarantee that, when recommending next time, the user's who obtains characteristic parameter is the most accurately, to improve the accuracy of recommending next time.
The invention provides a kind of recommend method, by according to the user ID of first user, search presetting database; Content recommendation corresponding with the user ID of first user in presetting database is recommended to output, so that first user is learnt.The learning content that realization is used the user of facility for study to learn, conforms to user's learning demand and Expectation of Learning, and then makes user use the results of learning of facility for study to improve.
Embodiment tetra-
The embodiment of the present invention four provides a kind of commending system 50, and commending system 50 can be the part or all of of facility for study, and referring to Fig. 5, commending system 50 can comprise:
Search unit 501, for according to the user ID of first user, search presetting database; Wherein, first user is the user of current login facility for study; Presetting database comprises at least one user's user ID and the content recommendation corresponding with described user ID; Content recommendation comprises the entity content of using the digital content of facility for study or not using facility for study;
Output unit 502, if the user ID and the content recommendation corresponding with user ID that for presetting database, comprise first user, content recommendation corresponding with the user ID of first user in presetting database is recommended to output, so that first user is learnt;
Analyze acquiring unit 503, if do not comprise user ID and the content recommendation corresponding with user ID of first user for presetting database, according to the characteristic parameter of first user and the first preset strategy, analyze and obtain the first content recommendation;
Described output unit 502 can also be for, the first content recommendation recommended to output, so that first user is learnt.
Optionally, the characteristic parameter of described first user comprises: the learning content that the stage of growth data of first user or the study situation of first user or first user are selected; Wherein, the study situation of first user comprises first user learning content or first user learning content not.
Further, described analysis acquiring unit 503 specifically can be for,
If the characteristic parameter of first user comprises the stage of growth data of first user, using content corresponding to the characteristic parameter with first user as the first content recommendation; Or,
If the characteristic parameter of first user comprises first user and learned content, the derivative content of the content that first user characteristic parameter is comprised is as the first content recommendation; Or,
If the characteristic parameter of first user comprises the first user learning content that learning content or first user are not selected, the content that the characteristic parameter of first user is comprised as the first content recommendation.
Further, referring to Fig. 6, described system 50 can also also comprise,
Receiving element 504, for receiving first user to the trigger action of content recommendation is set;
Display unit 505, for according to the user ID of first user, shows that the content recommendation in the individual recommending data storehouse of first user is selected for first user; Wherein, the individual recommending data storehouse of first user comprises at least one the content recommendation that first user is default;
Described receiving element 504 can also be for, receives the second content recommendation that first user is selected; Wherein, the second content recommendation belongs to the individual recommending data storehouse of first user;
Storage unit 506, for the second content recommendation is saved to presetting database, and corresponding with the user ID of first user.
Optionally, described receiving element 504 can also be for, receives the 3rd content recommendation of first user input;
The described unit 501 of searching can also be for, searches in public recommending data storehouse, whether to comprise the 3rd content recommendation; Wherein, public recommending data storehouse comprises at least one content recommendation;
Described storage unit 506 can also be for, if public recommending data storehouse comprises the 3rd content recommendation, the 3rd content recommendation is saved to the individual recommending data storehouse of first user.
Optionally, if do not comprise the 3rd content recommendation in public recommending data storehouse, described storage unit 506 can also be for,
The 3rd content recommendation is saved to the individual recommending data storehouse of first user or the individual recommending data storehouse of first user and public recommending data storehouse; Or,
Described analysis acquiring unit 503 can also be for, according to first user information and the second preset strategy, analyzes and obtain the 4th content recommendation;
Described storage unit 506 can also be for, and the 4th content recommendation is saved to the individual recommending data storehouse of first user or the individual recommending data storehouse of first user and public recommending data storehouse.
Further, analyzing acquiring unit 503 can also be for,
According to first user information, the content that first user information is comprised is as the 4th content recommendation.
Optionally, described display unit 505 can also be for,
If do not comprise the 3rd content recommendation in public recommending data storehouse, in user interface, show self-built content recommendation or system generating recommendations content, for first user, select.
Accordingly,
Described receiving element 504 can also be for, receives the trigger action of first user to self-built content recommendation;
Described storage unit 506 can also be for, and the 3rd content recommendation is saved to the individual recommending data storehouse of first user or the individual recommending data storehouse of first user and public recommending data storehouse; Or,
Described receiving element 504 can also be for, receives the trigger action of first user to system generating recommendations content;
Described analysis acquiring unit 503 can also be for, and according to first user, the 4th content recommendation is obtained in information analysis;
Described storage unit 506 can also be for, and the 4th content recommendation is saved to the individual recommending data storehouse of first user or the individual recommending data storehouse of first user and public recommending data storehouse.
