CN106454431A - Method and system for recommending television programs - Google Patents
Method and system for recommending television programs Download PDFInfo
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- CN106454431A CN106454431A CN201610900119.3A CN201610900119A CN106454431A CN 106454431 A CN106454431 A CN 106454431A CN 201610900119 A CN201610900119 A CN 201610900119A CN 106454431 A CN106454431 A CN 106454431A
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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]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
- H04N21/262—Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission, generating play-lists
- H04N21/26258—Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission, generating play-lists for generating a list of items to be played back in a given order, e.g. playlist, or scheduling item distribution according to such list
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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
- H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
- H04N21/4667—Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
-
- 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
-
- 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
- H04N21/47—End-user applications
- H04N21/482—End-user interface for program selection
- H04N21/4826—End-user interface for program selection using recommendation lists, e.g. of programs or channels sorted out according to their score
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- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- Software Systems (AREA)
- Human Computer Interaction (AREA)
- Two-Way Televisions, Distribution Of Moving Picture Or The Like (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention provides a method and system for recommending television programs. In the method, a program recommendation list can be dynamically adjusted according to the on-demand behavior of a user within a current time period, and television programs are recommended according to the adjusted program recommendation list. In the method and system provided by the invention, as the watching habits of the current user are combined, the probability that the recommended television programs are programs which the user wants to watch is larger, and the accuracy is relatively high.
Description
Technical field
The present invention relates to ntelligent television technolog field is and in particular to a kind of TV programme suggesting method and system.
Background technology
In recent years, with the continuous intensification of television set intelligently degree and interconnection networking degree, the type sum of TV programme
Amount increases considerably, and various programs, news are flooded with the life of people.In the face of so numerous and jumbled content cluster, people cannot
Oneself information really interested is obtained by simple search, and repeats frequently way of search, people can be made to be weary of right
The selection of program, loses the interest viewing and admiring program.
For solving the above problems, television program recommendation system arises at the historic moment.Television program recommendation system is according to user's history
Behavioral data, sets up user interest preference model, filters out user's program interested, rapidly and accurately recommends to be liked to user
Good programme information.
During realizing the present invention, the inventors found that program recommendation system of the prior art is at least deposited
In following problem：
When generating recommendation list, traditional proposed algorithm has filtered the TV programme of targeted customer's program request, but has
A little TV programme such as serials etc., user may not finish, and may exactly user wish to；
Current television program recommendation system does not differentiate between domestic consumer it is impossible to specific aim is according to the happiness of active user well
Recommend well corresponding program；
Current TV programme are mainly live in the majority, supplemented by program request, but the fusion not yet in effect of current TV programme
The electric program menu of each channel of the same day, leads to some programs recommended, and some program same day recommended are not broadcast
Go out；
These above-mentioned problems lead to the accuracy of the program that current program recommendation system recommended low.
Content of the invention
(1) technical problem solving
It is an object of the present invention at least part of improve the accuracy that program is recommended.
(2) technical scheme
For reaching above-mentioned purpose, the first aspect of the invention provides a kind of TV programme suggesting method, including：
User is recorded as according to user's history program request record and program base attribute and generates offline program recommendation list；
Mobile state adjustment is entered according to current influence factor to described offline program recommendation list；Described current influence factor bag
Include program request behavior in current slot for the active user；
TV programme are recommended for active user according to the real time programme recommendation list after adjustment.
Further, described current influence factor also includes multiple channels in the preset time period after current slot
Real-time electronic program guide B.
Further, the current influence factor of described basis enters Mobile state adjustment to described offline program recommendation list, including：
Real-time Candidate Set E is generated according to program set A and real-time electronic program guide B；
Real-time Candidate Set D is generated according to offline program recommendation list and real-time electronic program guide B；
Merge real-time Candidate Set E and real-time Candidate Set D, be real time programme recommendation list List after user generates adjustment；
Described program set A is the set of active user's request program in current slot.
