CN103942317B

CN103942317B - Recommending method and system

Info

Publication number: CN103942317B
Application number: CN201410171048.9A
Authority: CN
Inventors: 严建军; 王恺宁; 钟美玲; 胡乐梅
Original assignee: JIANGXI MIISI TECHNOLOGY Co Ltd
Current assignee: JIANGXI MIISI TECHNOLOGY Co Ltd
Priority date: 2014-04-25
Filing date: 2014-04-25
Publication date: 2017-05-03
Anticipated expiration: 2034-04-25
Also published as: CN103942317A

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

One kind recommends method and system

Technical field

The present invention relates to electronic applications are educated, more particularly to a kind of recommendation method and system.

Background technology

At present, because people are for the attention of study so that facility for study has obtained extensive development.Facility for study is one The education electronic product for providing the user study content is planted, user can be learnt by using facility for study with comprehensive raising Ability, cultivates learning interest, develops potential.

Facility for study on existing market, is generally all that study content is built in facility for study or is built in and In practising the learning card that uses of coordinative composition of equipments, by the user using facility for study according to the wish of oneself, or according to setting in advance The study plan of fixed fixation, voluntarily learns to built-in study content.

But inventor has found that prior art is at least suffered from the drawback that：User is voluntarily learnt built-in using facility for study Study content, does not correspond due to learning content with the learning demand and Expectation of Learning of user, causes results of learning limited.

The content of the invention

The embodiment of the present invention provides a kind of recommendation method and system, realizes the study learnt using the user of facility for study Content, is consistent with the learning demand and Expectation of Learning of user, and then user is improved using the results of learning of facility for study.

To reach above-mentioned purpose, the technical scheme that the embodiment of the present invention is adopted is,

A kind of first aspect, there is provided recommendation method, including：

According to the ID of first user, presetting database is searched；Wherein, the first user logs in study for current The user of equipment；The presetting database includes the ID of at least one user and recommendation corresponding with the ID Content；The content recommendation include using the facility for study digital content or do not use in the entity of the facility for study Hold；

If the ID comprising the first user and corresponding with the ID pushing away in the presetting database Content is recommended, then content recommendation corresponding with the ID of the first user in the presetting database is recommended into output, with So that the first user is learnt；

If not comprising the ID of the first user and corresponding with the ID in the presetting database Content recommendation, then according to the characteristic parameter and the first preset strategy of the first user, analysis obtains the first content recommendation, and will First content recommendation recommends output, so that the first user is learnt.

With reference in a first aspect, in the first possible implementation of first aspect, the feature of the first user is joined Number includes：Either the study situation of the first user or the first user are selected the stage of growth data of the first user The study content selected；Wherein, the study situation of the first user has learnt interior perhaps described first including the first user User does not learn content.

With reference to the first possible implementation of first aspect, in second possible implementation of first aspect In, the characteristic parameter and the first preset strategy according to the first user is analyzed and obtains the first content recommendation, including：

If the characteristic parameter of the first user includes the stage of growth data of the first user, will use with described first The corresponding content of characteristic parameter at family is used as the first content recommendation；Or,

If the characteristic parameter of the first user has learned content including the first user, by the first user feature The derivative content of the content that parameter includes is used as the first content recommendation；Or,

If the characteristic parameter of the first user does not learn content or first user choosing including the first user The study content selected, the content that the characteristic parameter of the first user is included as the first content recommendation.

With reference to first aspect or first aspect the first possible implementation or second of first aspect it is possible Implementation, in the third possible implementation of first aspect, in the ID according to first user, searches Before presetting database, methods described also includes,

The first user is received to arranging the trigger action of content recommendation；

According to the ID of the first user, show in the recommendation in the personal recommending data storehouse of the first user Hold for first user selection；Wherein, the personal recommending data storehouse of the first user include at least one described first The content recommendation of user preset；

Receive the second content recommendation that the first user is selected；Wherein, second content recommendation belongs to described first The personal recommending data storehouse of user；

Second content recommendation is preserved to presetting database, and it is corresponding with the ID of the first user.

With reference to the third possible implementation of first aspect, in the 4th kind of possible implementation of first aspect In, in the ID according to the first user, show the recommendation in the personal recommending data storehouse of the first user Before content is for first user selection, methods described also includes,

Receive the 3rd content recommendation of the first user input；

Whether search in public recommending data storehouse includes the 3rd content recommendation；Wherein, the public recommending data storehouse Including at least one content recommendation；

If the public recommending data storehouse includes the 3rd content recommendation, by the 3rd content recommendation preserve to The personal recommending data storehouse of the first user.

With reference to the 4th kind of possible implementation of first aspect, in the 5th kind of possible implementation of first aspect In, if not including the 3rd content recommendation in the public recommending data storehouse, methods described also includes,

3rd content recommendation is preserved personal recommending data storehouse or the first user to the first user Personal recommending data storehouse and the public recommending data storehouse；Or,

According to the first user information and the second preset strategy, analysis obtains the 4th content recommendation, the described 4th is pushed away Recommend content to preserve to the personal recommending data storehouse or the personal recommending data storehouse of the first user of the first user and described Public recommending data storehouse.

With reference to the 5th kind of possible implementation of first aspect, in the 6th kind of possible implementation of first aspect In, it is described that 4th content recommendation is obtained according to the first user information and the analysis of the second preset strategy, including：

According to the first user information, the content that the first user information is included is recommended interior as the described 4th Hold.

With reference to the 5th kind of possible implementation or the 6th kind of possible implementation of first aspect of first aspect, In 7th kind of possible implementation of first aspect, methods described also includes,

If not including the 3rd content recommendation in the public recommending data storehouse, self-built pushing away is shown in the user interface Perhaps system generates content recommendation in recommending, for first user selection；

Accordingly, it is described to preserve the 3rd content recommendation to the personal recommending data storehouse or described of the first user The personal recommending data storehouse of first user and the public recommending data storehouse；Or, obtained according to the first user information analysis The 4th content recommendation is taken, the 4th content recommendation is preserved to the personal recommending data storehouse or described first of the first user The personal recommending data storehouse of user and the public recommending data storehouse, including,

Trigger action of the first user to self-built content recommendation is received, the 3rd content recommendation is preserved to described The personal recommending data storehouse of first user or the personal recommending data storehouse of the first user and the public recommending data storehouse；Or Person,

The trigger action that the first user generates content recommendation to system is received, according to the first user information analysis The 4th content recommendation is obtained, and the 4th content recommendation is preserved to the personal recommending data storehouse or described of the first user The personal recommending data storehouse of first user and the public recommending data storehouse.

