CN106131684A - A kind of content recommendation method and terminal - Google Patents
A kind of content recommendation method and terminal Download PDFInfo
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- CN106131684A CN106131684A CN201610483000.0A CN201610483000A CN106131684A CN 106131684 A CN106131684 A CN 106131684A CN 201610483000 A CN201610483000 A CN 201610483000A CN 106131684 A CN106131684 A CN 106131684A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
- H04N21/4508—Management of client data or end-user data
- H04N21/4532—Management of client data or end-user data involving end-user characteristics, e.g. viewer profile, preferences
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/441—Acquiring end-user identification, e.g. using personal code sent by the remote control or by inserting a card
- H04N21/4415—Acquiring end-user identification, e.g. using personal code sent by the remote control or by inserting a card using biometric characteristics of the user, e.g. by voice recognition or fingerprint scanning
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
- H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
- H04N21/4668—Learning process for intelligent management, e.g. learning user preferences for recommending movies for recommending content, e.g. movies
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- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- General Health & Medical Sciences (AREA)
- Human Computer Interaction (AREA)
- Theoretical Computer Science (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The embodiment of the invention discloses a kind of content recommendation method and terminal, wherein, the method includes: identifies login user, and obtains the preference type of described login user, wherein, the preference type of described login user is to add up the content that described login user is interested to obtain；Obtain recommended, and according to the preference type of described login user described recommended screened thus obtain target recommended；Described target recommended is pushed to described login user.Implement the embodiment of the present invention, it is possible to realize personalized recommendation program exactly for different user groups.
Description
Technical field
The present invention relates to Internet technical field, particularly relate to a kind of content recommendation method and terminal.
Background technology
Along with the fast development of cultural industry, TV programme become increasingly abundant.User is in the TV programme in the face of becoming increasingly abundant
Time become at a loss as to what to do, do not know how to select oneself program interested to watch from numerous TV programme.Therefore, have
The program that user is interested is recommended, to user, in effect ground, becomes the vital task of program recommendation system.
At present, the data of some video website or Web TV first counting user TV reception, then according to system
The data watching program of meter calculate program audience rating, rear line and recommend the program that audience ratings is high.
But, practice finds, recommends the program that audience ratings is high simply to meet the demand of major part user, not to user
The demand of some specific groups can be met.Such as, the colony of current TV reception is mainly youngster, and youngster is mainly seen
Seeing youth image drama, and old people prefers to watch drama class program, child prefers to watch animated films, therefore, when to user
When recommending the higher youth image drama of audience ratings, it may only be possible to meet youthful demand, can for old man or child
Can not be to like very much.Therefore, program audience rating is utilized to recommend the method for program can not be accurate for different user group to user
True ground personalized recommendation program.
Summary of the invention
The embodiment of the invention discloses a kind of content recommendation method and terminal, it is possible to realize standard for different user groups
True ground personalized recommendation program.
Embodiment of the present invention first aspect discloses a kind of content recommendation method, including:
Identification login user, and obtain the preference type of described login user, wherein, the preference type of described login user
It is to add up the content that described login user is interested to obtain；
Obtain recommended, and according to the preference type of described login user described recommended screened thus obtain
Obtain target recommended；
Described target recommended is pushed to described login user.
Embodiment of the present invention second aspect discloses a kind of terminal, including:
Recognition unit, is used for identifying login user；
First acquiring unit, for obtaining the preference type of the described login user of described recognition unit identification, wherein, institute
The preference type stating login user is to add up the content that described login user is interested to obtain；And push away for acquisition
Recommend object；
First screening unit, for obtaining the preference type of described login user to described according to described first acquiring unit
Recommended carries out screening thus obtains target recommended；
Recommendation unit, uses for the described target recommended of described first screening unit screening is pushed to described login
Family.