Further, described real-time Candidate Set E is generated according to program set A and real-time electronic program guide B, including：
Preference pref to program in program set A for the user is calculated according to user interest modeling method；And using based on section
The proposed algorithm of mesh content, calculates in program set A and real-time electronic program guide B between programme attribute according to program base attribute
Similitude sim, and the product life to preference pref of program in program set A according to programme attribute similitude sim and user
Become preference w to program in real-time electronic program guide B for the user, the size according to preference w saves in real-time electronic program guide B
Mesh is ranked up, and generates real-time Candidate Set E.
Further, described according to user's history program request record and program base attribute be recorded as user generate offline program
Recommendation list, including：
Using user interest modeling method, user's history program request record and program base attribute record are processed, with life
Become user preference matrix；
According to user preference matrix, using the Collaborative Filtering Recommendation Algorithm based on user, generate the first initial recommendation list
Ulist；And/or using the Collaborative Filtering Recommendation Algorithm based on program, generate the second initial recommendation list Vlist；
Generate the 3rd initial recommendation row according to not completing the series performance that all collection/phase plays in user's history program request record
Table Plist；
Initially push away according in the middle of the first initial recommendation list Ulist generating and the second initial recommendation list Vlist generation
Recommend list UVlist；
Offline program is generated according to middle initial recommendation list UVlist and the 3rd initial recommendation list Plist and recommends row
Table.
Second aspect, the invention provides a kind of television program recommendation system, including：
Offline list recommending module, generates for being recorded as user according to user's history program request record and program base attribute
Offline program recommendation list；
Adjusting module, for entering Mobile state adjustment according to current influence factor to described offline program recommendation list；Described
Current influence factor includes program request behavior in current slot for the active user；
Real-time list recommending module, for recommending TV Festival according to the real time programme recommendation list after adjustment for active user
Mesh.
Further, described current influence factor also includes multiple channels in the preset time period after current slot
Real-time electronic program guide B.
Further, described adjusting module, for carrying out to described offline program recommendation list according to current influence factor
Dynamic adjustment, including：
Real-time Candidate Set E is generated according to program set A and real-time electronic program guide B；
Real-time Candidate Set D is generated according to offline program recommendation list and real-time electronic program guide B；
Merge real-time Candidate Set E and real-time Candidate Set D, be real time programme recommendation list List after user generates adjustment.
Further, described adjusting module, waits in real time for being generated according to program set A and real-time electronic program guide B
Selected works E, including：
The preference pref to program in program set A for the user is calculated according to user interest modeling method；And adopt base
In the proposed algorithm of programme content, program in program set A and real-time electronic program guide B is calculated according to program base attribute and belongs to
Similitude sim between property, and the preference pref to program in program set A according to programme attribute similitude sim and user
Product generate preference w to program in real-time electronic program guide B for the user, the size according to preference w is to real-time electronic program
In menu B, program is ranked up, and generates real-time Candidate Set E.
Further, described offline list recommending module, for according to user's history program request record and program base attribute
It is recorded as user and generate offline program recommendation list, including：
Using user interest modeling method, user's history program request record and program base attribute record are processed, with life
Become user preference matrix；
According to user preference matrix, using the Collaborative Filtering Recommendation Algorithm based on user, generate the first initial recommendation list
Ulist；And/or using the Collaborative Filtering Recommendation Algorithm based on program, generate the second initial recommendation list Vlist；
Generate the 3rd initial recommendation row according to not completing the series performance that all collection/phase plays in user's history program request record
Table Plist；
Initially push away according in the middle of the first initial recommendation list Ulist generating and the second initial recommendation list Vlist generation
Recommend list UVlist；
Offline program is generated according to middle initial recommendation list UVlist and the 3rd initial recommendation list Plist and recommends row
Table.
(3) beneficial effect
The TV programme suggesting method that the present invention provides, can be according to program request behavior in current slot for the user to section
Mesh recommendation list is dynamically adjusted, and recommends TV programme according to the program recommendation list after adjustment.In the present invention, due to
Combine active user viewing custom, the TV programme therefore recommended for active user's program to be watched probability relatively
Greatly, accuracy rate is higher.
Brief description
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
Have technology description in required use accompanying drawing be briefly described it should be apparent that, drawings in the following description be only this
Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, acceptable
Other accompanying drawings are obtained according to these accompanying drawings.