With reference to the 5th kind of possible implementation of first aspect or the 6th kind of possible implementation of first aspect or 7th kind of possible implementation of first aspect, in the 8th kind of possible implementation of first aspect, it is described will be described 3rd content recommendation preserves the personal recommending data storehouse of the personal recommending data storehouse to the first user or the first user With the public recommending data storehouse, including,

Show and keep data privately owned or shared data, for first user selection；

The first user is received for the trigger action for keeping data privately owned, the 3rd content recommendation is preserved to institute State the personal recommending data storehouse of first user；Or,

Trigger action of the first user for shared data is received, the 3rd content recommendation is preserved to described The personal recommending data storehouse of one user and the public recommending data storehouse.

It is possible to the 8th kind of first aspect with reference to the first possible implementation of first aspect or first aspect Any one of implementation, in the 9th kind of possible implementation of first aspect, methods described also includes：

Record updates the characteristic parameter of the first user.

A kind of second aspect, there is provided commending system, including：

Searching unit, for according to the ID of first user, searching presetting database；Wherein, the first user For the current user for logging in facility for study；The ID of the presetting database including at least one user and with the user Identify corresponding content recommendation；The content recommendation include using the facility for study digital content or do not use the study The physical contents of equipment；

Output unit, if in the presetting database comprising the first user ID and with the user Corresponding content recommendation is identified, then by content recommendation corresponding with the ID of the first user in the presetting database Recommend output, so that the first user is learnt；

Analysis acquiring unit, if in the presetting database not comprising the first user ID and with institute The corresponding content recommendation of ID is stated, then according to the characteristic parameter and the first preset strategy of the first user, analysis is obtained First content recommendation；

The output unit is additionally operable to, and first content recommendation is recommended to export, so that the first user is carried out Study.

With reference to second aspect, in the first possible implementation of second aspect, the feature ginseng of the first user Number includes：Either the study situation of the first user or the first user are selected the stage of growth data of the first user The study content selected；Wherein, the study situation of the first user has learnt interior perhaps described first including the first user User does not learn content.

With reference to the first possible implementation of second aspect, in second possible implementation of second aspect In, it is described analysis acquiring unit specifically for,

With reference to second aspect or second aspect the first possible implementation or second of second aspect it is possible Implementation, in the third possible implementation of second aspect, the system also includes,

Receiving unit, for receiving the first user to arranging the trigger action of content recommendation；

Display unit, for according to the ID of the first user, showing that the personal of the first user recommends number According to the content recommendation in storehouse so that the first user is selected；Wherein, the personal recommending data storehouse of the first user include to The default content recommendation of first user described in one item missing；

The receiving unit is additionally operable to, and receives the second content recommendation that the first user is selected；Wherein, described second push away Recommend the personal recommending data storehouse that content belongs to the first user；

Storage unit, for second content recommendation to be preserved to presetting database, and with the use of the first user Family mark correspondence.

With reference to the third possible implementation of second aspect, in the 4th kind of possible implementation of second aspect In,

The receiving unit is additionally operable to, and receives the 3rd content recommendation of the first user input；

The searching unit is additionally operable to, and whether search in public recommending data storehouse includes the 3rd content recommendation；Wherein, The public recommending data storehouse includes at least one content recommendation；

The storage unit is additionally operable to, if the public recommending data storehouse includes the 3rd content recommendation, by institute State the 3rd content recommendation to preserve to the personal recommending data storehouse of the first user.

With reference to the 4th kind of possible implementation of second aspect, in the 5th kind of possible implementation of second aspect In, if not including the 3rd content recommendation in the public recommending data storehouse, the storage unit is additionally operable to,

The analysis acquiring unit is additionally operable to, and according to the first user information and the second preset strategy, analysis obtains the Four content recommendations；

The storage unit is additionally operable to, and the 4th content recommendation is preserved to the personal recommending data of the first user Storehouse or the personal recommending data storehouse and the public recommending data storehouse of the first user.

With reference to the 5th kind of possible implementation of second aspect, in the 6th kind of possible implementation of second aspect In, the analysis acquiring unit is additionally operable to,

With reference to the 5th kind of possible implementation or the 6th kind of possible implementation of second aspect of second aspect, In 7th kind of possible implementation of second aspect, the display unit is additionally operable to,

Accordingly,

The receiving unit is additionally operable to, and receives trigger action of the first user to self-built content recommendation；

The storage unit is additionally operable to, and the 3rd content recommendation is preserved to the personal recommending data of the first user Storehouse or the personal recommending data storehouse and the public recommending data storehouse of the first user；Or,

The receiving unit is additionally operable to, and receives the trigger action that the first user generates content recommendation to system；

The analysis acquiring unit is additionally operable to, and according to the first user information analysis the 4th content recommendation is obtained；

With reference to the 5th kind of possible implementation of second aspect or the 6th kind of possible implementation of second aspect or 7th kind of possible implementation of second aspect, in the 8th kind of possible implementation of second aspect,

The display unit is additionally operable to, and shows and keeps data privately owned or shared data, for first user selection；

The receiving unit is additionally operable to, and receives the first user for the trigger action for keeping data privately owned；

The storage unit is additionally operable to, and the 3rd content recommendation is preserved to the personal recommending data of the first user Storehouse；Or,

The receiving unit is additionally operable to, and receives the first user for the trigger action of shared data；

The storage unit is additionally operable to, and the 3rd content recommendation is preserved to the personal recommending data of the first user Storehouse and the public recommending data storehouse.

It is possible to the 8th kind of second aspect with reference to the first possible implementation of second aspect or second aspect Any one of implementation, in the 9th kind of possible implementation of second aspect, the system also includes：

Record updating block, for the characteristic parameter that record updates the first user.