Optionally, described display unit 505 can also be for, shows and keep the privately owned or shared data of data, for first user, selects;
Described receiving element 504 can also be for, receives first user for keeping the privately owned trigger action of data;
Described storage unit 506 can also be for, the 3rd content recommendation is saved to the individual recommending data storehouse of first user; Or,
Described receiving element 504 can also be for, receives first user for the trigger action of sharing data;
Described storage unit 506 can also be for, the 3rd content recommendation is saved to individual recommending data storehouse and the public recommending data storehouse of first user.
Preferably, described analysis acquiring unit 503 specifically can be for,
Obtain the characteristic parameter of first user;
According to the characteristic parameter of first user, analyze and obtain the first content recommendation mating with the characteristic parameter of first user.
Optionally, referring to Fig. 6, described commending system 50 can also comprise, records updating block 507, for recording the characteristic parameter that upgrades described first user.
The invention provides a kind of commending system 50, by according to the user ID of first user, search presetting database; If comprise user ID and the content recommendation corresponding with user ID of first user in presetting database, content recommendation corresponding with the user ID of first user in presetting database is recommended to output, so that first user is learnt; If do not comprise user ID and the content recommendation corresponding with user ID of first user in presetting database, according to the characteristic parameter of first user and the first preset strategy analysis, obtain the first content recommendation; And the first content recommendation is recommended to output, so that first user is learnt.The learning content that realization is used the user of facility for study to learn, conforms to user's learning demand and Expectation of Learning, and then makes user use the results of learning of facility for study to improve.
One of ordinary skill in the art will appreciate that: all or part of step that realizes said method embodiment can complete by the relevant hardware of programmed instruction, aforesaid program can be stored in a computer read/write memory medium, this program, when carrying out, is carried out the step that comprises said method embodiment; And aforesaid storage medium comprises: various media that can be program code stored such as ROM, RAM, magnetic disc or CDs.
The above; be only the specific embodiment of the present invention, but protection scope of the present invention is not limited to this, is anyly familiar with those skilled in the art in the technical scope that the present invention discloses; can expect easily changing or replacing, within all should being encompassed in protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection domain of described claim.

Claims (20)

1. a recommend method, is characterized in that, comprising:
According to the user ID of first user, search presetting database; Wherein, described first user is the user of current login facility for study; Described presetting database comprises at least one user's user ID and the content recommendation corresponding with described user ID; Described content recommendation comprises the entity content of using the digital content of described facility for study or not using described facility for study;
If comprise the user ID of described first user and the content recommendation corresponding with described user ID in described presetting database, content recommendation corresponding with the user ID of described first user in described presetting database is recommended to output, so that described first user is learnt;
If do not comprise the user ID of described first user and the content recommendation corresponding with described user ID in described presetting database, according to the characteristic parameter of described first user and the first preset strategy, the first content recommendation is obtained in analysis, and described the first content recommendation is recommended to output, so that described first user is learnt.
2. recommend method according to claim 1, is characterized in that, the characteristic parameter of described first user comprises: the learning content that the stage of growth data of described first user or the study situation of described first user or described first user are selected; Wherein, the study situation of described first user comprises described first user learning content or described first user learning content not.
3. recommend method according to claim 2, is characterized in that, describedly according to the characteristic parameter of described first user and the first preset strategy analysis, obtains the first content recommendation, comprising:
If the characteristic parameter of described first user comprises the stage of growth data of described first user, using content corresponding to the characteristic parameter with described first user as the first content recommendation; Or,
If the characteristic parameter of described first user comprises described first user and learned content, the derivative content of the content that described first user characteristic parameter is comprised is as the first content recommendation; Or,
If the characteristic parameter of described first user comprises the described first user learning content that learning content or described first user are not selected, the content that the characteristic parameter of described first user is comprised as the first content recommendation.
4. according to the recommend method described in claim 1-3 any one, it is characterized in that, described, according to the user ID of first user, before searching presetting database, described method also comprises,
Receive described first user to the trigger action of content recommendation is set;
According to the user ID of described first user, show that the content recommendation in the individual recommending data storehouse of described first user is selected for described first user; Wherein, the individual recommending data storehouse of described first user comprises at least one the default content recommendation of described first user;
Receive the second content recommendation that described first user is selected; Wherein, described the second content recommendation belongs to the individual recommending data storehouse of described first user;
Described the second content recommendation is saved to presetting database, and corresponding with the user ID of described first user.