The flow chart of the TV programme suggesting method that Fig. 1 provides for one embodiment of the invention；
The flow chart of the TV programme suggesting method that Fig. 2 provides for yet another embodiment of the invention；
Fig. 3 is the schematic diagram of split flow in the middle part of the TV programme suggesting method in Fig. 2；
Fig. 4 is the schematic diagram of split flow in the middle part of the TV programme suggesting method in Fig. 2；
The structural representation of the television program recommendation system that Fig. 5 one embodiment of the invention provides.
Specific embodiment
Purpose, technical scheme and advantage for making the embodiment of the present invention are clearer, below in conjunction with the embodiment of the present invention
In accompanying drawing, the technical scheme in the embodiment of the present invention is clearly and completely described it is clear that described embodiment is
The a part of embodiment of the present invention, rather than whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art
The every other embodiment being obtained under the premise of not making creative work, broadly falls into the scope of protection of the invention.
In a first aspect, the basic embodiment of the present invention provides a kind of TV programme suggesting method, referring to Fig. 1, the method
Including：
Step S11, is recorded as user according to user's history program request record and program base attribute and generates offline program recommendation
List；
Step S12, enters Mobile state adjustment according to current influence factor to described offline program recommendation list；Described current shadow
The factor of sound includes program request behavior in current slot for the active user；
Step S13, recommends TV programme according to the real time programme recommendation list after adjustment for active user.
The recommendation method of TV programme provided in an embodiment of the present invention, can be according to program request in current slot for the user
Behavior is dynamically adjusted to program recommendation list, and recommends TV programme according to the program recommendation list after adjustment.This
In bright, the viewing due to combining active user is accustomed to, and the TV programme therefore recommended are active user's section to be watched
Purpose probability is larger, and accuracy rate is higher.
In the specific implementation, can also implement according to one or several modes following, to improve program recommendation further
Accuracy：
The reality of multiple channels in the preset time period that mode one, described current influence factor also include after current slot
When electric program menu.
So in the specific implementation, the program of recommending those currently never to play, such as variety show can be avoided
Deng the accuracy that raising program is recommended further.
Further, now step S12 can specifically include：
Step S121, generates real-time Candidate Set E according to program set A and real-time electronic program guide B；
Step S122, generates real-time Candidate Set D according to offline program recommendation list and real-time electronic program guide B；
Step S123, merges real-time Candidate Set E and real-time Candidate Set D, is that user generates the real time programme recommendation after adjustment
List List.
Specifically, when implementing, above-mentioned step S121 can specifically include：Calculated according to user interest modeling method
Preference pref to program in program set A for the user；And using the proposed algorithm based on programme content, according to program base attribute
Calculate similitude sim between programme attribute in program set A and real-time electronic program guide B, and according to programme attribute similitude
Sim and user generate user to program in real-time electronic program guide B to the product of preference pref of program in program set A
Preference w, the size according to preference w is ranked up to program in real-time electronic program guide B, generates real-time Candidate Set E.
Here program set A is the set of active user's request program in current slot.
Mode three：Described offline program is generated according to user's history program request record and program base attribute record recommend row
Table, including：
Using user interest modeling method, user's history program request record and program base attribute record are processed, with life
Become user preference matrix；
According to user preference matrix, using the Collaborative Filtering Recommendation Algorithm based on user, generate the first initial recommendation list
Ulist；And/or using the Collaborative Filtering Recommendation Algorithm based on program, generate the second initial recommendation list Vlist；
Generate the 3rd initial recommendation row according to not completing the series performance that all collection/phase plays in user's history program request record
Table Plist；
Initially push away according in the middle of the first initial recommendation list Ulist generating and the second initial recommendation list Vlist generation
Recommend list UVlist；
Offline program is generated according to middle initial recommendation list UVlist and the 3rd initial recommendation list Plist and recommends row
Table.
In this way, more can effectively filter out user's program interested, remove user less interested
Program, improve recommend accuracy.
Further, in mode three, described according to generate the first initial recommendation list Ulist and the second initial recommendation
List Vlist generates middle initial recommendation list UVlist, can implement according to following preferred embodiment, to improve section further
The accuracy that mesh is recommended.Specifically, can include：
According to the preference to the program in the first initial recommendation list Ulist and the second initial recommendation list Vlist for the user
Degree generates middle initial recommendation list UVlist；Wherein, in the first initial recommendation list Ulist and the second initial recommendation list
When Vlist all comprises same program, the preference taking this program is that this program is corresponding in the first initial recommendation list Ulist
Preference and in the second initial recommendation list Vlist corresponding preference mean value.
