CN105323602A - Program ordering method and device - Google Patents
Program ordering method and device Download PDFInfo
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- CN105323602A CN105323602A CN201410354110.8A CN201410354110A CN105323602A CN 105323602 A CN105323602 A CN 105323602A CN 201410354110 A CN201410354110 A CN 201410354110A CN 105323602 A CN105323602 A CN 105323602A
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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
- H04N21/258—Client or end-user data management, e.g. managing client capabilities, user preferences or demographics, processing of multiple end-users preferences to derive collaborative data
- H04N21/25866—Management of end-user data
- H04N21/25891—Management of end-user data being end-user 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]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
- H04N21/24—Monitoring of processes or resources, e.g. monitoring of server load, available bandwidth, upstream requests
- H04N21/2407—Monitoring of transmitted content, e.g. distribution time, number of downloads
Abstract
The invention provides a program ordering method and device. The program ordering method comprises that program data of all programs online at present is obtained, and the program data comprises the number of days online and the request frequency of the programs; a score of each program is calculated according to the number of days online and the request frequency thereof; and according to the score of each program, all the programs online at present are ordered to obtain a program order list. According to the technical scheme provided by the invention, the programs are ordered according the request frequency and online time of the programs, the features of the programs and user habits are both considered into consideration, and the obtained program order list is more practical.
Description
Technical field
The present invention relates to the processing method of TV programme, particularly relate to a kind of program ordering method and device.
Background technology
In recent years, abundant along with TV programme resource, all kinds of TV programme emerges in an endless stream, and along with the variation of mass media and the intensification day by day of competition, the terminal that various television content exports also has attracted more client racking one's brains.In epoch of this information-based blast, user worries, be no longer TV programme very little, but TV programme is too many.How among large-scale program information, fast and effeciently find out most important content and be pushed to user, become TV programme to push one large key problem.
In the various sort algorithms to IPTV program in the prior art, usually be all using single factors as ranking criteria, synthetically do not consider that multiple factor sorts to program, therefore, the rank of the IPTV program adopting prior art to obtain can not meet the query demand of user quickly and easily.
Summary of the invention
In order to solve the problems of the technologies described above, the invention provides a kind of program ordering method and device, can sort to program according to the video-on-demand times of program and on-line time, thus consider the hobby of the feature of program own and user, the program ranking list obtained is more practical.
To achieve these goals, the invention provides a kind of program ordering method, comprising: the program data obtaining current all programs of reaching the standard grade, described program data comprises the video-on-demand times of reach the standard grade number of days and the program of program; To each program in described current all programs of reaching the standard grade, according to reach the standard grade number of days and the video-on-demand times of described program, calculate the score of described program; According to the score of each program, described current all programs of reaching the standard grade are sorted, obtains program ranking list.
Above-mentioned program ordering method, wherein, described reach the standard grade number of days and video-on-demand times according to described program, the score calculating described program comprises: the score score by program described in following formulae discovery:
Wherein, P is the video-on-demand times of described program, and T is the number of days of reaching the standard grade of described program, Gp is video-on-demand times shared weight in the score of program, Gt is the weight of number of days shared by the score of program of reaching the standard grade, and Gp+Gt=1, Dr are the default attenuation rate corresponding with described number of days of reaching the standard grade.
Above-mentioned program ordering method, wherein, the program data of the current all programs of reaching the standard grade of described acquisition comprises: to each program in described current all programs of reaching the standard grade, obtain the number of days of reaching the standard grade of described program; Determine the number of times of each user of program described in program request program described in program request within every day of reaching the standard grade in number of days of described program; According to the number of times of each user described program described in program request within every day of reaching the standard grade in number of days of described program, obtain the video-on-demand times of described program.
Above-mentioned program ordering method, wherein, described current all programs of reaching the standard grade comprise: collection of TV plays program and comprise the non-collection of TV plays program of VOD and/or TVOD; Describedly determine that each user of program described in program request number of times of program described in program request within every day of reaching the standard grade in number of days of described program is specially: when described program is non-collection of TV plays program, determine that described each user number of times of program described in program request within every day of reaching the standard grade in number of days of described program is the actual video-on-demand times of described user; When described program is collection of TV plays program, determine that described each user number of times of program described in program request within every day of the number of days of reaching the standard grade of described program is only 1 time.
Above-mentioned program ordering method, wherein, described method realizes based on the large data platform of Hadoop; The program data of the current all programs of reaching the standard grade of described acquisition is specially: on the large data platform of described Hadoop, is obtained the program data of current all programs of reaching the standard grade by multiple parallel Map handling process and multiple parallel Reduce handling process; The score of the described program of described calculating is specially: by described multiple parallel Map and Reduce handling process, respectively according to reach the standard grade number of days and the video-on-demand times of each program, calculates the score of program.