The present invention provides a kind of recommendation method and system, by the ID according to first user, searches preset data Storehouse；If the ID comprising first user and content recommendation corresponding with ID in presetting database, by present count Recommend output according to content recommendation corresponding with the ID of first user in storehouse, so that first user is learnt；If pre- If the ID not comprising first user and content recommendation corresponding with ID in database, then according to first user Characteristic parameter and the first preset strategy, analysis obtains the first content recommendation；And recommend the first content recommendation to export, so that the One user is learnt.The study content that realization is learnt using the user of facility for study, the learning demand and study with user Expect to be consistent, and then user is improved using the results of learning of facility for study.

Description of the drawings

In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing The accompanying drawing to be used needed for having technology description is briefly described, it should be apparent that, drawings in the following description are only this Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, can be with Other accompanying drawings are obtained according to these accompanying drawings.

Fig. 1 is a kind of schematic flow sheet of recommendation method provided in an embodiment of the present invention；

Fig. 2 is a kind of schematic flow sheet of default method for generating personal recommending data storehouse provided in an embodiment of the present invention；

Fig. 3 is the schematic flow sheet of another kind of recommendation method provided in an embodiment of the present invention；

Fig. 4 is the schematic flow sheet of another recommendation method provided in an embodiment of the present invention；

Fig. 5 is a kind of structural representation of commending system provided in an embodiment of the present invention；

Fig. 6 is the structural representation of another kind of commending system provided in an embodiment of the present invention.

Specific 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 carried out clear, complete Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than the embodiment of whole.It is based on Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of creative work is not made Embodiment, belongs to the scope of protection of the invention.

Embodiment one

The embodiment of the present invention one provides a kind of recommendation method, is applied to facility for study, and referring to Fig. 1, methods described can be wrapped Include：

101st, according to the ID of first user, presetting database is searched；

Wherein, the first user is the user for currently logging in facility for study；

It should be noted that the user including but not limited to can be used using the head of a family of facility for study, child and mechanism Family；All embodiments of the invention are not specifically limited for the type of user.

The ID of first user can be the use that is different from other users of the first user used in facility for study Name in an account book, or other identification informations that first user is used when registering in facility for study, all embodiments of the invention pair Specifically do not limited in ID.

Wherein, the presetting database can include the ID of at least one user and corresponding with the ID Content recommendation；Content recommendation can include using facility for study digital content or do not use the physical contents of facility for study；

It should be noted that the content recommendation described in all embodiments of the invention, can including but not limited to using study The E-learning Content of equipment, the entity study content, the activity description that do not use facility for study；

Wherein, the E-learning Content of the use facility for study, can write, read including built-in in facility for study Book, music is listened, drawn a picture, taken exercise, see animation, listen story, play games；

The entity for not using facility for study learns content, can include not used in reality the writing of facility for study, Read, listen music, draw a picture, take exercise, see animation, listen story, play games；

The activity description can include, outdoor activity, extracurricular activities, rest, play games.

Exemplary, presetting database can be stored in table form in facility for study, and referring to table 1, example is a kind of Presetting database；

Table 1

ID	Content recommendation
		zhangsan	Read《Small red cap》
lisi	Study《Alphabet》
		wangwu	Outdoor activity is played soccer
……	……

At least one ID that presetting database includes and content recommendation corresponding with ID by user with When it is default generate, all embodiments of the invention are not specifically limited for the moment of default generation.

For each user of facility for study, preset the ID in generation presetting database and mark with user The method for knowing corresponding content recommendation is identical, and the present invention is generated in presetting database only by taking first user as an example to user preset Including ID and the method for content recommendation corresponding with ID illustrate, specifically can walk including following a～d Suddenly：

The trigger action of a, reception first user to setting content recommendation；

Wherein, first user to arrange content recommendation trigger action refer to, when first user needs setting to meet expectation Or demand content recommendation when, operation of the first user to facility for study；The trigger action can be depresses button operation, It can also be the trigger action mode of the other forms such as touch operation；

By trigger action of the first user to setting content recommendation so that facility for study is entered and arranges the interface recommended.

Trigger action can include that first user selects to enter to arrange by the mechanical key in facility for study and recommend interface Trigger action, it is also possible to select to enter to arrange by the electronic key in facility for study including first user and recommend touching for interface Operation is sent out, all embodiments of the invention are not specifically limited this；

Optionally, the mechanical key can include physical button；The electronic key can include touch key-press.

B, according to the ID of first user, show content recommendation in the personal recommending data storehouse of first user for First user is selected；

Wherein, the personal recommending data storehouse of first user can include the default content recommendation of at least one first user；

The personal recommending data storehouse of first user can at any time be preset by first user and be generated, and the present invention is for first user The default moment for generating first user is not specifically limited；

It is exemplary, the method that first user presets the personal recommending data storehouse for generating first user, referring to Fig. 2, specifically B1～b3 can be comprised the steps：

B1, the 3rd content recommendation for receiving first user input；

Wherein, the 3rd content recommendation is first user at the personal recommending data storehouse of default generation first user, input Meet in oneself demand or desired study content any one.

Whether include the 3rd content recommendation in b2, the public recommending data storehouse of lookup；

Wherein, the public recommending data storehouse includes at least one content recommendation；The public recommending data storehouse is study The set of built-in content recommendation in equipment.

If b3, public recommending data storehouse include the 3rd content recommendation, the 3rd content recommendation is preserved to first user Personal recommending data storehouse.

Optionally, if not including the 3rd content recommendation in public recommending data storehouse, can write from memory in the system of facility for study Recognize configuration, directly the 3rd content recommendation is preserved to the personal recommending data storehouse of first user or the individual of first user and recommends number According to storehouse and public recommending data storehouse；Or, the 4th content recommendation is obtained according to first user information and the analysis of the second preset strategy, 4th content recommendation is preserved to the personal recommending data storehouse or the personal recommending data storehouse of first user of first user and public Recommending data storehouse.