In the embodiment of the present invention, terminal is by identifying that the login user logging in this terminal obtains the hobby of this login user
Type, this terminal filters out target recommended further according to the preference type of this login user in the recommended obtained,
After this terminal this target recommended is recommended this login user.Visible, implement the embodiment of the present invention, this terminal is not in the face of
With log in this terminal login user time, can realize accurately for different user groups for the hobby of each login user
Ground personalized recommendation content.
Accompanying drawing explanation
For the technical scheme being illustrated more clearly that in the embodiment of the present invention, below by use required in embodiment
Accompanying drawing is briefly described, it should be apparent that, the accompanying drawing in describing below is some embodiments of the present invention, general for this area
From the point of view of logical technical staff, on the premise of not paying creative work, it is also possible to obtain other accompanying drawing according to these accompanying drawings.
Fig. 1 is the schematic flow sheet of a kind of content recommendation method disclosed in the embodiment of the present invention；
Fig. 1 (a) is the schematic diagram that a kind of recommended disclosed in the embodiment of the present invention shows in the terminal；
Fig. 2 is the schematic flow sheet of another kind of content recommendation method disclosed in the embodiment of the present invention；
Fig. 3 is the structural representation of a kind of terminal disclosed in the embodiment of the present invention；
Fig. 4 is the structural representation of another kind of terminal disclosed in the embodiment of the present invention；
Fig. 5 is the structural representation of another kind of terminal disclosed in the embodiment of the present invention；
Fig. 6 is the structural representation of another kind of terminal disclosed in the embodiment of the present invention.
Detailed description of the invention
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
Describe, it is clear that described embodiment is a part of embodiment of the present invention rather than whole embodiments wholely.Based on this
Embodiment in bright, the every other enforcement that those of ordinary skill in the art are obtained under not making creative work premise
Example, broadly falls into the scope of protection of the invention.
It should be noted that the term used in embodiments of the present invention is only merely for the mesh describing specific embodiment
, and it is not intended to be limiting the present invention." one of singulative used in the embodiment of the present invention and appended claims
Kind ", " described " and " being somebody's turn to do " be also intended to include majority form, unless context clearly shows that other implications.It is also understood that this
Any or all possible group that the term "and/or" used in literary composition refers to and comprises one or more project of listing being associated
Close.
The embodiment of the invention discloses a kind of content recommendation method and terminal, it is possible to realize standard for different user groups
True ground personalized recommendation content.It is described in detail individually below.
Referring to Fig. 1, Fig. 1 is the schematic flow sheet of a kind of content recommendation method disclosed in the embodiment of the present invention.Such as Fig. 1 institute
Showing, this content recommendation method may comprise steps of.
S101, terminal recognition login user, and obtain the preference type of this login user, wherein, the happiness of this login user
Good type is to add up the content that this login user is interested to obtain.
This terminal can be to run Android operation system, iOS operating system, Windows operating system or other operations
The mobile device of system, such as mobile phone, removable computer, panel computer, personal digital assistant (Personal Digital
Assistant, PDA), intelligent television etc..
The identity such as the fingerprint of at least one user, vocal print, iris or password that can prestore in this terminal or server are tested
Card information.When login user logs in this terminal, this login user can be to this terminal input fingerprint, vocal print, iris or password
Etc. authentication information, if the identity of one of them user that the authentication information of this login user input prestores with this terminal
Checking information matches, then this login user can be identified.
As shown in the table, in smart mobile phone, the authentication of prestored user A, user B and user C is believed in the form of a list
Breath.Wherein, the authentication information of user A is the fingerprint A of user A, and the authentication information of user B is the vocal print B of user B,
The authentication information of user C is password 100000.
| User | Authentication information |
| User A | Fingerprint A |
| User B | Vocal print B |
| User C | 100000 |
If user is input fingerprint A when logging in this smart mobile phone, then this smart mobile phone can identify that this login user is for using
Family A；If user is input vocal print B when logging in this smart mobile phone, then this smart mobile phone can identify that this login user is user B；
If user's input password 100001 when logging in this smart mobile phone, because not prestoring password 100001 at this smart mobile phone, then should
Smart mobile phone cannot identify this login user.