In order to make it easy to understand, one kind of the following method to the recommendation TV programme that the present invention provides is preferred embodiment
It is described in more detail：
Referring to Fig. 2, the overall flow of the method may include steps of：
1, using Set Top Box harvester, collect user's history program request record；Using EPG data harvester, collect each
Electric program menu and program base attribute record that individual channel is play；Above-mentioned real-time electronic program guide so can be obtained
B；
2, according to program base attribute record and user's history program request record, generate in user's history program request record and do not complete
The series performance that all collection/phase plays generates the 3rd initial recommendation list Plist；Utilize user interest modeling method simultaneously, raw
Become user preference matrix；
3, according to user preference matrix, it is utilized respectively the Collaborative Filtering Recommendation Algorithm based on user with based on program, be mesh
Mark user generates the first initial recommendation list Ulist；Using the Collaborative Filtering Recommendation Algorithm based on program, it is targeted customer's generation
Second initial recommendation list Vlist；
4, merge the 3rd initial recommendation list Plist, the first initial recommendation list Ulist and the second initial recommendation list
Vlist, generates offline program recommendation list UVPlist；
5, using Set Top Box harvester, collect user's real-time VOD behavior, generate active user in current slot
The program set A of program request
6, based on program set A, offline program recommendation list UVPlist and real-time electronic program guide B, using based on interior
The proposed algorithm held, generates real time programme recommendation list List.
Wherein, the Collaborative Filtering Recommendation Algorithm based on user and the Collaborative Filtering Recommendation Algorithm recommended users based on program
The similitude program of program request, will not recommend the TV programme of user program request again, and actually TV programme have for it
Repeated and successional feature, user can watch the different collection numbers of same TV programme to a great extent, is therefore generating
Need when consequently recommended list to consider that the series performance not completing all collection/phases broadcastings in user's history program request record is the
Three initial recommendation list Plist, generate offline program recommendation list UVPlist.
Wherein, in above-mentioned step 4, when each algorithm generates respective recommendation list and is merged, referring to
Fig. 3, concrete operation step can be as follows：
(1) if program is variety column news etc., total collection number FjValue do not know, now inclined to this program according to user
Good size is ranked up generating the 3rd initial recommendation list Plist；If program is TV series, total collection number FjFor fixation
Value, then counting user uuProgram v is crossed in program requestjThe collection number of time recentlyWith total collection number FjRelation, ifThen according to
Family uuTo program vjPreference size be ranked up, generate the 3rd initial recommendation list Plist；IfThen not this program
Include the 3rd initial recommendation list Plist.
(2) size to program preferences according to user, merges at the beginning of the first initial recommendation list Ulist recommendation list and second
Program in beginning recommendation list Vlist generates middle initial recommendation list UVlist, if same program initially pushes away in centre respectively
Recommend in list UVlist and the second initial recommendation list Vlist, then take the mean value of its preference, arranged further according to preference size
Sequence；
(3) merge the program in middle initial recommendation list UVlist and the 3rd initial recommendation list Plist, according to preference
Size is ranked up, and generates offline program recommendation list UVPlist.
In above-mentioned step 6, referring to Fig. 4, the process generating real time programme recommendation list List can include：
(1) according to current time T, take forward the user institute program request in the interval (i.e. [T-t1, T] time interval) of t1 minute
Program set A；
(2) utilize EPG data harvester, according to current time T, the interval taking t2 minute backward is (i.e. when [T, T+t2]
Between interval) in each channel the program set B that comprised of electric program menu；Here program set B is current impact
The real-time electronic program guide B of multiple channels in the preset time period that factor also includes after current slot；
(3) section destination aggregation (mda)s all in offline program recommendation list UVPlist are designated as C；
(4) generate real-time Candidate Set D=C ∩ B；
(5) using the proposed algorithm based on programme content, program set A and program set are calculated according to program base attribute
The attribute similarity sim of program in B；
(6) according to user interest preference model, calculate preference pref to program in program set A for the user；
(7) calculate preference w=pref*sim to program in program set B for the user；
(8) size according to preference w, generates real-time Candidate Set E；
(9) the preference size to program in real-time Candidate Set D and real-time Candidate Set E according to user, to real-time Candidate Set D and
Program in Candidate Set E is ranked up in real time, is that active user generates real time programme recommendation list List.