5. recommend method according to claim 4, is characterized in that, described, according to the user ID of described first user, before showing that content recommendation in the individual recommending data storehouse of described first user is selected for described first user, described method also comprises,
Receive the 3rd content recommendation of described first user input;
Search and in public recommending data storehouse, whether comprise described the 3rd content recommendation; Wherein, described public recommending data storehouse comprises at least one content recommendation;
If described public recommending data storehouse comprises described the 3rd content recommendation, described the 3rd content recommendation is saved to the individual recommending data storehouse of described first user.
6. recommend method according to claim 5, is characterized in that, if do not comprise described the 3rd content recommendation in described public recommending data storehouse, described method also comprises,
Described the 3rd content recommendation is saved to the individual recommending data storehouse of described first user or the individual recommending data storehouse of described first user and described public recommending data storehouse; Or,
According to described first user information and the second preset strategy, the 4th content recommendation is obtained in analysis, and described the 4th content recommendation is saved to the individual recommending data storehouse of described first user or the individual recommending data storehouse of described first user and described public recommending data storehouse; Wherein, the information of described first user comprises user.
7. recommend method according to claim 6, is characterized in that, describedly according to described first user information and the second preset strategy analysis, obtains the 4th content recommendation, comprising:
According to described first user information, the content that described first user information is comprised is as described the 4th content recommendation.
8. according to the recommend method described in claim 6 or 7, it is characterized in that, described method also comprises,
If do not comprise described the 3rd content recommendation in described public recommending data storehouse, in user interface, show self-built content recommendation or system generating recommendations content, for described first user, select;
Accordingly, described described the 3rd content recommendation is saved to the individual recommending data storehouse of described first user or the individual recommending data storehouse of described first user and described public recommending data storehouse; Or, according to described first user information analysis, obtain the 4th content recommendation, described the 4th content recommendation is saved to the individual recommending data storehouse of described first user or the individual recommending data storehouse of described first user and described public recommending data storehouse, comprise,
Receive the trigger action of described first user to self-built content recommendation, described the 3rd content recommendation is saved to the individual recommending data storehouse of described first user or the individual recommending data storehouse of described first user and described public recommending data storehouse; Or,
Receive the trigger action of described first user to system generating recommendations content, according to described first user information analysis, obtain the 4th content recommendation, and described the 4th content recommendation is saved to the individual recommending data storehouse of described first user or the individual recommending data storehouse of described first user and described public recommending data storehouse.
9. according to the recommend method described in claim 6-8 any one, it is characterized in that, described described the 3rd content recommendation is saved to the individual recommending data storehouse of described first user or the individual recommending data storehouse of described first user and described public recommending data storehouse, comprises
Show and keep the privately owned or shared data of data, for described first user, select;
Receive described first user for keeping the privately owned trigger action of data, described the 3rd content recommendation is saved to the individual recommending data storehouse of described first user; Or,
Receive described first user for the trigger action of sharing data, described the 3rd content recommendation is saved to individual recommending data storehouse and the described public recommending data storehouse of described first user.
10. according to the recommend method described in claim 1-9 any one, it is characterized in that, described method also comprises:
Record upgrades the characteristic parameter of described first user.
11. 1 kinds of commending systems, is characterized in that, comprising:
Search unit, for according to the user ID of first user, search presetting database; Wherein, described first user is the user of current login facility for study; Described presetting database comprises at least one user's user ID and the content recommendation corresponding with described user ID; Described content recommendation comprises the entity content of using the digital content of described facility for study or not using described facility for study;
Output unit, if the user ID that comprises described first user for described presetting database and the content recommendation corresponding with described user ID, content recommendation corresponding with the user ID of described first user in described presetting database is recommended to output, so that described first user is learnt;
Analyze acquiring unit, if do not comprise the user ID of described first user and the content recommendation corresponding with described user ID for described presetting database, according to the characteristic parameter of described first user and the first preset strategy, analyze and obtain the first content recommendation;
Described output unit also for, described the first content recommendation is recommended to output, so that described first user is learnt.
12. commending systems according to claim 11, is characterized in that, the characteristic parameter of described first user comprises: the learning content that the stage of growth data of described first user or the study situation of described first user or described first user are selected; Wherein, the study situation of described first user comprises described first user learning content or described first user learning content not.