Present invention also offers a kind of program collator, comprising: acquisition module, for obtaining the program data of current all programs of reaching the standard grade, described program data comprises the video-on-demand times of reach the standard grade number of days and the program of program; Computing module, for each program in described current all programs of reaching the standard grade, according to reach the standard grade number of days and the video-on-demand times of described program, calculates the score of described program; Order module, for the score according to each program, sorts to described current all programs of reaching the standard grade, obtains program ranking list.
Above-mentioned program collator, wherein, described computing module is by the score score of program described in following formulae discovery:
Wherein, P is the video-on-demand times of described program, and T is the number of days of reaching the standard grade of described program, Gp is video-on-demand times shared weight in the score of program, Gt is the weight of number of days shared by the score of program of reaching the standard grade, and Gp+Gt=1, Dr are the default attenuation rate corresponding with described number of days of reaching the standard grade.
Above-mentioned program collator, wherein, described acquisition module comprises: the first acquiring unit, for each program in described current all programs of reaching the standard grade, obtains the number of days of reaching the standard grade of described program; First determining unit, for determining the number of times of each user of program described in program request program described in program request within every day of reaching the standard grade in number of days of described program; Second acquisition unit, for the number of times according to each user described program described in program request within every day of reaching the standard grade in number of days of described program, obtains the video-on-demand times of described program.
Above-mentioned program collator, wherein, described current all programs of reaching the standard grade comprise: collection of TV plays program and comprise the non-collection of TV plays program of VOD and/or TVOD; Described first determining unit comprises: first determines subelement, for when described program is non-collection of TV plays program, determine that described each user number of times of program described in program request within every day of reaching the standard grade in number of days of described program is the actual video-on-demand times of described user; Second determines subelement, for when described program is collection of TV plays program, determines that described each user number of times of program described in program request within every day of the number of days of reaching the standard grade of described program is only 1 time.
Above-mentioned program collator, wherein, described device can be used on the large data platform of Hadoop; Described acquisition module is further used on the large data platform of described Hadoop, is obtained the program data of current all programs of reaching the standard grade by multiple parallel Map handling process and multiple parallel Reduce handling process; Described computing module is further used for by described multiple parallel Map and Reduce handling process, respectively according to reach the standard grade number of days and the video-on-demand times of each program, calculates the score of program.
The beneficial effect of technique scheme of the present invention is as follows:
The invention provides a kind of program ordering method and device, can sort according to the video-on-demand times of program and on-line time to program, thus considered the hobby of the feature of program own and user, the program ranking list obtained is more practical.
Accompanying drawing explanation
The schematic flow sheet of the program ordering method that Fig. 1 provides for the embodiment of the present invention.
The relation schematic diagram of the video-on-demand times of program score and program in the program ordering method that Fig. 2 provides for the embodiment of the present invention.
The relation schematic diagram of the number of days of reaching the standard grade of program score and program in the program ordering method that Fig. 3 provides for the embodiment of the present invention.
In the program ordering method that Fig. 4 provides for the embodiment of the present invention when attenuation rate is different the relation schematic diagram of the number of days of reaching the standard grade of program score and program.
Fig. 5 is the flow chart of steps that program ordering method that the embodiment of the present invention provides realizes in the large data platform of Hadoop.
Embodiment
For making the technical problem to be solved in the present invention, technical scheme and advantage clearly, be described in detail below in conjunction with the accompanying drawings and the specific embodiments.
The embodiment of the present invention is in order to solve in prior art, during sequence to IPTV program, using single factors as the problem of ranking criteria, provide a kind of program ordering method and device, can sort to program according to the video-on-demand times of program and on-line time, thus having considered the hobby of the feature of program own and user, the program ranking list obtained is more practical.
The schematic flow sheet of the program ordering method that Fig. 1 provides for the embodiment of the present invention, as shown in the figure, described method comprises:
Step S10, obtains the program data of current all programs of reaching the standard grade, and described program data comprises the video-on-demand times of reach the standard grade number of days and the program of program;
Step S12, to each program in described current all programs of reaching the standard grade, according to reach the standard grade number of days and the video-on-demand times of described program, calculates the score of described program;
Step S14, according to the score of each program, sorts to described current all programs of reaching the standard grade, obtains program ranking list.
Program ordering method provided by the invention, the score of program is calculated according to the video-on-demand times of reach the standard grade number of days and the program of program, and then according to the score of program, program is sorted, namely when sorting to program, both consider the feature of program own, i.e. the on-line time of program, consider again the hobby of user, the i.e. video-on-demand times of program, thus make program ranking list not only with the on-line time of program about but also relevant with the number of clicks of user.