Can be prestored in this terminal or server the content interested at least one user.Wherein, user is interested
Content can be the user of this terminal record video that once used this terminal to watch, user once play music, user
The TV programme etc. that the article once read and user once watched.
Content as shown in the table, interested for prestored user A and user B in the form of a list in smart mobile phone.Wherein,
User A uses this smart mobile phone once TV reception " ultimate challenge ", " having requested refrigerator " and " brother of running "；User
B uses this smart mobile phone once to play music " worm flies ", " Limax and golden orioles ", " two tigers " and " number duck ".
The preference type of this login user can be program category, such as variety, TV or film etc.；Or, this login
The preference type of user can also be nursery rhymes, popular song or classic song etc..
With reference to from the point of view of above-mentioned two table, if user inputs fingerprint A on this smart mobile phone when logging in this smart mobile phone, then
This this login user of smart mobile phone identification is user A, then this smart mobile phone inquiry user A content " ultimate challenge " interested,
" having requested refrigerator " and " brother of running "；The content that user A is interested is added up and is drawn this login user by this terminal
Preference type is variety.
Optionally, if type corresponding to login user content interested is the most, this terminal may determine that this login is used
Proportion shared by each type in the type corresponding to content that family is interested, selects the type that proportion is big as this login
The preference type of user.
As shown in the table, user A uses this smart mobile phone once TV reception " ultimate challenge ", " having requested refrigerator "
" brother of running ", and this user A also uses this smart mobile phone to play music " those flowers " and " Take me to
your heart》。
Because this smart mobile phone judges that the type corresponding to content interested for this login user A is variety and popular sound
Happy.Wherein, variety has 3, and shared proportion is 3/5, and pop music has 2, and shared proportion is 2/5.Therefore, this intelligence hands
Machine selects proportion to be very popular the music preference type as this login user A.
Optionally, if type corresponding to login user content interested is the most, this terminal can this login user sense
All types corresponding to the content of interest is using the preference type as this login user.
From the point of view of upper table, the type corresponding to content that this login user A is interested is variety and pop music, because of
This, this smart mobile phone can be using variety and pop music as the preference type of this login user.
S102, this terminal obtain recommended, and sieve this recommended according to the preference type of this login user
Select thus obtain target recommended.
This terminal can obtain recommended from server or each big website etc., and wherein, this recommended can be service
Device or each big recommendation of websites are to various types of videos, music or the various TV programme etc. of user.Such as, variety " choose by the limit
War ", " having requested refrigerator " and " brother of running " etc., pop music " those flowers " and " Take me to your heart "
Deng.
This terminal filters out the preference type of this login user as target recommended in this recommended.
Such as, the recommended that smart mobile phone obtains is variety " ultimate challenge ", " having requested refrigerator " and " brother of running
Younger brother ", pop music " those flowers " and " Take me to your heart ".The preference type of this login user is variety, then
This smart mobile phone can be using " ultimate challenge ", " having requested refrigerator " and " brother of running " as target recommended.
This target recommended is pushed to this login user by S103, this terminal.
This terminal can be in this target recommended of the display screen display of this terminal.At smart mobile phone as shown in Fig. 1 (a)
This target recommended of display screen display, wherein, this target recommended be " ultimate challenge ", " having requested refrigerator " and
" brother of running ".
In the method described by Fig. 1, terminal is by identifying that the login user logging in this terminal obtains this login user
Preference type, this terminal further according to this login user preference type obtain recommended in filter out target recommend right
As, finally this target recommended is recommended this login user by this terminal.Visible, implement the method that Fig. 1 describes, this terminal exists
When the different login users logging in this terminal, can be that different user groups is real for the hobby of each login user
Personalized recommendation content the most exactly.
Referring to Fig. 2, Fig. 2 is the schematic flow sheet of another kind of content recommendation method disclosed in the embodiment of the present invention.Such as Fig. 2
Shown in, this content recommendation method may comprise steps of.
