CN105681908A - Broadcast television system based on individual watching behaviour and personalized programme recommendation method thereof - Google Patents

Abstract

The invention discloses a broadcast television system based on an individual watching behaviour and a personalized programme recommendation method thereof. The broadcast television system provided by the invention comprises an input part, a programme information storage part, an analysis unit and a recommendation information sending part, wherein the input part is used for inputting various parameters and instructions required by the broadcast television system for personalized programme recommendation; the programme information storage part is used for storing information and data related to all kinds of broadcast television programmes; the analysis unit is used for generating a personalized programme list to be sent according to various parameters input through the input part and the information related to the broadcast television programmes read from the programme information storage part, and determining a recommendation crowd as a sending object; and the recommendation information sending part is used for sending the personalized programme list to the recommendation crowd. By means of the structure, different personalized programme lists can be recommended to different users of the broadcast television system.

Description

Based on broadcast television system and the individual character program commending method thereof of individual viewing behavior

The application submitted on July 1st, 2013, application number is 201310272576.9, point case application of invention and created name " the individual character program commending method in broadcast television system and this system ", this application claims application number is 201210342540.9, and the applying date is the right of priority of the Chinese patent application on September 17th, 2012.

Technical field

The present invention relates in broadcasting television technology field, in more detail, it relates to the individual character program commending method in the broadcast television system of corresponding individual character program and this broadcast television system can be recommended respectively to specific rating colony.

Background technology

Along with Television programme become increasingly abundant, broadcast television user is just facing the problem of " information overload " similar with Internet user, under such circumstances, how can follow the tracks of the interests change of user, the problem finding user's content of TV program interested is very urgent, and broadcast television individual character program recommendation system can effectively address this problem.

The theoretical basis of broadcast television individual character program commending is decision support technique and data mining technology. Decision support system (DSS) (DSS, DecisionSupportSystem) is proposed the seventies in 20th century first by American scientist Keen and ScottMorton, has achieved huge development to the eighties in 20th century. Along with the continuous research and discovery of domestic and international experts and scholars, nowadays decision support system (DSS) has developed into the New Types of Decision Support Systems that data warehouse, on-line analytical processing and data mining combine. Its typical feature is the information obtaining aid decision making from mass data. Data mining (DM, DataMining) is a special kind of skill extracting valuable knowledge from mass data. Constantly perfect along with data mining technology, data mining obtains in decision support field and applies more and more widely. These knowledge are that decision-making provides strong support. Broadcast television individual character program commending, based on decision support system (DSS), builds the model and method dealt with problems, and by data mining technology digging user viewing behavior rule and the potential rating crowd of excavation.

The essence of individual character program commending is that program user watched sorts, and in this field, current existing method has the sort algorithm etc. under simple statistics algorithm, simple cascade cluster algorithm, Bayes network algorithm, multiple feature.The common issue that above several method exists only to realize the sequence that user watches program, but can not provide different service for without the user of feature, does not possess ability viewer hived off simultaneously.

Summary of the invention

The present invention a little makes for solving the aforementioned problems in the prior, its object is to provide the individual character program commending method in a kind of broadcast television system and this broadcast television system, broadcast TV program can be recommended flexibly, it is achieved the function of individual character program commending according to the different demands of viewer.

For this reason, the present invention provides a kind of broadcast television system, comprising: input portion, carries out the various parameter needed for individual character program commending and various instruction for inputting above-mentioned broadcast television system; Programme information storage portion, for storing the information about various broadcast TV program and data; Analytical unit, the information about broadcast TV program utilizing the various parameter inputted by input portion and reading from above-mentioned programme information storage portion, is generated the individual character programme to be sent, and determines the recommendation crowd as sending object; And recommendation information transmission portion, send above-mentioned individual character programme to the above-mentioned rating colony determined.