Wherein, first user information can include but is not limited to the learning objective information of user, the courses taken of user letter Breath, the information of buying books of user, the preference information of user etc.；

Wherein, for the acquisition of first user information, including but not limited to can be obtained by first user input or be System is obtained by internet, and the present invention is not specifically limited this；

Exemplary, when the user profile of first user is obtained by internet, the number with educational institution can be adopted Dock with Books Marketing quotient data storehouse according to storehouse docking, with the mode of friend-making sites connection first user information is obtained, this Invention is not specifically limited this.

Wherein, the 4th content recommendation is obtained according to first user information and the analysis of the second preset strategy, specifically can be included：

According to first user information, the content that first user information is included is used as the 4th content recommendation.

Exemplary, if getting first user has the first course of study, can analyze the 4th content recommendation of acquisition is " reviewing the first course "；Or, if getting first user there are the first books of purchase, the 4th content recommendation of acquisition can be analyzed For " reading the first books "；Or, if getting the hobby of first user to play soccer, acquisition the 4th can be analyzed and pushed away Content is recommended for " playing soccer " etc.；

It should be noted that above-mentioned say according to the form that first user information analysis the 4th content recommendation of acquisition is example Bright, the process to obtaining the 4th content recommendation according to first user information analysis is not specifically limited, every according to first User profile and preset strategy analysis obtain the process of content recommendation and belong to protection scope of the present invention.

Preferably, if not including the 3rd content recommendation in public recommending data storehouse, can not be in the system of facility for study Default configuration, determines it is that self-built content recommendation or system generate content recommendation according to the selection of user；It is described referring to Fig. 2 First user presets the method in the personal recommending data storehouse for generating first user can also be included：

B4, show that self-built content recommendation or system generate content recommendation in the user interface, for first user selection；

Wherein, user interface can include the interactive interface for user operation that facility for study is provided by screen.

Wherein, if first user selects self-built content recommendation, first user can be received self-built content recommendation is touched Operation, and the default configuration in the system of facility for study are sent out, the 3rd content recommendation is preserved to the personal of first user and is recommended number According to storehouse or the personal recommending data storehouse and public recommending data storehouse of first user；

Optionally, in all embodiments of the invention, when first user selects self-built content recommendation, first user may be used also To illustrate to the addition of the 3rd content recommendation, the explanation can include the information such as label, the evaluation of the 3rd content recommendation；

Accordingly, the 3rd content recommendation is preserved to the personal recommending data storehouse of first user or the individual of first user and is pushed away Database and public recommending data storehouse are recommended, can be included, the explanation of the 3rd content recommendation and the 3rd content recommendation is preserved to The personal recommending data storehouse of one user or the personal recommending data storehouse of first user and public recommending data storehouse.

It is further alternative, if first user selects self-built content recommendation, first user can be received to self-built recommendation The trigger action of content, but the not content recommendation save location of default configuration the 3rd in the system of facility for study, but according to One user's selects to determine the save location of the 3rd content recommendation；Referring to Fig. 2, the first user is default to generate first user The method in personal recommending data storehouse can also include：

B5, display keep data privately owned or shared data, for first user selection；

3rd content recommendation is preserved to first and used by b6, reception first user for the trigger action for keeping data privately owned The personal recommending data storehouse at family；

B7, reception first user preserve the 3rd content recommendation to first user for the trigger action of shared data Personal recommending data storehouse and public recommending data storehouse.

Further, in step b4, if first user selects system to generate content recommendation, first user is received to being System generates the trigger action of content recommendation, and execution step b8～b9：

B8, according to first user information analysis obtain the 4th content recommendation；

Wherein, the 4th content recommendation is any one in first user information generation content recommendation.

B9, by the 4th content recommendation preserve to first user personal recommending data storehouse or the personal of first user recommend number According to storehouse and the public recommending data storehouse.

Wherein, for the personal recommending data storehouse or the individual of first user that preserve the 4th content recommendation to first user Recommending data storehouse and the public recommending data storehouse, can be configured the preservation of the 4th content recommendation by the system default of facility for study Position, it is also possible to which the save location of the 4th content recommendation is determined according to the selection of first user；For the choosing according to first user The save location for determining the 4th content recommendation is selected, the method that the 3rd content recommendation is determined according to the selection of first user with b5～b7 Similar, here is no longer repeated.

C, the second content recommendation for receiving first user selection；

Wherein, the second content recommendation belongs to the personal recommending data storehouse of first user；

D, the second content recommendation is preserved to presetting database, and it is corresponding with the ID of first user.

Exemplary, if first user had preset first before this uses facility for study in facility for study The personal recommending data storehouse of user is simultaneously selected, then in presetting database comprising first user ID and with Family identifies corresponding content recommendation, then execution step 102；

If first user uses for the first time the facility for study, or first user not to have before this uses facility for study Have and the personal recommending data storehouse of first user is preset in facility for study and is selected, then then do not wrap in presetting database ID containing first user and content recommendation corresponding with ID, then execution step 103.

If the 102, the ID comprising first user and content recommendation corresponding with ID in presetting database, Content recommendation corresponding with the ID of first user in presetting database is recommended into output, so that first user Practise；

Wherein, the form of output is recommended to include but is not limited to word, sound, picture, video, animation, interaction etc.；Tool The form of the recommendation output of body can according to the actual requirements determine that the present invention is not specifically limited for the form for recommending output It is fixed.

If the 103, the ID not comprising first user and content recommendation corresponding with ID in presetting database, Then according to the characteristic parameter and the first preset strategy of first user, analysis obtains the first content recommendation；And by the first content recommendation Recommend output, so that first user is learnt.

Wherein, the first content recommendation is that the recommendation for obtaining is analyzed according to the characteristic parameter and the first preset strategy of first user Any one in content.

Wherein, the characteristic parameter of the first user can include but is not limited to the study stage of growth of first user, Study content that study situation, the first user of one user is selected in system etc.；Wherein, the study situation of the first user Interior perhaps first user can be learnt including first user and do not learnt content.