Above-mentioned program ordering method, wherein, described reach the standard grade number of days and video-on-demand times according to described program, the score calculating described program comprises:
Score score by program described in following formulae discovery:
Wherein, P is the video-on-demand times of described program, and T is the number of days of reaching the standard grade of described program, Gp is video-on-demand times shared weight in the score of program, Gt is the weight of number of days shared by the score of program of reaching the standard grade, and Gp+Gt=1, Dr are the default attenuation rate corresponding with described number of days of reaching the standard grade.
As can be seen from above-mentioned formula, first factor affecting the score of program is the video-on-demand times P of program.
When other conditions are constant, the video-on-demand times P of program is more, and the score of program is higher.As shown in Figure 2, when other conditions are constant, the score of program linearly rises with the video-on-demand times of program.And the video-on-demand times of program is more, illustrate that this program is subject to the fancy grade of user larger, thus, can draw from above-mentioned formula, be subject to the program of user preferences, its score is higher.
The second factor affecting the score of program is the number of days T that reaches the standard grade of distance program.
When other conditions are constant, the program of newly reaching the standard grade, namely the number of days of reaching the standard grade of program is shorter, and the score of program is higher.In other words, the score of a program, constantly can decline along with the prolongation of number of days of reaching the standard grade, as shown in Figure 3.
The 3rd factor affecting the score of program is the default attenuation rate Dr corresponding with number of days of reaching the standard grade.
The speed that the score that its numerical values recited determines program declines with the prolongation of number of days of reaching the standard grade.
If Dr is set to 0.01, then only have the program of number of days within 100 days of reaching the standard grade, the score of program just can be subject to reaching the standard grade the impact of number of days, when other conditions are constant, number of days of reaching the standard grade is longer, the score of program is lower, and for reaching the standard grade the program of number of days more than 100 days, then only can be subject to the impact of the video-on-demand times of program.The program that number of days is longer if wish to reach the standard grade can be subject to reaching the standard grade the impact of number of days, then can reduce the value of Dr, otherwise, if only wish to reach the standard grade, the shorter program of number of days can be subject to reaching the standard grade the impact of number of days, then can increase the value of Dr, the score of the program of number of days in 10 days of reaching the standard grade as only wished is subject to reaching the standard grade the impact of number of days, then Dr value can be set to 0.1.
As shown in Figure 4, respectively illustrate as Dr=0.01 and Dr=0.02 in figure, the score of program is with the variation tendency of number of days of reaching the standard grade, and can see that Dr is larger when other values are constant, the speed that the score of program declines with the prolongation of number of days of reaching the standard grade is faster.But to consider as (1-Dr*T) <=0 simultaneously, the score of number of days on program of reaching the standard grade does not affect, like this for the program that number of days of reaching the standard grade is long, when it meets (1-Dr*T) <=0, the score of program only can be subject to the impact of video-on-demand times.
The 4th factor affecting the score of program is video-on-demand times shared weight Gp and the number of days shared weight Gt in the score of program that reaches the standard grade in the score of program.Both are added and equal 1, and when a change is large, another one must diminish.
Illustrate below, each factor above-mentioned is on the impact of the score of program:
Existing program A, program B and the program C reached the standard grade, wherein, reaching the standard grade between number of days of program A is 1 day, video-on-demand times is 200 times, and reaching the standard grade between number of days of program B is 1 day, and video-on-demand times is 250 times, reaching the standard grade between number of days of program C is 25 days, video-on-demand times is 250 times, for different video-on-demand times shared weight Gp and the number of days shared weight Gt in the score of program that reaches the standard grade in the score of program, calculates the score of program A, program B and program C respectively.
(1) suppose that video-on-demand times shared weight Gp in the score of program is 0.8, the number of days shared weight Gt in the score of program that reaches the standard grade is 0.2, and the default attenuation rate Dr corresponding with number of days of reaching the standard grade is 0.01.
Then can obtain must being divided into of program A according to above-mentioned formula: 199.6; Must being divided into of program B can be obtained: 249.5; Must being divided into of program C can be obtained: 237.5.
(2) suppose that video-on-demand times shared weight Gp in the score of program is 0.1, the number of days shared weight Gt in the score of program that reaches the standard grade is 0.9, and the default attenuation rate Dr corresponding with number of days of reaching the standard grade is 0.01, then:
Then can obtain must being divided into of program A according to above-mentioned formula: 198.2, must being divided into of program B can be obtained: 247.75, must being divided into of program C can be obtained: 193.75.