In addition, the present invention also provides the individual character program commending method in a kind of broadcast television system, this broadcast television system comprises input portion, programme information storage portion, analytical unit and recommendation information transmission portion, it is characterized in that, the method comprises the following steps: inputs above-mentioned broadcast television system by input portion and carries out the various parameter needed for individual character program commending and various instruction; Analytical procedure, the information about broadcast TV program utilizing the various parameter inputted by input portion and reading from above-mentioned programme information storage portion, is generated the individual character programme to be sent, and determines the recommendation crowd as sending object; And send portion by recommendation information, send above-mentioned individual character programme to the above-mentioned rating colony determined.

Useful effect:

Present invention achieves the solution selecting individual character program commending method according to the different demand of broadcast television user flexibly. The program category analysis of threshold method provided or clustering method, it is possible to realize assisting program making business to stablize the loyal spectators of program, find the object of the potential spectators of program. User watched behavior analysis method is by the analysis of particular user viewing behavior, it is possible to realizes the rating preference effectively holding user, recommends the object of individual character program.

Accompanying drawing explanation

Fig. 1 is the schematic diagram of the concrete structure representing the broadcast television system 100 that the present invention relates to.

Fig. 2 is the schema representing the individual character program recommendation process performed by above-mentioned broadcast television system 100.

Fig. 3 is the schema of first example of the analysis process that the analytical unit 130 represented in above-mentioned broadcast television system 100 performs.

Fig. 4 is the schema of the 2nd example of the analysis process that the analytical unit 130 represented in above-mentioned broadcast television system 100 performs.

Embodiment

Below, the specific embodiment of the individual character program commending method in the broadcast television system that the present invention relates to and this system is described with reference to accompanying drawing.

Fig. 1 is the schematic diagram of the concrete structure representing the broadcast television system 100 that the present invention relates to.

As shown in Figure 1, the broadcast television system 100 of the present invention comprises input portion 110, programme information storage portion 120, analytical unit 130, recommendation information transmission portion 140.

Wherein, for inputting, above-mentioned broadcast television system carries out data and the various instructions such as the various parameters needed for individual character program commending in input portion 110, and it can be keyboard, touch-screen, handwriting input device, mouse etc.

Programme information storage portion 120 for storing the information about various broadcast TV program, the type of such as program, the time parameter of program, the various threshold values etc. set in advance. In addition, above-mentioned programme information storage portion 120 can also store other data needed for above-mentioned broadcast television system 100 n-back test. These information and data can be stored in advance in above-mentioned programme information storage portion 120, it is also possible to be stored in above-mentioned programme information storage portion 120 by input portion 110 after inputting.

Analytical unit 130 is for the information about broadcast TV program utilizing the data such as the various parameters by input portion 110 input and read from above-mentioned programme information storage portion 120, above-mentioned various parameter and information are carried out analyzing and processing, the individual character programme that generation to be sent, and determine the recommendation crowd as sending object. in the present invention, analytical unit 130 can utilize program category analytical method (such as program category analysis of threshold method, program category cluster analysis method), viewing behavior analytical method (the individual behavioural analysis method of such as rating, rating group behavior analytical method) in any one, wherein, when utilizing program threshold value type analysis method or program category cluster analysis method, recommendation crowd can be determined by calculating rating crowd's occupation rate, when utilizing the individual behavioural analysis method of rating or rating group behavior analytical method, the channel of recommended program list can be determined by calculating channel contributions rate, thus generate the individual character programme for recommending. above-mentioned analytical unit 130 which analytical method of choice for use, it is possible to determine by the user instruction from input portion 110. and, the particular content about these analytical methods incites somebody to action detail below.

Recommendation information transmission portion 140, sends above-mentioned individual character programme by the mode such as short message mode or e-mail to the rating individuality determined or colony. At this, this recommendation information transmission portion 140 can be short message sending platform, it is also possible to be that e-mail sends platform.

Then, with reference to Fig. 2 illustrate above-mentioned broadcast television system institute 100 execution individual character program recommendation process.

First, user carries out data and the various instructions (step S211) such as the various parameters needed for individual character program commending by input portion 110 input. At this, these parameters can comprise the parameter of user location, the class shape parameter of program and time parameter etc. Various instruction can comprise the instruction of the analytical procedure utilized for selection analysis unit 130, it is also possible to comprises the instruction of the transmission mode determining recommendation information transmission portion 140. The data such as above-mentioned parameter can be stored in programme information storage portion 120, it is also possible to is sent to analytical unit 130 and uses.