S201, terminal recognition login user, and obtain the preference type of this login user, wherein, the happiness of this login user
Good type is to add up the content that this login user is interested to obtain.
S202, this terminal obtain recommended.
This terminal can obtain recommended from server or each big website etc., and wherein, this recommended can be service
Device or each big recommendation of websites are to various types of videos, music or the various TV programme etc. of user.
S203, this terminal filter out m quasi-recommended from this recommended, and wherein, this quasi-recommended is that this is stepped on
Employing the recommended that the preference type at family is corresponding, this m is the positive integer more than zero.
For example, if the recommended that smart mobile phone obtains is variety " ultimate challenge ", " having requested refrigerator " and " runs
Brother ", pop music " those flowers " and " Take me to your heart ".The preference type of this login user is comprehensive
Skill, then the recommended that the preference type of this login user is corresponding is " ultimate challenge ", " having requested refrigerator " and " brother of running
Younger brother "；This smart mobile phone can filter out 2 quasi-recommendeds from the recommended that the preference type of this login user is corresponding
" ultimate challenge " and " having requested refrigerator ".
S204, this terminal obtain the recommendation weights of each quasi-recommended in this m quasi-recommended.
Optionally, the concrete executive mode of step S204 can be: obtains this m quasi-recommendation from the social media of user
The recommendation weights of each quasi-recommended in object.
The social media of this user can be microblogging, mhkc, circle of friends or QQ space etc..
These recommendation weights can be as the criterion the clicking rate of recommended, audience ratings or recommend index etc..If it is right to recommend than standard
As for " ultimate challenge " and " having requested refrigerator ", and the audience ratings of " ultimate challenge " is 1.65, the audience ratings of " having requested refrigerator "
2.127, therefore, the recommendation weights of " ultimate challenge " are 1.65, and the recommendation weights of " having requested refrigerator " are 2.127.
Optionally, the concrete step that performs of step S204 can also include:
Step 11), the connection recommending weights binding of each quasi-recommended in this m of this terminal judges quasi-recommended
It is whether people is to preset trusted contact.
Step 12) if, the contact recommending weights binding of each quasi-recommended in this m of this terminal quasi-recommended
This default trusted contact artificial, then increase the recommendation weights of each quasi-recommended in this m quasi-recommended.
Step 13) if, the contact recommending weights binding of each quasi-recommended in this m of this terminal quasi-recommended
People is not this default trusted contact, then reduce the recommendation weights of each quasi-recommended in this m quasi-recommended.
For example, if the recommendation index that " ultimate challenge " is in circle of friends is three stars, and " ultimate challenge " is recommended
Be the friend oneself being better, then smart mobile phone increases recommendation index to four star of " ultimate challenge "；If recommending " ultimate challenge "
Be a stranger, then smart mobile phone reduce " ultimate challenge " recommendation index to two star.
This m standard is pushed away by S205, this terminal according to the recommendation weights of each quasi-recommended in this m quasi-recommended
Recommend object and carry out descending.
Such as, if the recommendation weights of quasi-recommended " ultimate challenge " are 1.65, the recommendation weights of " having requested refrigerator " are
2.127, then according to the result recommending weights that these 2 quasi-recommendeds are carried out descending of each quasi-recommended be: " visit
Hold in the palm refrigerator " " ultimate challenge ".
The quasi-recommended of x before sequence number in this m quasi-recommended is selected by S206, this terminal according to descending result
Being selected as this target recommended, this x is the positive integer more than zero less than or equal to this m.
Such as, if these 2 quasi-recommendeds are dropped by the recommendation weights according to each quasi-recommended in step S205
The result of sequence arrangement is: " having requested refrigerator " " ultimate challenge ", and x=1, then this target recommended is " having requested refrigerator "；
If x=2, then this target recommended is " having requested refrigerator " and " ultimate challenge ".