Preferably, the content for being included according to the characteristic parameter of first user, according to the characteristic parameter and first of first user Preset strategy, analysis obtains the first content recommendation, can include,

If the characteristic parameter of first user includes the stage of growth data of first user, by the characteristic parameter with first user Corresponding content is used as the first content recommendation；Or,

If the characteristic parameter of first user has learned content including first user, first user characteristic parameter is included in The derivative content held is used as the first content recommendation；

Wherein, the derivative content of the content that first user characteristic parameter includes can include：First user characteristic parameter bag The next chapter content of the content for including, or the keyword identical content of the content included with first user characteristic parameter, or The identic content of the content included with first user characteristic parameter；The present invention is not specifically limited this.

Or,

If the characteristic parameter of first user includes that first user does not learn the study content that content or first user are selected, The content that the characteristic parameter of first user is included is used as the first content recommendation.

Exemplary, if first user uses facility for study for the first time, the characteristic parameter that can get first user is Age bracket in the study stage of growth data of first user；According to the age in the study stage of growth data of first user Section, analysis obtains the study content of the child for being adapted to the age bracket as the first content recommendation；

If first user non-first time uses facility for study, then, of first user has been have recorded in facility for study Habit situation, obtains in the study situation of first user and does not learn content as the characteristic parameter of first user；Use according to first Content is not learned in the study situation at family, the content that this is not learned is used as the first content recommendation；

Or, if first user non-first time uses facility for study, then, first user has been have recorded in facility for study Study situation, obtain first user study situation in learn content as the characteristic parameter of first user；According to Association's content in the study situation of one user, the association that this has been learned content learns content as the first content recommendation.

Above-mentioned example is merely illustrative characteristic parameter and the first preset strategy according to first user, and analysis is obtained and the The optional mode of the first content recommendation of the characteristic parameter matching of one user, but the mode not limited to this in practical application； In practical application, the content of the first preset strategy can determine according to the actual requirements, and the present invention is not specifically limited this.

It should be noted that step 102 and step 103 are coordination, the restriction without sequencing.

It should be noted that the recommendation method that all embodiments of the invention are provided, can be when user uses facility for study Use at any time, the present invention is not specifically limited this；

Also, it should be noted that all embodiments of the invention provide recommendation method in content recommendation output time, can During being study next time, or when next stage learns, the present invention is not defined to this；Specific content recommendation Output time, can be determined according to the actual requirements, and the present invention is not specifically limited this.

The present invention provides a kind of recommendation method, by the ID according to first user, searches presetting database；If pre- If in database comprising first user ID and content recommendation corresponding with ID, then by presetting database with The corresponding content recommendation of ID of first user recommends output, so that first user is learnt；If presetting database In not comprising first user ID and content recommendation corresponding with ID, then according to the characteristic parameter of first user And first preset strategy analysis obtain the first content recommendation；And recommend the first content recommendation to export, so that first user enters Row study.The study content that realization is learnt using the user of facility for study, is consistent with the learning demand and Expectation of Learning of user, And then user is improved using the results of learning of facility for study.

Embodiment two

The embodiment of the present invention two provides another kind of recommendation method, and with user A facility for study, and user's A Non-preconditions are used As a example by content recommendation, the recommendation method shown in Fig. 1 is described in detail, referring to Fig. 3, methods described can include：

301st, according to the ID of user A, presetting database is searched；

For example, user A is to use facility for study for the first time, and before the personal recommending data storehouse of pre-set user A is not simultaneously Selected, then be the ID without first user and content recommendation corresponding with ID in presetting database 's；If user A triggering facility for studies are recommended, then in the ID according to user A, inquiry presetting database, in advance If not including the ID and content recommendation corresponding with ID of user A in database, then execution step 302.

302nd, the characteristic parameter of user A is obtained；

For example, the subscriber data filled according to user A, it is known that the age of user A is 2 years old, meets characteristic parameter " age bracket 1～3 ", then the characteristic parameter for getting user A is " age bracket 1～3 ".

303rd, according to the characteristic parameter of user A, analysis obtains the first content recommendation matched with the characteristic parameter of user A.

It is assumed that according to data statistics, the high study content of age bracket child's interest-degree of 1 years old～3 years old is followed successively by listens event Thing, nursery rhymes etc.；

For example, according to the characteristic parameter " age bracket 1～3 " of user A, can analyze and get and user's A characteristic parameters The content recommendation " listening story " matched somebody with somebody.

304th, the first content recommendation is recommended to export, so that user A is learnt.

For example, by animation form, by showing that " child, we come together to listen story in the screen of facility for study ！" and the speech play language, content recommendation " listening story " is recommended to export, so that user A is learnt.

305th, record updates the characteristic parameter of user A.

Specifically, the characteristic parameter record of user is updated in user characteristics parameter database, it is ensured that in recommendation next time When, the characteristic parameter of the user of acquisition is most accurately, to improve the accuracy of recommendation next time.

The present invention provides a kind of recommendation method, by the ID according to first user, searches presetting database；According to The characteristic parameter of first user and the analysis of the first preset strategy obtain the first content recommendation；And recommend the first content recommendation defeated Go out, so that first user is learnt.The study content that realization is learnt using the user of facility for study, the study with user Demand and Expectation of Learning are consistent, and then user is improved using the results of learning of facility for study.

Embodiment three

The embodiment of the present invention three provides another kind of recommendation method, similar with embodiment two, is set using study with user A As a example by standby, the recommendation method shown in Fig. 1 is described in detail, but in the present embodiment, user A bases in facility for study The demand of child and the expectation of the head of a family, preset personalized content recommendation；Referring to Fig. 4, methods described can include：

401st, trigger action of receive user A to setting content recommendation；

For example, when user A uses facility for study, the demand and expectation according to user A presets individual character in facility for study The content recommendation of change, now, user A arranges boundary by the trigger action to arranging content recommendation, the recommendation into facility for study Face.

402nd, the 3rd content recommendation of receive user A input；

For example, there is the book of a papery in user A family《Lobo》, and user A expects that child uses one section of facility for study Afterwards, may exit off facility for study study《Lobo》This this book, then, the 3rd content recommendation of user A inputs is reading《Big ash Wolf》.