It can thus be appreciated that: for identical video-on-demand times shared weight Gp and the number of days shared weight Gt in the score of program that reaches the standard grade in the score of program, video-on-demand times is more, and the score of program is higher, and number of days of reaching the standard grade is longer, and the score of program is lower.
When weight Gp shared in the score of video-on-demand times at program is higher, then video-on-demand times is comparatively large on the impact of the score of program, and the impact of number of days on the score of program is less up and down; And when weight Gt shared in the score of number of days at program of reaching the standard grade is larger, video-on-demand times and number of days of reaching the standard grade affect all comparatively large on the score of program, but comparatively speaking, the impact of number of days on the score of program of now reaching the standard grade is larger.
Therefore, if the score of program more considers the newness degree of program, namely whether program is Latest Online, then can to reach the standard grade the value of number of days shared weight Gt in the score of program, the degree of program by user preferences is considered if instead more, the i.e. video-on-demand times of program, then can tune up the value of video-on-demand times shared weight Gp in the score of program.If both considered, a suitable value of can choosing one or the other of these two.
In a specific embodiment of the present invention, step S10 can comprise:
To each program in described current all programs of reaching the standard grade, obtain the number of days of reaching the standard grade of described program;
Determine the number of times of each user of program described in program request program described in program request within every day of reaching the standard grade in number of days of described program;
According to the number of times of each user described program described in program request within every day of reaching the standard grade in number of days of described program, obtain the video-on-demand times of described program.
As a rule, the quantity of current program of reaching the standard grade is very large, and the on-line time of each program is had nothing in common with each other, the on-line time of program is only relevant to program as the feature of program own, and the program after reaching the standard grade can by each user's program request, the video-on-demand times of program is relevant to user behavior, therefore, when obtaining current joint destination data of reaching the standard grade, first the number of days of reaching the standard grade of current program of reaching the standard grade can be got, then for each program, determine the video-on-demand times of the user of this program of program request within the every day of reaching the standard grade number of days, and then get the video-on-demand times of program, such as, program A number of days of reaching the standard grade is 2 days, user 1, user 2 and user 3 program request this program A, wherein, reach the standard grade in first day at program A, this program of user 1 program request A once, reach the standard grade in second day at program A, user 2 at this program of program request A once, this program of user 3 program request twice, then the video-on-demand times of program A is 4 times.
Current program of reaching the standard grade can be collection of TV plays program and the non-collection of TV plays program comprising VOD and/or TVOD, and for collection of TV plays, a user may watch many collection of this collection of TV plays within one day, if user often watches a collection, determine its video-on-demand times once, then the number of times of a user this collection of TV plays of program request within a day is several times, then can cause with collection of TV plays be entirety to calculate its score time, the video-on-demand times of collection of TV plays can apparently higher than the video-on-demand times of other non-collection of TV plays, thus cause the score of collection of TV plays higher.
Therefore, in order to solve the problems of the technologies described above, above-mentioned program ordering method, wherein, described current all programs of reaching the standard grade comprise: collection of TV plays program and comprise the non-collection of TV plays program of VOD and/or TVOD; Describedly determine that each user of program described in program request number of times of program described in program request within every day of reaching the standard grade in number of days of described program is specially:
When described program is non-collection of TV plays program, determine that described each user number of times of program described in program request within every day of reaching the standard grade in number of days of described program is the actual video-on-demand times of described user; When described program is collection of TV plays program, determine that described each user number of times of program described in program request within every day of the number of days of reaching the standard grade of described program is only 1 time.
The object of aforesaid operations is, for a non-collection of TV plays program, the user of this program of program request is the number of times of this user actual this program of program request within this day at intraday video-on-demand times, such as, for first phase variety show, reaching the standard grade period at this variety show, one user this program of program request three times within some day, then determine that this user is three times for the video-on-demand times of this program, for collection of TV plays, by determining that the user of video on demand collection of drama is 1 time at intraday video-on-demand times, and not the program request of user to each collection of this collection of TV plays is used as a program request of this collection of TV plays, the video-on-demand times avoiding collection of TV plays is too high and cause its score higher.
Above-mentioned program ordering method, wherein, described method realizes based on the large data platform of Hadoop; The program data of the current all programs of reaching the standard grade of described acquisition can be specially: on the large data platform of described Hadoop, is obtained the program data of current all programs of reaching the standard grade by multiple parallel Map handling process and multiple parallel Reduce handling process; The score of the described program of described calculating can be specially: by described multiple parallel Map and Reduce handling process, respectively according to reach the standard grade number of days and the video-on-demand times of each program, calculates the score of program.