13. commending systems according to claim 12, is characterized in that, described analysis acquiring unit specifically for,
If the characteristic parameter of described first user comprises the stage of growth data of described first user, using content corresponding to the characteristic parameter with described first user as the first content recommendation; Or,
If the characteristic parameter of described first user comprises described first user and learned content, the derivative content of the content that described first user characteristic parameter is comprised is as the first content recommendation; Or,
If the characteristic parameter of described first user comprises the described first user learning content that learning content or described first user are not selected, the content that the characteristic parameter of described first user is comprised as the first content recommendation.
14. according to the commending system described in claim 11-13 any one, it is characterized in that, described system also comprises,
Receiving element, for receiving described first user to the trigger action of content recommendation is set;
Display unit, for according to the user ID of described first user, shows that the content recommendation in the individual recommending data storehouse of described first user is selected for described first user; Wherein, the individual recommending data storehouse of described first user comprises at least one the default content recommendation of described first user;
Described receiving element also for, receive the second content recommendation that described first user is selected; Wherein, described the second content recommendation belongs to the individual recommending data storehouse of described first user;
Storage unit, for described the second content recommendation is saved to presetting database, and corresponding with the user ID of described first user.
15. commending systems according to claim 14, is characterized in that,
Described receiving element also for, receive the 3rd content recommendation of described first user input;
Described search unit also for, search in public recommending data storehouse, whether to comprise described the 3rd content recommendation; Wherein, described public recommending data storehouse comprises at least one content recommendation;
Described storage unit also for, if described public recommending data storehouse comprises described the 3rd content recommendation, described the 3rd content recommendation is saved to the individual recommending data storehouse of described first user.
16. commending systems according to claim 15, is characterized in that, if do not comprise described the 3rd content recommendation in described public recommending data storehouse, described storage unit also for,
Described the 3rd content recommendation is saved to the individual recommending data storehouse of described first user or the individual recommending data storehouse of described first user and described public recommending data storehouse; Or,
Described analysis acquiring unit also for, according to described first user information and the second preset strategy, analyze and to obtain the 4th content recommendation;
Described storage unit also for, described the 4th content recommendation is saved to the individual recommending data storehouse of described first user or the individual recommending data storehouse of described first user and described public recommending data storehouse.
17. commending systems according to claim 16, is characterized in that, described analysis acquiring unit also for,
According to described first user information, the content that described first user information is comprised is as described the 4th content recommendation.
18. according to the commending system described in claim 16 or 17, it is characterized in that, described display unit also for,
If do not comprise described the 3rd content recommendation in described public recommending data storehouse, in user interface, show self-built content recommendation or system generating recommendations content, for described first user, select;
Accordingly,
Described receiving element also for, receive the trigger action of described first user to self-built content recommendation;
Described storage unit also for, described the 3rd content recommendation is saved to the individual recommending data storehouse of described first user or the individual recommending data storehouse of described first user and described public recommending data storehouse; Or,
Described receiving element also for, receive the trigger action of described first user to system generating recommendations content;
Described analysis acquiring unit also for, according to described first user information analysis, obtain the 4th content recommendation;
Described storage unit also for, described the 4th content recommendation is saved to the individual recommending data storehouse of described first user or the individual recommending data storehouse of described first user and described public recommending data storehouse.
19. according to the commending system described in claim 16-18 any one, it is characterized in that,
Described display unit also for, show to keep the privately owned or shared data of data, for described first user, select;
Described receiving element also for, receive described first user for keeping the privately owned trigger action of data;
Described storage unit also for, described the 3rd content recommendation is saved to the individual recommending data storehouse of described first user; Or,
Described receiving element also for, receive described first user for the trigger action of sharing data;
Described storage unit also for, described the 3rd content recommendation is saved to individual recommending data storehouse and the described public recommending data storehouse of described first user.
20. according to the commending system described in claim 11-19 any one, it is characterized in that, described system also comprises:
Record updating block, for recording the characteristic parameter that upgrades described first user.
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CN105425942A (en) * 2015-10-28 2016-03-23 广东小天才科技有限公司 Method and system for pushing learning content to a display interface
CN105205763A (en) * 2015-11-06 2015-12-30 陈国庆 Teaching method and apparatus based on new media modes
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CN110825960A (en) * 2019-10-08 2020-02-21 中通服创发科技有限责任公司 Learning content recommendation method and device

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