On the other hand, the embodiment of the present invention additionally provides a kind of system recommending TV programme, and referring to 5, this system includes：
Offline list recommending module 51, for being recorded as user's life according to user's history program request record and program base attribute
Become offline program recommendation list；
Adjusting module 52, for entering Mobile state adjustment according to current influence factor to described program recommendation list；Described work as
Front influence factor includes program request behavior in current slot for the active user；
Real-time list recommending module 53, for recommending TV according to the real time programme recommendation list after adjustment for active user
Program.
Further, described current influence factor also includes multiple channels in the preset time period after current slot
Real-time electronic program guide B.
Further, this system also includes：Described adjusting module 52, for pushing away to described program according to current influence factor
Recommend list and enter Mobile state adjustment, including：
Real-time Candidate Set E is generated according to program set A and real-time electronic program guide B；
Real-time Candidate Set D is generated according to offline program recommendation list and real-time electronic program guide B；
Merge real-time Candidate Set E and real-time Candidate Set D, be real time programme recommendation list List after user generates adjustment；
Described program set A is the set of active user's request program in current slot.
Further, described adjusting module 52, real for being generated according to program set A and real-time electronic program guide B
When Candidate Set E, can specifically include：
Preference pref to program in program set A for the user is calculated according to user interest modeling method；And using based on section
The proposed algorithm of mesh content, calculates in program set A and real-time electronic program guide B between programme attribute according to program base attribute
Similitude sim, and the product life to preference pref of program in program set A according to programme attribute similitude sim and user
Become preference w to program in real-time electronic program guide B for the user, the size according to preference w saves in real-time electronic program guide B
Mesh is ranked up, and generates real-time Candidate Set E.
Accordingly, adjusting module 52 is used for merging real-time Candidate Set E and real-time Candidate Set D, is after user generates adjustment
Real time programme recommendation list List.
Further, described offline list recommending module 51 is used for according to user's history program request record and program base attribute
It is recorded as user and generate offline program recommendation list, including：
Using user interest modeling method, user's history program request record and program base attribute record are processed, with life
Become user preference matrix；
According to user preference matrix, using the Collaborative Filtering Recommendation Algorithm based on user, generate the first initial recommendation list
Ulist；And/or using the Collaborative Filtering Recommendation Algorithm based on program, generate the second initial recommendation list Vlist；
Generate the 3rd initial recommendation row according to not completing the series performance that all collection/phase plays in user's history program request record
Table Plist；
Initially push away according in the middle of the first initial recommendation list Ulist generating and the second initial recommendation list Vlist generation
Recommend list UVlist；
Offline program recommendation list is generated according to middle initial recommendation list UVlist and the 3rd initial recommendation list Plist
UVPlist.
Understandable be, due to above-mentioned second aspect introduction recommendation TV programme system be can execute the present invention
The device of the method for recommendation TV programme in embodiment, so based on the recommendation TV programme described in the embodiment of the present invention
Method, those skilled in the art will appreciate that the specific embodiment of the system of recommendation TV programme of the present embodiment with
And its various change form, so how here realizes the recommendation in the embodiment of the present invention for the system of this recommendation TV programme
The method of TV programme is no longer discussed in detail.As long as those skilled in the art implement to recommend TV Festival in the embodiment of the present invention
The device that purpose method is adopted, broadly falls into the scope that the application to be protected.
The third aspect, the invention provides a kind of electronic equipment, including processor and storage medium；Described processor by with
In the instruction executing storage in described storage medium；
Be stored with described storage medium instruction, this instruction be used for executing described in first aspect and any one method
Instruction.