This target recommended is pushed to this login user by S207, this terminal.
This terminal can be in this target recommended of the display screen display of this terminal.
In the method described by Fig. 2, it is higher that terminal selects recommendation weights according to the recommendation weights that recommended is corresponding
Recommended, and the recommended selected is recommended user.Visible, implement the method that Fig. 2 describes, terminal can be to use
Current popular program etc. is recommended at family.
Referring to Fig. 3, Fig. 3 is the structural representation of a kind of terminal disclosed in the embodiment of the present invention.Wherein, shown in Fig. 3
Terminal may include that
Recognition unit 301, is used for identifying login user.
The identity such as the fingerprint of at least one user, vocal print, iris or password that can prestore in this terminal or server are tested
Card information.When login user logs in this terminal, this login user can be to this terminal input fingerprint, vocal print, iris or password
Etc. authentication information, if the identity of one of them user that the authentication information of this login user input prestores with this terminal
Checking information matches, then this login user can be identified.
First acquiring unit 302, for obtaining the preference type of this login user that this recognition unit 301 identifies, wherein,
The preference type of this login user is to add up the content that this login user is interested to obtain；And be used for obtaining recommendation
Object.
Can be prestored in this terminal or server the content interested at least one user.Wherein, user is interested
Content can be the user of this terminal record video that once used this terminal to watch, user once play music, user
The TV programme etc. that the article once read and user once watched.
This terminal can obtain recommended from server or each big website etc., and wherein, this recommended can be service
Device or each big recommendation of websites are to various types of videos, music or the various TV programme etc. of user.Such as, variety " choose by the limit
War ", " having requested refrigerator " and " brother of running " etc., pop music " those flowers " and " Take me to your heart "
Deng.
First screening unit 303, for obtaining the preference type of this login user to this according to this first acquiring unit 302
Recommended carries out screening thus obtains target recommended.
This terminal filters out the preference type of this login user as target recommended in this recommended.
Such as, the recommended that smart mobile phone obtains is variety " ultimate challenge ", " having requested refrigerator " and " brother of running
Younger brother ", pop music " those flowers " and " Take me to your heart ".The preference type of this login user is variety, then
This smart mobile phone can be using " ultimate challenge ", " having requested refrigerator " and " brother of running " as target recommended.
Recommendation unit 304, this target recommended for this first screening unit 303 being screened is pushed to this login and uses
Family.
This terminal can be in this target recommended of the display screen display of this terminal.
Visible, implement the terminal that Fig. 3 describes, this terminal is when in the face of the different login users logging in this terminal, permissible
For the hobby of each login user, realize personalized recommendation content exactly for different user groups.
Referring to Fig. 4, Fig. 4 is the structural representation of another kind of terminal disclosed in the embodiment of the present invention, wherein, shown in Fig. 4
Terminal be that terminal as shown in Figure 3 is optimized and obtains.Compared with the terminal shown in Fig. 4, this first screening shown in Fig. 4
Unit 303 includes:
Second screening unit 305, for filtering out m quasi-recommended from this recommended, wherein, it is right that this standard is recommended
As the recommended that the preference type for this login user is corresponding, this m is the positive integer more than zero.
For example, if the recommended that smart mobile phone obtains is variety " ultimate challenge ", " having requested refrigerator " and " runs
Brother ", pop music " those flowers " and " Take me to your heart ".The preference type of this login user is comprehensive
Skill, then the recommended that the preference type of this login user is corresponding is " ultimate challenge ", " having requested refrigerator " and " brother of running
Younger brother "；This smart mobile phone can filter out 2 quasi-recommendeds from the recommended that the preference type of this login user is corresponding
" ultimate challenge " and " having requested refrigerator ".
Second acquisition unit 306, for obtaining in this m the quasi-recommended that this second screening unit 305 filters out
The recommendation weights of each quasi-recommended.
These recommendation weights can be as the criterion the clicking rate of recommended, audience ratings or recommend index etc..