Whether the 403rd, search in public recommending data storehouse includes the 3rd content recommendation；

For example,《Lobo》The awfully hot door of this this book, everybody can read, then, will include in public database 《Lobo》This this book, then execution step 404；

Or,《Lobo》This this book has just been listed or other reasonses, and the few read, then, in public database Would not include《Lobo》This this book, then execution step 405.

404th, the 3rd content recommendation is preserved to the personal recommending data storehouse of user A.

Wherein, if public recommending data storehouse includes the 3rd content recommendation, the 3rd content recommendation is preserved to user A's Personal recommending data storehouse.

For example,《Lobo》The awfully hot door of this this book, everybody can read, and common data is found in step 403 Storehouse includes《Lobo》This this book, then read the 3rd content recommendation《Lobo》Preserve to the personal of user A and recommend number According to storehouse.

405th, show that self-built content recommendation or system generate content recommendation in the screen of facility for study, for user A choosings Select；

For example,《Lobo》This this book has just been listed or other reasonses, and the few read, and is searched in step 403 public Do not include in database altogether《Lobo》This this book；At this point it is possible to show in the screen of facility for study self-built content recommendation or System generates content recommendation, for user A selections.

If user A selects self-built content recommendation, execution step 406；If user A selects system to generate content recommendation, Execution step 409.

406th, show and keep data privately owned or shared data, for user A selections；

For example, after user A selects self-built content recommendation, then can determine that user A needs to protect the 3rd content recommendation Deposit to the personal recommending data storehouse of user A；

At this point it is possible to show holding, data are privately owned or shared data, for user A select by the 3rd content recommendation only preserve to The personal recommending data storehouse of user A, had still both preserved the 3rd content recommendation to the personal recommending data storehouse of user A, also by Three content recommendations are preserved to public database to be shared with other users.

If user A selects to keep data privately owned, execution step 407；If user A selects shared data, execution step 408。

407th, the 3rd content recommendation is preserved to the personal recommending data storehouse of user A；

408th, the 3rd content recommendation is preserved to the personal recommending data storehouse and public recommending data storehouse of first user.

409th, the 4th content recommendation is obtained according to the information analysis of user A；

For example,《Lobo》This this book has just been listed or other reasonses, and the few read, and is searched in step 403 public Do not include in database altogether《Lobo》This this book, and user A selects in step 405 system to generate content recommendation, then, it is System obtains 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》, So according to this information, system automatically generates the 4th content recommendation reading for meeting user's request《Lobo》.

410th, the 4th content recommendation is preserved to the personal recommending data storehouse of user A.

It is assumed that by step 402～step 410, user A saves following content recommendations in personal recommending data storehouse： Read《Lobo》, listen music《Only have mother good in the world》, play games etc..

411st, the content recommendation in the personal recommending data storehouse of user A is shown, for user A selections；

412nd, the second content recommendation that receive user A is selected；

It is assumed that the next study content that user A is selected is：Listen music《Only have mother good in the world》.

413rd, the second content recommendation is preserved to presetting database, and it is corresponding with the ID of user A.

So far, user A has completed to preset personalized recommendation content.When child is learnt using the Account Logon of user A During equipment, then 414～step 415 of execution step.

414th, according to the ID of user A, presetting database is searched

415th, content recommendation corresponding with the ID of user A in presetting database is recommended into output, so that first User is learnt.

For example, in presetting database, saved in ID and the recommendation corresponding with ID of user A Hold：Listen music《Only have mother good in the world》.

So, then content recommendation corresponding with the ID of user A in presetting database listened into music《There was only mother in the world Mother is good》Recommend output, so that first user is learnt.

416th, record updates the characteristic parameter of user A.

The present invention provides a kind of recommendation method, by the ID according to first user, searches presetting database；Will be pre- If content recommendation corresponding with the ID of first user recommends output in database, so that first user is learnt. The study content that realization is learnt using the user of facility for study, is consistent with the learning demand and Expectation of Learning of user, and then makes Obtain user to improve using the results of learning of facility for study.

Example IV

The embodiment of the present invention four provides a kind of commending system 50, and commending system 50 can be the part of facility for study or complete Portion, referring to Fig. 5, commending system 50 can include：

Searching unit 501, for according to the ID of first user, searching presetting database；Wherein, first user is The current user for logging in facility for study；The ID of presetting database including at least one user and with the ID pair The content recommendation answered；Content recommendation include using facility for study digital content or do not use the physical contents of facility for study；

Output unit 502, if in presetting database comprising the ID of first user and corresponding with ID Content recommendation, then corresponding with the ID of first user content recommendation in presetting database is recommended into output so that First user is learnt；

Analysis acquiring unit 503, if marking for the ID not comprising first user in presetting database and with user Know corresponding content recommendation, then according to the characteristic parameter and the first preset strategy of first user, analysis obtains the first content recommendation；

The output unit 502 can be also used for, and the first content recommendation be recommended to export, so that first user Practise.

Optionally, the characteristic parameter of the first user includes：The stage of growth data of first user or first user Study situation or first user select study content；Wherein, the study situation of first user is learned including first user Perhaps first user does not learn content in practising.

Further, the analysis acquiring unit 503 specifically can be used for,

If the characteristic parameter of first user has learned content including first user, first user characteristic parameter is included in The derivative content held is used as the first content recommendation；Or,

If the characteristic parameter of first user includes that first user does not learn the study content that content or first user are selected, The content that the characteristic parameter of first user is included as the first content recommendation.

Further, referring to Fig. 6, the system 50 can also also include,

Receiving unit 504, for receiving first user to arranging the trigger action of content recommendation；

Display unit 505, for according to the ID of first user, in showing the personal recommending data storehouse of first user Content recommendation for first user selection；Wherein, the personal recommending data storehouse of first user includes at least one first user Default content recommendation；

The receiving unit 504 can be also used for, and receive the second content recommendation that first user is selected；Wherein, second push away Recommend the personal recommending data storehouse that content belongs to first user；

Storage unit 506, for the second content recommendation to be preserved to presetting database, and with the ID of first user Correspondence.