Through the above description of the embodiments, those skilled in the art can be understood that each embodiment can
Mode by software plus necessary general hardware platform to be realized naturally it is also possible to pass through hardware.Based on such understanding, on
That states that technical scheme substantially contributes to prior art in other words partly can be embodied in the form of software product, should
Computer software product can store in a computer-readable storage medium, such as ROM/RAM, magnetic disc, CD etc., including some fingers
Order is with so that a computer equipment (can be personal computer, server, or network equipment etc.) executes each enforcement
Example or some partly described methods of embodiment.
In specification mentioned herein, illustrate a large amount of details.It is to be appreciated, however, that the enforcement of the present invention
Example can be put into practice in the case of not having these details.In some instances, known method, structure are not been shown in detail
And technology, so as not to obscure the understanding of this description.
Similarly it will be appreciated that in order to simplify the disclosure and help understand one or more of each inventive aspect,
Above in the description to the exemplary embodiment of the present invention, each feature of the present invention is grouped together into single enforcement sometimes
In example, figure or descriptions thereof.However, the method for the disclosure should be construed to reflect following intention：I.e. required guarantor
The application claims of shield more features than the feature being expressly recited in each claim.More precisely, it is such as following
Claims reflected as, inventive aspect is all features less than single embodiment disclosed above.Therefore,
The claims following specific embodiment are thus expressly incorporated in this specific embodiment, wherein each claim itself
All as the separate embodiments of the present invention.
Those skilled in the art are appreciated that and the module in the equipment in embodiment can be carried out adaptively
Change and they are arranged in one or more equipment different from this embodiment.Can be the module in embodiment or list
Unit or assembly be combined into a module or unit or assembly, and can be divided in addition multiple submodule or subelement or
Sub-component.In addition to such feature and/or at least some of process or unit exclude each other, can adopt any
Combination is to all features disclosed in this specification (including adjoint claim, summary and accompanying drawing) and so disclosed
Where method or all processes of equipment or unit are combined.Unless expressly stated otherwise, this specification (includes adjoint power
Profit requires, summary and accompanying drawing) disclosed in each feature can carry out generation by the alternative features providing identical, equivalent or similar purpose
Replace.
Although additionally, it will be appreciated by those of skill in the art that some embodiments in this include institute in other embodiments
Including some features rather than further feature, but the combination of the feature of different embodiment means to be in the scope of the present invention
Within and form different embodiments.For example, in the following claims, embodiment required for protection any it
One can in any combination mode using.
It should be noted that above-described embodiment the present invention will be described rather than limits the invention, and ability
Field technique personnel can design alternative embodiment without departing from the scope of the appended claims.In the claims,
Any reference symbol between bracket should not be configured to limitations on claims.Word "comprising" does not exclude the presence of not
Element listed in the claims or step.Word "a" or "an" before element does not exclude the presence of multiple such
Element.The present invention can come real by means of the hardware including some different elements and by means of properly programmed computer
Existing.If in the unit claim listing equipment for drying, several in these devices can be by same hardware branch
To embody.The use of word first, second, and third does not indicate that any order.These words can be explained and run after fame
Claim.
Finally it should be noted that：Above example only in order to technical scheme to be described, is not intended to limit；Although
With reference to the foregoing embodiments the present invention is described in detail, it will be understood by those within the art that：It still may be used
To modify to the technical scheme described in foregoing embodiments, or equivalent is carried out to wherein some technical characteristics；
And these modification or replace, do not make appropriate technical solution essence depart from various embodiments of the present invention technical scheme spirit and
Scope.
Claims (10)
1. a kind of TV programme suggesting method is it is characterised in that include：
User is recorded as according to user's history program request record and program base attribute and generates offline program recommendation list；
Mobile state adjustment is entered according to current influence factor to described offline program recommendation list；Described current influence factor includes working as
Program request behavior in current slot for the front user；
TV programme are recommended for active user according to the real time programme recommendation list after adjustment.
2. method according to claim 1 is it is characterised in that after described current influence factor also includes current slot
Preset time period in multiple channels real-time electronic program guide B.
3. method according to claim 2 is it is characterised in that the current influence factor of described basis pushes away to described offline program
Recommend list and enter Mobile state adjustment, including：
Real-time Candidate Set E is generated according to program set A and real-time electronic program guide B；
Real-time Candidate Set D is generated according to offline program recommendation list and real-time electronic program guide B；
Merge real-time Candidate Set E and real-time Candidate Set D, be real time programme recommendation list List after user generates adjustment；Described
Program set A is the set of active user's request program in current slot.