Optionally, this second acquisition unit 306, specifically for obtaining this second screening unit in the social media of user
The recommendation weights of each quasi-recommended in this m filtered out a quasi-recommended.
The social media of this user can be microblogging, mhkc, circle of friends or QQ space etc..
Sequencing unit 307, each standard in this m the quasi-recommended obtained according to this second acquisition unit 306
The recommendation weights of recommended carry out descending to this m quasi-recommended.
Select unit 308, for the descending result according to the generation of this sequencing unit 307 by this m quasi-recommended
Before middle serial number, the quasi-recommended of x is chosen as this target recommended, and this x is the positive integer more than zero less than or equal to this m.
Such as, sequencing unit 307 carries out descending according to the recommendation weights of each quasi-recommended to these 2 quasi-recommendeds
The result of arrangement is: " having requested refrigerator " " ultimate challenge ", and x=1, then this target recommended is " having requested refrigerator "；If x
=2, then this target recommended is " having requested refrigerator " and " ultimate challenge ".
Visible, the terminal implementing Fig. 4 description can be that user recommends current popular program etc..
Referring to Fig. 5, Fig. 5 is the structural representation of another kind of terminal disclosed in the embodiment of the present invention, wherein, shown in Fig. 5
Terminal be that terminal as shown in Figure 4 is optimized and obtains.Compared with the terminal shown in Fig. 4, the second acquisition shown in Fig. 5 is single
Unit 306 includes:
Judging unit 309, for judging that the weights of recommending of each quasi-recommended in this m quasi-recommended are bound
Whether contact person is to preset trusted contact.
Increase unit 310, for when the judged result of this judging unit 309 is for being, increase in this m quasi-recommended
The recommendation weights of each quasi-recommended.
Reduce unit 311, for when the judged result of this judging unit 309 is no, then reduce by this m quasi-recommended
In the recommendation weights of each quasi-recommended.
For example, if the recommendation index that " ultimate challenge " is in circle of friends is three stars, and " ultimate challenge " is recommended
Be the friend oneself being better, then smart mobile phone increases recommendation index to four star of " ultimate challenge "；If recommending " ultimate challenge "
Be a stranger, then smart mobile phone reduce " ultimate challenge " recommendation index to two star.
Visible, the terminal implementing Fig. 5 description can be that user recommends current popular program etc..
Referring to Fig. 6, Fig. 6 is the structural representation of another kind of terminal disclosed in the embodiment of the present invention.Wherein, the present invention is real
The terminal that executing example provides may be used for implementing the method that the various embodiments of the present invention shown in above-mentioned Fig. 1 and Fig. 2 realize, for the ease of
Illustrating, illustrate only the part relevant to various embodiments of the present invention, concrete ins and outs do not disclose, and refer to Fig. 1 and Fig. 2 institute
The various embodiments of the present invention shown.Wherein, the terminal shown in Fig. 6 may include that
Processor 1 and the input equipment 3 being connected with processor 1 by interface 2 and by interface 2 and processor 1
The output device 6 being connected and the memorizer 5 being connected with processor 1 by bus 4.Wherein, memorizer 5 is used for storing
Batch processing code；Processor 1, for calling the program code of storage in memorizer 5, is used for performing following operation:
Identification login user, and the preference type of this login user is obtained by this input equipment 3, wherein, this login is used
The preference type at family is to add up the content that this login user is interested to obtain；
Obtain recommended by this input equipment 3, and according to the preference type of this login user, this recommended is entered
Row filter thus obtain target recommended；
By this output device 6, this target recommended is pushed to this login user.
Wherein, can prestore in this terminal or server the content interested at least one user.Wherein, user's sense
The content of interest can be the user of this terminal record video that once used this terminal to watch, user once play music,
Article that user once read and the TV programme etc. that user once watched.