Because the quantity of the program of reaching the standard grade is normally very large, joint destination data is generally mass data, therefore the large data platform of Hadoop can be adopted to carry out the sequence of program, the large data platform of Hadoop provides a kind of framework that can carry out distributed treatment to mass data, its core is Hadoop distributed file system hdfs and MapReduce, wherein, hdfs is that mass data provides storage, and MapReduce is mass data provides calculating, adopt the large data platform of Hadoop, improve the performance that magnanimity program data is processed, the sequence to program can be realized within a short period of time.
Whole processing procedure can be realized by a series of Hadoop operation.Hadoop operation can be divided into again Map and Reduce two handling processes.In following work flow, method name if not otherwise specified, then Map refers to the handling process of Map method, and Reduce refers to the handling process of Reduce method.
Operation one: carry out preliminary treatment to program data, is increased to program of newly reaching the standard grade in buffer memory, and the program rolled off the production line is deleted from buffer memory.
Map is input as program data
Program data is timed and is synchronized to large data platform, wherein comprises the increase to program and deletion action.The input data of Map comprise the historical data in the data and hdfs of synchronously coming the same day, and the number of days that the historical data in hdfs retains can configure.
Every bar program data comprises but is not limited to as shown in table 1 below:
| opflag | Action type |
| programcode | Program code |
| onlinetime | Program on-line time (form yyyymmddhhmmss) |
| param2 | Program category |
Table 1
The output of Map is as shown in table 2 below
| KEY | VALUE |
| Programcode | , a program data | line number |
Table 2
Map export through Hadoop default treatment, become the input of Reduce, after repeat no more.
Reduce input is as shown in table 3 below:
Table 3
Wherein, line1|linenumber1, line2|linenumber2 ... a set of all value that key is identical when being Map output.
Reduce processing procedure is as follows:
In Reduce method, to program data, ascending sort is carried out according to line number (namely operating sequencing) to every bar data of synchronously coming the same day, action type is that the program (program of namely newly reaching the standard grade) increased then is increased in history program data, and action type is that it then deletes by the program (program namely rolled off the production line) deleted from history program data;
The history program data after additions and deletions operation, write back to as current program data of reaching the standard grade in hdfs, the subsequent job for next deuterzooid operation and this subjob uses;
If TV play list collection, then the program code integrated with TV play list is as key, and the program code of the acute head of TV play is that value forms a key-value pair, writes in hdfs for subsequent job.
Operation two: watching record of user process, from IPTV ticket, acquisition user card number, program code, user watch this joint object time and program category field, calculate program score; The call bill data of same Map input, comprise synchronously came the same day change list and history ticket, the number of days that history ticket retains can configure.
The input of Map: watching record of user
Every bar ticket is including but not limited to as shown in table 4 below:
Table 4
The processing procedure of Map is as follows:
In Map method, from every jargon forms data, take out user's card number, program code, user watches the initial time of this program, program category field;
According to program code, from hdfs, obtain other information of this program, judge whether this program is TV play type.
If not collection of TV plays, then watching record of user corresponding viewing number of times be 1 time;
If TV play list collection, from buffer memory, then obtain the program code of the collection of drama that this single set pair is answered, the program code contentcode of single collection is changed into the program code of collection of TV plays, program category changes the program category of collection of TV plays into, the viewing number of times of this list collection wouldn't be calculated, directly in episode information write Reduce in Map.
The output of Map:
If not TV play, then export as shown in table 5 below:
| Key | Value |
| Param2|contentcode | 1 |
Table 5
If TV play, then export as shown in table 6 below
| Key | Value |
| Param2|contentcode | begintime:cardno |
Table 6
Wherein, the contentcode in TV play is the program code of collection of TV plays, and param2 is the type of TV play head, and begintime is the viewing time in units of sky intercepted.
Reduce input is as shown in following table 7 and table 8:
| Key | Value |
| Param2|ccontentcode | List(1,1...) |
Table 7
Table 8
Reduce processing procedure is as follows:
A member variable List is created for preserving the final programme information exported in Reduce class;
For every bar data, combine according to different Param2|ccontentcode, if param2 is not TV play head, then travels through list data and be added, what obtain this program is watched number of times;
If TV play head, then calculate the quantity of begintime:cardno, once, namely a TV play is watched repeatedly by same user on the same day in same begintime:cardno appearance calculation repeatedly, only calculate once, go out TV play by viewing number of times.