Optionally, the receiving unit 504 can be also used for, and receive the 3rd content recommendation of first user input；

The searching unit 501 can be also used for, and whether search in public recommending data storehouse includes the 3rd content recommendation；Its In, public recommending data storehouse includes at least one content recommendation；

The storage unit 506 can be also used for, if public recommending data storehouse includes the 3rd content recommendation, by the 3rd Content recommendation is preserved to the personal recommending data storehouse of first user.

Optionally, if not including the 3rd content recommendation in public recommending data storehouse, the storage unit 506 can also be used In,

3rd content recommendation is preserved personal recommending data storehouse or the personal recommending data of first user to first user Storehouse and public recommending data storehouse；Or,

The analysis acquiring unit 503 can be also used for, and according to first user information and the second preset strategy, analysis is obtained 4th content recommendation；

The storage unit 506 can be also used for, and the 4th content recommendation be preserved to the personal recommending data of first user Storehouse or the personal recommending data storehouse and public recommending data storehouse of first user.

Further, analyze acquiring unit 503 can be also used for,

Optionally, the display unit 505 can be also used for,

If in public recommending data storehouse include the 3rd content recommendation, show in the user interface self-built content recommendation or System generates content recommendation, for first user selection.

Accordingly,

The receiving unit 504 can be also used for, and receive trigger action of the first user to self-built content recommendation；

The storage unit 506 can be also used for, and the 3rd content recommendation be preserved to the personal recommending data of first user Storehouse or the personal recommending data storehouse and public recommending data storehouse of first user；Or,

The receiving unit 504 can be also used for, and receive the trigger action that first user generates content recommendation to system；

The analysis acquiring unit 503 can be also used for, and according to first user information analysis the 4th content recommendation is obtained；

Optionally, the display unit 505 can be also used for, and shows and keeps data privately owned or shared data, for first User selects；

The receiving unit 504 can be also used for, and receive first user for the trigger action for keeping data privately owned；

The storage unit 506 can be also used for, and the 3rd content recommendation be preserved to the personal recommending data of first user Storehouse；Or,

The receiving unit 504 can be also used for, and receive first user for the trigger action of shared data；

The storage unit 506 can be also used for, and the 3rd content recommendation be preserved to the personal recommending data of first user Storehouse and public recommending data storehouse.

Preferably, the analysis acquiring unit 503 specifically can be used for,

Obtain the characteristic parameter of first user；

According to the characteristic parameter of first user, analysis is obtained in the first recommendation matched with the characteristic parameter of first user Hold.

Optionally, referring to Fig. 6, the commending system 50 can also include, record updating block 507, update for recording The characteristic parameter of the first user.

The present invention provides a kind of commending system 50, by the ID according to first user, searches presetting database；If ID comprising first user and content recommendation corresponding with ID in presetting database, then by presetting database Content recommendation corresponding with the ID of first user recommends output, so that first user is learnt；If preset data ID not comprising first user and content recommendation corresponding with ID in storehouse, then join according to the feature of first user Number and the analysis of the first preset strategy obtain the first content recommendation；And recommend the first content recommendation to export, so that first user Learnt.The study content that realization is learnt using the user of facility for study, the learning demand and Expectation of Learning phase with user Symbol, and then user is improved using the results of learning of facility for study.

One of ordinary skill in the art will appreciate that：Realizing all or part of step of said method embodiment can pass through Completing, aforesaid program can be stored in a computer read/write memory medium the related hardware of programmed instruction, the program Upon execution, the step of including said method embodiment is performed；And aforesaid storage medium includes：ROM, RAM, magnetic disc or light Disk etc. is various can be with the medium of store program codes.

The above, the only specific embodiment of the present invention, but protection scope of the present invention is not limited thereto, any Those familiar with the art the invention discloses technical scope in, change or replacement can be readily occurred in, all should contain Cover within protection scope of the present invention.Therefore, protection scope of the present invention should be defined by the scope of the claims.

Claims

1. a kind of recommendation method, it is characterised in that include：

First user is received to arranging the trigger action of content recommendation；

According to the ID of the first user, show content recommendation in the personal recommending data storehouse of the first user with Select for the first user；Wherein, the personal recommending data storehouse of the first user includes at least one described first user Default content recommendation；

Receive the second content recommendation that the first user is selected；Wherein, second content recommendation belongs to the first user Personal recommending data storehouse；

Second content recommendation is preserved to presetting database, and it is corresponding with the ID of the first user；

According to the ID of the first user, presetting database is searched；Wherein, the first user logs in study for current The user of equipment；The presetting database includes the ID of at least one user and recommendation corresponding with the ID Content；The content recommendation include using the facility for study digital content or do not use in the entity of the facility for study Hold；

If including in the presetting database in ID and the recommendation corresponding with the ID of the first user Hold, then content recommendation corresponding with the ID of the first user in the presetting database is recommended into output, so that The first user is learnt；

If the ID not comprising the first user and recommendation corresponding with the ID in the presetting database Content, then according to the characteristic parameter and the first preset strategy of the first user, analysis obtains the first content recommendation, and will be described First content recommendation recommends output, so that the first user is learnt.

2. recommendation method according to claim 1, it is characterised in that the characteristic parameter of the first user includes：It is described In the study that either the study situation of the first user or the first user are selected of the stage of growth data of first user Hold；Wherein, the study situation of the first user including the first user learnt in perhaps described first user do not learn Content.

3. recommendation method according to claim 2, it is characterised in that the characteristic parameter according to the first user and The analysis of first preset strategy obtains the first content recommendation, including：

If the stage of growth data of the characteristic parameter of the first user including the first user, by with the first user The corresponding content of characteristic parameter is used as the first content recommendation；Or,

If the characteristic parameter of the first user has learned content including the first user, by the first user characteristic parameter Including content derivative content as the first content recommendation；Or,

If the characteristic parameter of the first user includes that the first user does not learn what content or the first user were selected Study content, the content that the characteristic parameter of the first user is included as the first content recommendation.

4. recommendation method according to claim 1, it is characterised in that marked according to the user of the first user described Know, it is described before showing the content recommendation in the personal recommending data storehouse of the first user for first user selection Method also includes,

Receive the 3rd content recommendation of the first user input；

Whether search in public recommending data storehouse includes the 3rd content recommendation；Wherein, the public recommending data storehouse includes At least one content recommendation；

If the public recommending data storehouse includes the 3rd content recommendation, the 3rd content recommendation is preserved to described The personal recommending data storehouse of first user.