4. method according to claim 3 it is characterised in that described according to program set A with real-time electronic program guide B
Generate real-time Candidate Set E, including：
Preference pref to program in program set A for the user is calculated according to user interest modeling method；And using based in program
The proposed algorithm held, calculates the phase between programme attribute in program set A and real-time electronic program guide B according to program base attribute
Like property sim, and use is generated to the product of preference pref of program in program set A according to programme attribute similitude sim and user
Preference w to program in real-time electronic program guide B for the family, the size according to preference w is entered to program in real-time electronic program guide B
Row sequence, generates real-time Candidate Set E.
5. method according to claim 1 is it is characterised in that described belong to substantially according to user's history program request record and program
Property be recorded as user generate offline program recommendation list, including：
Using user interest modeling method, user's history program request record and program base attribute record are processed, to generate use
Family preference matrix；
According to user preference matrix, using the Collaborative Filtering Recommendation Algorithm based on user, generate the first initial recommendation list
Ulist；And/or using the Collaborative Filtering Recommendation Algorithm based on program, generate the second initial recommendation list Vlist；
Generate the 3rd initial recommendation list according to not completing the series performance that all collection/phase plays in user's history program request record
Plist；
Middle initial recommendation row are generated according to the first initial recommendation list Ulist generating and the second initial recommendation list Vlist
Table UVlist；
Offline program recommendation list is generated according to middle initial recommendation list UVlist and the 3rd initial recommendation list Plist.
6. a kind of television program recommendation system is it is characterised in that include：
Offline list recommending module, generates offline for being recorded as user according to user's history program request record and program base attribute
Program recommendation list；
Adjusting module, for entering Mobile state adjustment according to current influence factor to described offline program recommendation list；Described current
Influence factor includes program request behavior in current slot for the active user；
Real-time list recommending module, for recommending TV programme according to the real time programme recommendation list after adjustment for active user.
7. system according to claim 6 is it is characterised in that after described current influence factor also includes current slot
Preset time period in multiple channels real-time electronic program guide B.
8. system according to claim 7 is it is characterised in that described adjusting module, for according to current influence factor pair
Described offline program recommendation list enters Mobile state adjustment, including：
Real-time Candidate Set E is generated according to program set A and real-time electronic program guide B；
Real-time Candidate Set D is generated according to offline program recommendation list and real-time electronic program guide B；
Merge real-time Candidate Set E and real-time Candidate Set D, be real time programme recommendation list List after user generates adjustment；Described
Program set A is the set of active user's request program in current slot.
9. system according to claim 8 is it is characterised in that described adjusting module, for according to program set A and in real time
Electric program menu B generates real-time Candidate Set E, including：
Preference pref to program in program set A for the user is calculated according to user interest modeling method；And using based in program
The proposed algorithm held, calculates the phase between programme attribute in program set A and real-time electronic program guide B according to program base attribute
Like property sim, and use is generated to the product of preference pref of program in program set A according to programme attribute similitude sim and user
Preference w to program in real-time electronic program guide B for the family, the size according to preference w is entered to program in real-time electronic program guide B
Row sequence, generates real-time Candidate Set E.
10. system according to claim 9 is it is characterised in that described offline list recommending module, for being gone through according to user
History program request record and program base attribute are recorded as user and generate offline program recommendation list, including：
Using user interest modeling method, user's history program request record and program base attribute record are processed, to generate use
Family preference matrix；
According to user preference matrix, using the Collaborative Filtering Recommendation Algorithm based on user, generate the first initial recommendation list
Ulist；And/or using the Collaborative Filtering Recommendation Algorithm based on program, generate the second initial recommendation list Vlist；
Generate the 3rd initial recommendation list according to not completing the series performance that all collection/phase plays in user's history program request record
Plist；
Middle initial recommendation row are generated according to the first initial recommendation list Ulist generating and the second initial recommendation list Vlist
Table UVlist；
Offline program recommendation list is generated according to middle initial recommendation list UVlist and the 3rd initial recommendation list Plist.
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