Optionally, processor 1, for calling the program code of storage in memorizer 5, performs this according to this login user
This recommended is screened thus is obtained target recommended and concretely comprise the following steps by preference type:
Filtering out m quasi-recommended from this recommended, wherein, this quasi-recommended is the hobby of this login user
The recommended that type is corresponding, this m is the positive integer more than zero；
The recommendation weights of each quasi-recommended in this m quasi-recommended are obtained by this input equipment 3；
This m quasi-recommended is carried out by the recommendation weights according to each quasi-recommended in this m quasi-recommended
Descending；
According to descending result, the quasi-recommended of x before sequence number in this m quasi-recommended is chosen as this target
Recommended, this x is the positive integer more than zero less than or equal to this m.
Optionally, processor 1, for calling the program code of storage in memorizer 5, performs the quasi-recommendation of this acquisition this m right
Concretely comprising the following steps of the recommendation weights of each quasi-recommended in as:
The each quasi-recommendation obtained from the social media of user in this m quasi-recommended by this input equipment 3 is right
The recommendation weights of elephant.
Optionally, processor 1, for calling the program code of storage in memorizer 5, performs this social media from user
Concretely comprising the following steps of the recommendation weights of each quasi-recommended in this m of upper acquisition quasi-recommended:
Judge whether the contact person recommending weights binding of each quasi-recommended in this m quasi-recommended is default
Trusted contact；
If the contact recommending weights binding of each quasi-recommended in this m quasi-recommended is artificially preset credible
It is people, then increases the recommendation weights of each quasi-recommended in this m quasi-recommended；
If the contact person recommending weights binding of each quasi-recommended in this m quasi-recommended is not default not credible
Contact person, then reduce the recommendation weights of each quasi-recommended in this m quasi-recommended.
Visible, the terminal that enforcement Fig. 5 describes, can be for each when in the face of the different login users logging in this terminal
The hobby of login user, realizes personalized recommendation content exactly for different user groups.
Step in embodiment of the present invention method can carry out order according to actual needs and adjust, merges and delete.
Unit in embodiment of the present invention terminal can merge according to actual needs, divides and delete.
One of ordinary skill in the art will appreciate that all or part of step in the various methods of above-described embodiment is can
Completing instructing relevant hardware by program, this program can be stored in a computer-readable recording medium, storage
Medium include read only memory (Read-Only Memory, ROM), random access memory (Random Access Memory,
RAM), programmable read only memory (Programmable Read-only Memory, PROM), erasable programmable is read-only deposits
Reservoir (Erasable Programmable Read Only Memory, EPROM), disposable programmable read only memory (One-
Time Programmable Read-Only Memory, OTPROM), the electronics formula of erasing can make carbon copies read only memory
(Electrically-Erasable Programmable Read-Only Memory, EEPROM), read-only optical disc (Compact
Disc Read-Only Memory, CD-ROM) or other disk storages, disk memory, magnetic tape storage or can
For carrying or store any other medium computer-readable of data.
Above content recommendation method a kind of disclosed in the embodiment of the present invention and terminal are described in detail, herein should
Being set forth principle and the embodiment of the present invention by specific case, the explanation of above example is only intended to help reason
Solve method and the core concept thereof of the present invention；Simultaneously for one of ordinary skill in the art, according to the thought of the present invention,
All will change in detailed description of the invention and range of application, be to sum up somebody's turn to do, this specification content should not be construed as the present invention
Restriction.
Claims (10)
1. a content recommendation method, it is characterised in that described method includes:
Identification login user, and obtain the preference type of described login user, wherein, the preference type of described login user is right
Described login user content interested carries out adding up and obtains；
Obtain recommended, and according to the preference type of described login user described recommended screened thus obtain mesh
Mark recommended；
Described target recommended is pushed to described login user.
Method the most according to claim 1, it is characterised in that described login user content interested include following at least
A kind of: music, described login user that the video that described login user was once watched, described login user were once play once were read
The TV programme that the article read and described login user were once watched.