According to program number, from hdfs, obtain the information of this program, from programme information, obtain program on-line time, calculate the number of days T that reaches the standard grade of this program, get the weight of configured video-on-demand times: 0.8, the weight of number of days of reaching the standard grade: 0.2, attenuation rate: 0.01, calculates the score of this program;
After Reduce method calculates the score of a program each time, in the list stored in Reduce class, when list quantity is greater than 1000, list is carried out to the flashback sequence once undertaken by mark height, delete and come 1000 programs below;
Export 1000 programs in list, as 1000 programs that score is the highest;
Reduce exports: according to score sequencing, export the details data of each program, inquire about for Set Top Box.
The flow process that the large data platform of Hadoop sorts to the program of IPTV as shown in Figure 5, comprising:
Step S500, reads in the program data that IPTV synchronously comes, and wherein, program data comprises the on-line time of program;
Step S502, reads in the history program data in Hadoop distributed file system hdfs;
Step S504, according to the action type of the program data of synchronously coming, carries out additions and deletions operation to history program data;
Step S506, writes back hdfs by the program data after process, for TV play list collection program wherein, by the corresponding relation of this Dan Jiyu collection of TV plays write hdfs;
Step S508, reads in IPTV call bill data, and this call bill data comprises the viewing recorded information of user;
Step S510, according to IPTV call bill data, determines the video-on-demand times of each program of reaching the standard grade;
Step S512, according to video-on-demand times and the number of days of reaching the standard grade of each program, calculates the score of program;
Step S514, according to the score of program, sorts to program;
Step S516, export N number of program that score is the highest, wherein, N can be configured by user.
Present invention also offers a kind of program collator, comprising: acquisition module, for obtaining the program data of current all programs of reaching the standard grade, described program data comprises the video-on-demand times of reach the standard grade number of days and the program of program; Computing module, for each program in described current all programs of reaching the standard grade, according to reach the standard grade number of days and the video-on-demand times of described program, calculates the score of described program; Order module, for the score according to each program, sorts to described current all programs of reaching the standard grade, obtains program ranking list.
Above-mentioned program collator, wherein, described computing module is by the score score of program described in following formulae discovery:
Wherein, P is the video-on-demand times of described program, and T is the number of days of reaching the standard grade of described program, Gp is video-on-demand times shared weight in the score of program, Gt is the weight of number of days shared by the score of program of reaching the standard grade, and Gp+Gt=1, Dr are the default attenuation rate corresponding with described number of days of reaching the standard grade.
Above-mentioned program collator, wherein, described acquisition module comprises: the first acquiring unit, for each program in described current all programs of reaching the standard grade, obtains the number of days of reaching the standard grade of described program; First determining unit, for determining the number of times of each user of program described in program request program described in program request within every day of reaching the standard grade in number of days of described program; Second acquisition unit, for the number of times according to each user described program described in program request within every day of reaching the standard grade in number of days of described program, obtains the video-on-demand times of described program.
Above-mentioned program collator, wherein, described current all programs of reaching the standard grade comprise: collection of TV plays program and comprise the non-collection of TV plays program of VOD and/or TVOD; Described first determining unit
Comprise: first determines subelement, for when described program is non-collection of TV plays program, determine that described each user number of times of program described in program request within every day of reaching the standard grade in number of days of described program is the actual video-on-demand times of described user; Second determines subelement, for when described program is collection of TV plays program, determines that described each user number of times of program described in program request within every day of the number of days of reaching the standard grade of described program is only 1 time.
Above-mentioned program collator, wherein, described device can be used on the large data platform of Hadoop; Described acquisition module is further used on the large data platform of described Hadoop, is obtained the program data of current all programs of reaching the standard grade by multiple parallel Map handling process and multiple parallel Reduce handling process; Described computing module is further used for by described multiple parallel Map and Reduce handling process, respectively according to reach the standard grade number of days and the video-on-demand times of each program, calculates the score of program.
The above is the preferred embodiment of the present invention; it should be pointed out that for those skilled in the art, under the prerequisite not departing from principle of the present invention; can also make some improvements and modifications, these improvements and modifications also should be considered as protection scope of the present invention.
Claims (10)
1. a program ordering method, is characterized in that, comprising:
Obtain the program data of current all programs of reaching the standard grade, described program data comprises the video-on-demand times of reach the standard grade number of days and the program of program;
To each program in described current all programs of reaching the standard grade, according to reach the standard grade number of days and the video-on-demand times of described program, calculate the score of described program;
According to the score of each program, described current all programs of reaching the standard grade are sorted, obtains program ranking list.
2. method as claimed in claim 1, it is characterized in that, described reach the standard grade number of days and video-on-demand times according to described program, the score calculating described program comprises:
Score score by program described in following formulae discovery:
Wherein, P is the video-on-demand times of described program, and T is the number of days of reaching the standard grade of described program, Gp is video-on-demand times shared weight in the score of program, Gt is the weight of number of days shared by the score of program of reaching the standard grade, and Gp+Gt=1, Dr are the default attenuation rate corresponding with described number of days of reaching the standard grade.