5. recommendation method according to claim 4, it is characterised in that if not including in the public recommending data storehouse described 3rd content recommendation, methods described also includes,

3rd content recommendation is preserved personal recommending data storehouse or the individual of the first user to the first user Recommending data storehouse and the public recommending data storehouse；Or,

According to the first user information and the second preset strategy, analysis obtains the 4th content recommendation, the described 4th is recommended interior Hold and preserve to the personal recommending data storehouse or the personal recommending data storehouse of the first user of the first user and described public Recommending data storehouse；Wherein, the information of the first user includes user.

6. recommendation method according to claim 5, it is characterised in that described pre- according to the first user information and second If analysis of strategies obtain the 4th content recommendation, including：

According to the first user information, the content that the first user information is included is used as the 4th content recommendation.

7. the recommendation method according to claim 5 or 6, it is characterised in that methods described also includes,

If not including the 3rd content recommendation in the public recommending data storehouse, show in the user interface in self-built recommendation Perhaps system generates content recommendation, for first user selection；

Accordingly, it is described to preserve the 3rd content recommendation to the personal recommending data storehouse or described first of the first user The personal recommending data storehouse of user and the public recommending data storehouse；Or, obtain the according to the first user information analysis Four content recommendations, the 4th content recommendation is preserved personal recommending data storehouse or the first user to the first user Personal recommending data storehouse and the public recommending data storehouse, including,

Trigger action of the first user to self-built content recommendation is received, the 3rd content recommendation is preserved to described first The personal recommending data storehouse of user or the personal recommending data storehouse of the first user and the public recommending data storehouse；Or,

The trigger action that the first user generates content recommendation to system is received, is obtained according to the first user information analysis 4th content recommendation, and the 4th content recommendation is preserved to the personal recommending data storehouse or described first of the first user The personal recommending data storehouse of user and the public recommending data storehouse.

8. the recommendation method according to claim 5 or 6, it is characterised in that it is described by the 3rd content recommendation preserve to The personal recommending data storehouse of the first user or the personal recommending data storehouse of the first user and the public recommending data Storehouse, including,

Show and keep data privately owned or shared data, for first user selection；

The first user is received for keeping the privately owned trigger action of data, the 3rd content recommendation is preserved to described the The personal recommending data storehouse of one user；Or,

Trigger action of the first user for shared data is received, the 3rd content recommendation is preserved to described first and is used The personal recommending data storehouse at family and the public recommending data storehouse.

9. the recommendation method according to any one of claim 1-6, it is characterised in that methods described also includes：

Record updates the characteristic parameter of the first user.

10. a kind of commending system, it is characterised in that include：

Receiving unit, for receiving first user to arranging the trigger action of content recommendation；

Display unit, for according to the ID of the first user, showing the personal recommending data storehouse of the first user In content recommendation select for the first user；Wherein, the personal recommending data storehouse of the first user includes at least one The default content recommendation of the item first user；

The receiving unit is additionally operable to, and receives the second content recommendation that the first user is selected；Wherein, in second recommendation Appearance belongs to the personal recommending data storehouse of the first user；

Storage unit, for second content recommendation to be preserved to presetting database, and marks with the user of the first user Know correspondence；

Searching unit, for according to the ID of the first user, searching presetting database；Wherein, the first user For the current user for logging in facility for study；The ID of the presetting database including at least one user and with the user Identify corresponding content recommendation；The content recommendation include using the facility for study digital content or do not use the study The physical contents of equipment；

Output unit, if in the presetting database comprising the first user ID and with the ID Corresponding content recommendation, then recommend content recommendation corresponding with the ID of the first user in the presetting database Output, so that the first user is learnt；

Analysis acquiring unit, if in the presetting database not comprising the first user ID and with the use Family identifies corresponding content recommendation, then according to the characteristic parameter and the first preset strategy of the first user, analysis obtains first Content recommendation；

The output unit is additionally operable to, and first content recommendation is recommended to export, so that the first user is learnt.

11. commending systems according to claim 10, it is characterised in that the characteristic parameter of the first user includes：Institute State the stage of growth data study that either the study situation of the first user or the first user are selected of first user Content；Wherein, the study situation of the first user has learnt interior perhaps described first user including the first user Practise content.

12. commending systems according to claim 11, it is characterised in that the analysis acquiring unit specifically for,

13. commending systems according to claim 10, it is characterised in that

The searching unit is additionally operable to, and whether search in public recommending data storehouse includes the 3rd content recommendation；Wherein, it is described Public recommending data storehouse includes at least one content recommendation；

The storage unit is additionally operable to, if the public recommending data storehouse includes the 3rd content recommendation, by described Three content recommendations are preserved to the personal recommending data storehouse of the first user.

14. commending systems according to claim 13, it is characterised in that if not including institute in the public recommending data storehouse The 3rd content recommendation is stated, the storage unit is additionally operable to,

The analysis acquiring unit is additionally operable to, and according to the first user information and the second preset strategy, analysis obtains the 4th and pushes away Recommend content；

The storage unit is additionally operable to, by the 4th content recommendation preserve to the first user personal recommending data storehouse or The personal recommending data storehouse of the first user and the public recommending data storehouse.

15. commending systems according to claim 14, it is characterised in that the analysis acquiring unit is additionally operable to,

16. commending systems according to claims 14 or 15, it is characterised in that the display unit is additionally operable to,

Accordingly,

The storage unit is additionally operable to, by the 3rd content recommendation preserve to the first user personal recommending data storehouse or The personal recommending data storehouse of the first user and the public recommending data storehouse；Or,

17. commending systems according to claims 14 or 15, it is characterised in that

The storage unit is additionally operable to, and the 3rd content recommendation is preserved to the personal recommending data storehouse of the first user； Or,

The storage unit is additionally operable to, by the 3rd content recommendation preserve to the first user personal recommending data storehouse and The public recommending data storehouse.

18. commending systems according to any one of claim 10-15, it is characterised in that the system also includes：