Method the most according to claim 2, it is characterised in that the described preference type according to described login user is to described
Recommended carries out screening thus obtains target recommended, including:
Filtering out m quasi-recommended from described recommended, wherein, described quasi-recommended is the happiness of described login user
The recommended that good type is corresponding, described m is the positive integer more than zero；
Obtain the recommendation weights of each quasi-recommended in described m quasi-recommended；
Described m quasi-recommended is carried out by the recommendation weights according to each quasi-recommended in described m quasi-recommended
Descending；
According to descending result, the quasi-recommended of x before sequence number in described m quasi-recommended is chosen as described target
Recommended, described x is the positive integer more than zero less than or equal to described m.
Method the most according to claim 3, it is characterised in that each standard in the described m of described acquisition quasi-recommended
The recommendation weights of recommended, including:
The recommendation weights of each quasi-recommended in described m quasi-recommended are obtained from the social media of user.
Method the most according to claim 4, it is characterised in that described obtain from the social media of user described m accurate
The recommendation weights of each quasi-recommended in recommended, including:
Judge that whether the contact person recommending weights binding of each quasi-recommended in described m quasi-recommended is that preset can
Letter contact person；
If the contact recommending weights to bind of each quasi-recommended in described m quasi-recommended is artificial described default credible
Contact person, then increase the recommendation weights of each quasi-recommended in described m quasi-recommended；
If the contact person recommending weights binding of each quasi-recommended in described m quasi-recommended is not that described presetting can
Letter contact person, then reduce the recommendation weights of each quasi-recommended in described m quasi-recommended.
6. a terminal, it is characterised in that described terminal includes:
Recognition unit, is used for identifying login user；
First acquiring unit, for obtaining the preference type of described login user of described recognition unit identification, wherein, described in step on
The preference type employing family is to add up the content that described login user is interested to obtain；And it is right to be used for obtaining recommendation
As；
First screening unit, for obtaining the preference type of described login user to described recommendation according to described first acquiring unit
Object carries out screening thus obtains target recommended；
Recommendation unit, for being pushed to described login user by the described target recommended of described first screening unit screening.
Terminal the most according to claim 6, it is characterised in that described login user content interested include following at least
A kind of: music, described login user that the video that described login user was once watched, described login user were once play once were read
The TV programme that the article read and described login user were once watched.
Terminal the most according to claim 7, it is characterised in that described first screening unit includes:
Second screening unit, for filtering out m quasi-recommended, wherein, described quasi-recommended from described recommended
For the recommended that the preference type of described login user is corresponding, described m is the positive integer more than zero；
Second acquisition unit, for obtaining each standard in described m the quasi-recommended that described second screening unit filters out
The recommendation weights of recommended；
Sequencing unit, each quasi-recommendation in described m the quasi-recommended obtained according to described second acquisition unit is right
The recommendation weights of elephant carry out descending to described m quasi-recommended；
Select unit, for the descending result that generates according to described sequencing unit by sequence number in described m quasi-recommended
Quasi-recommended for front x is chosen as described target recommended, and described x is the positive integer more than zero less than or equal to described m.
Terminal the most according to claim 8, it is characterised in that described second acquisition unit, specifically for from the society of user
Hand over the recommendation of each quasi-recommended obtained on media in described m the quasi-recommended that described second screening unit filters out
Weights.
Terminal the most according to claim 9, it is characterised in that described second acquisition unit includes:
Judging unit, for judging the contact recommending weights binding of each quasi-recommended in described m quasi-recommended
Whether people is to preset trusted contact；
Increasing unit, for when the judged result of described judging unit is for being, increase in the individual quasi-recommended of described m is each
The recommendation weights of quasi-recommended；
Reducing unit, for when the judged result of described judging unit is no, then that reduces in described m quasi-recommended is every
The recommendation weights of individual quasi-recommended.
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| CN201610483000.0A CN106131684A (en) | 2016-06-24 | 2016-06-24 | A kind of content recommendation method and terminal |
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