3. method as claimed in claim 1, it is characterized in that, the program data of the current all programs of reaching the standard grade of described acquisition comprises:
To each program in described current all programs of reaching the standard grade, obtain the number of days of reaching the standard grade of described program;
Determine the number of times of each user of program described in program request program described in program request within every day of reaching the standard grade in number of days of described program;
According to the number of times of each user described program described in program request within every day of reaching the standard grade in number of days of described program, obtain the video-on-demand times of described program.
4. method as claimed in claim 3, it is characterized in that, described current all programs of reaching the standard grade comprise: collection of TV plays program and comprise the non-collection of TV plays program of video frequency request program VOD and/or true video frequency request program TVOD; Describedly determine that each user of program described in program request number of times of program described in program request within every day of reaching the standard grade in number of days of described program is specially:
When described program is non-collection of TV plays program, determine that described each user number of times of program described in program request within every day of reaching the standard grade in number of days of described program is the actual video-on-demand times of described user;
When described program is collection of TV plays program, determine that described each user number of times of program described in program request within every day of reaching the standard grade in number of days of described program is only 1 time.
5. method as claimed in claim 1, it is characterized in that, described method realizes based on the large data platform of Hadoop; The program data of the current all programs of reaching the standard grade of described acquisition is specially:
On the large data platform of described Hadoop, obtained the program data of current all programs of reaching the standard grade by multiple parallel Map handling process and multiple parallel Reduce handling process;
The score of the described program of described calculating is specially:
By described multiple parallel Map and Reduce handling process, respectively according to reach the standard grade number of days and the video-on-demand times of each program, calculate the score of program.
6. a program collator, is characterized in that, comprising:
Acquisition module, for obtaining the program data of current all programs of reaching the standard grade, described program data comprises the video-on-demand times of reach the standard grade number of days and the program of program;
Computing module, for each program in described current all programs of reaching the standard grade, according to reach the standard grade number of days and the video-on-demand times of described program, calculates the score of described program;
Order module, for the score according to each program, sorts to described current all programs of reaching the standard grade, obtains program ranking list.
7. device as claimed in claim 6, it is characterized in that, described computing module is by the score score of program described in following formulae discovery:
Wherein, P is the video-on-demand times of described program, and T is the number of days of reaching the standard grade of described program, Gp is video-on-demand times shared weight in the score of program, Gt is the weight of number of days shared by the score of program of reaching the standard grade, and Gp+Gt=1, Dr are the default attenuation rate corresponding with described number of days of reaching the standard grade.
8. device as claimed in claim 6, it is characterized in that, described acquisition module comprises:
First acquiring unit, for each program in described current all programs of reaching the standard grade, obtains the number of days of reaching the standard grade of described program;
First determining unit, for determining the number of times of each user of program described in program request program described in program request within every day of reaching the standard grade in number of days of described program;
Second acquisition unit, for the number of times according to each user described program described in program request within every day of reaching the standard grade in number of days of described program, obtains the video-on-demand times of described program.
9. device as claimed in claim 8, it is characterized in that, described current all programs of reaching the standard grade comprise: collection of TV plays program and comprise the non-collection of TV plays program of VOD and/or TVOD; Described first determining unit comprises:
First determines subelement, for when described program is non-collection of TV plays program, determines that described each user number of times of program described in program request within every day of reaching the standard grade in number of days of described program is the actual video-on-demand times of described user;
Second determines subelement, for when described program is collection of TV plays program, determines that described each user number of times of program described in program request within every day of reaching the standard grade in number of days of described program is only 1 time.
10. device as claimed in claim 6, it is characterized in that, described device can be used on the large data platform of Hadoop; Described acquisition module is further used on the large data platform of described Hadoop, is obtained the program data of current all programs of reaching the standard grade by multiple parallel Map handling process and multiple parallel Reduce handling process;
Described computing module is further used for by described multiple parallel Map and Reduce handling process, respectively according to reach the standard grade number of days and the video-on-demand times of each program, calculates the score of program.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410354110.8A CN105323602A (en) | 2014-07-23 | 2014-07-23 | Program ordering method and device |
| PCT/CN2015/072751 WO2015117571A1 (en) | 2014-07-23 | 2015-02-11 | Program ranking method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410354110.8A CN105323602A (en) | 2014-07-23 | 2014-07-23 | Program ordering method and device |
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| CN105323602A true CN105323602A (en) | 2016-02-10 |
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| Application Number | Title | Priority Date | Filing Date |
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| CN201410354110.8A Withdrawn CN105323602A (en) | 2014-07-23 | 2014-07-23 | Program ordering method and device |
Country Status (2)
| Country | Link |
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| CN (1) | CN105323602A (en) |
| WO (1) | WO2015117571A1 (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105828181A (en) * | 2016-04-14 | 2016-08-03 | 青岛海信传媒网络技术有限公司 | Video sequencing method and device based on video on-demand volume |
| CN106604081A (en) * | 2016-12-30 | 2017-04-26 | Tcl集团股份有限公司 | Method and device for generating video recommendation pool |
| CN107707964A (en) * | 2016-08-08 | 2018-02-16 | 华为软件技术有限公司 | The method and apparatus for predicting video content temperature |
| CN108074126A (en) * | 2017-03-30 | 2018-05-25 | 北京市商汤科技开发有限公司 | A kind of business statistical method, device and electronic equipment |
| CN108833985A (en) * | 2018-07-09 | 2018-11-16 | 深圳市茁壮网络股份有限公司 | A kind of multimedia programming methods of marking, ranking list generation method and device |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101739419A (en) * | 2008-11-04 | 2010-06-16 | 未序网络科技（上海）有限公司 | System for sequencing multi-index comprehensive weight video |
| CN102682132A (en) * | 2012-05-18 | 2012-09-19 | 合一网络技术(北京)有限公司 | Method and system for searching information based on word frequency, play amount and creation time |
| CN102740143A (en) * | 2012-07-03 | 2012-10-17 | 合一网络技术(北京)有限公司 | Network video ranking list generation system based on user behavior and method thereof |
| CN102769781A (en) * | 2012-07-17 | 2012-11-07 | 青岛海信传媒网络技术有限公司 | Method and device for recommending television program |
| CN103064862A (en) * | 2011-10-24 | 2013-04-24 | 阿里巴巴集团控股有限公司 | Method and device of multi-index-sorting data processing |
| CN103200427A (en) * | 2012-01-09 | 2013-07-10 | 腾讯科技（深圳）有限公司 | Network programme playing method and system, multi-media server and terminal |
-
2014
- 2014-07-23 CN CN201410354110.8A patent/CN105323602A/en not_active Withdrawn
-
2015
- 2015-02-11 WO PCT/CN2015/072751 patent/WO2015117571A1/en active Application Filing
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101739419A (en) * | 2008-11-04 | 2010-06-16 | 未序网络科技（上海）有限公司 | System for sequencing multi-index comprehensive weight video |
| CN103064862A (en) * | 2011-10-24 | 2013-04-24 | 阿里巴巴集团控股有限公司 | Method and device of multi-index-sorting data processing |
| CN103200427A (en) * | 2012-01-09 | 2013-07-10 | 腾讯科技（深圳）有限公司 | Network programme playing method and system, multi-media server and terminal |
| CN102682132A (en) * | 2012-05-18 | 2012-09-19 | 合一网络技术(北京)有限公司 | Method and system for searching information based on word frequency, play amount and creation time |
| CN102740143A (en) * | 2012-07-03 | 2012-10-17 | 合一网络技术(北京)有限公司 | Network video ranking list generation system based on user behavior and method thereof |
| CN102769781A (en) * | 2012-07-17 | 2012-11-07 | 青岛海信传媒网络技术有限公司 | Method and device for recommending television program |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105828181A (en) * | 2016-04-14 | 2016-08-03 | 青岛海信传媒网络技术有限公司 | Video sequencing method and device based on video on-demand volume |
| CN105828181B (en) * | 2016-04-14 | 2019-06-25 | 聚好看科技股份有限公司 | Video sequencing method and device based on video on demand amount |
| CN107707964A (en) * | 2016-08-08 | 2018-02-16 | 华为软件技术有限公司 | The method and apparatus for predicting video content temperature |
| CN106604081A (en) * | 2016-12-30 | 2017-04-26 | Tcl集团股份有限公司 | Method and device for generating video recommendation pool |
| CN106604081B (en) * | 2016-12-30 | 2020-03-24 | Tcl集团股份有限公司 | Method and device for generating video recommendation pool |
| CN108074126A (en) * | 2017-03-30 | 2018-05-25 | 北京市商汤科技开发有限公司 | A kind of business statistical method, device and electronic equipment |
| CN108833985A (en) * | 2018-07-09 | 2018-11-16 | 深圳市茁壮网络股份有限公司 | A kind of multimedia programming methods of marking, ranking list generation method and device |
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| Publication number | Publication date |
|---|---|
| WO2015117571A1 (en) | 2015-08-13 |
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