Then, in step S212, above-mentioned various parameter and information are carried out analyzing and processing by analytical unit 130, generate the individual character programme to be sent, and determine the recommendation individuality as sending object or colony. At this, analytical unit 130 can determine to use which kind of analytical method according to the instruction from input portion 110. Analytical unit 130 determines the rating individuality as recommended or colony, and generates individual character programme that is individual to above-mentioned recommendation or colony's recommendation.

Then, in step S213, above-mentioned recommendation information transmission portion 140 is according to selected transmission mode, and to the rating determined, individual or colony sends individual character programme.

It should be noted that, the term " crowd " recorded in this specification sheets, it is possible to the rating being independent is individual, it is also possible to the rating colony being made up of multiple rating individuality.

Below, with reference to Fig. 3 and Fig. 4, the analysis action performed by above-mentioned analytical unit 130 is described in detail. Fig. 3 is the schema of first example of the analysis process that the analytical unit 130 represented in above-mentioned broadcast television system 100 performs. Fig. 4 is the schema of the 2nd example of the analysis process that the analytical unit 130 represented in above-mentioned broadcast television system 100 performs.

(the first example: program category analytical method)

First, in step S311, carry out Selecting parameter. At this, above-mentioned parameter comprises regional parameters, program category parameter and time parameter.

In the present embodiment, regional parameters can select the data in any area existed in database, and wherein, above-mentioned data are to economize or in units of city-level data.

In the present embodiment, program category parameter can select two-stage program classification or three grades of program classifications, and wherein the two-stage classification of program category comprises 4 classes, is respectively news controlling, amusement class program, educational program and service class program; Above-mentioned three grades of program classifications of above-mentioned two-stage program classification comprise 27 classes altogether, and wherein above-mentioned three grades of program classifications of above-mentioned news controlling are roundup news message program, classified news message program, Special Topics in Journalism class program, news talk show, world news class program, large-scale news program; Three grades of program classifications of above-mentioned amusement class program are TV play program, physical culture program, film class program, variety show, music program, drama programs, game shows, reality TV show program, amusement talk. feature program, international amusement class program, large-scale entertainment; Above-mentioned three grades of program classifications of above-mentioned educational program are social education program, juvenile. young program, international education class program, large-scale then educational programs; Three grades of program classifications of above-mentioned service class program are Service Programmer, financing program, commercial paper program, country's service class program, channel publicity. rating service program, large-scale service program.

In addition, in the present embodiment, in Duan Weiyi week analysis time selected by above-mentioned time parameter, the time period of recommended program list is next week of current time.

In step S312, generate individual character programme according to above-mentioned program category, and using the programme of this individual character programme as the above-mentioned program category in next week of current time.

In step S313, long when calculating rating according to above-mentioned regional parameters, program category parameter and time parameter. During above-mentioned rating, length can be obtained by following formula:

$T=\underset{i=1}{\overset{n}{\Σ}}{T}_i$

Wherein:

N represents the number of programs in selected program category;

T_iWhen representing effective rating long.

In the present embodiment, during effective rating, length comprises following four kinds of situations:

As WR_Begin < TV_Begin, WR_End, < during TV_End, during effective rating, length is defined as WR_End-TV_Begin;

As TV_Begin < WR_Begin, TV_End, < during WR_End, during effective rating, length is defined as TV_End-WR_Begin;

As WR_Begin < TV_Begin, TV_End, < during WR_End, during effective rating, length is defined as TV_End-TV_Begin;

As TV_Begin < WR_Begin, WR_End, < during TV_End, during effective rating, length is defined as WR_End-WR_Begin.

Wherein, WR_Begin and WR_End represents the time (WR_Begin < WR_End) that the rating record satisfied condition starts and terminates respectively;

TV_Begin and TV_End represents that certain program broadcasts the time (TV_Begin < TV_End) starting and terminating respectively;

Step S314, according to progress row rating population analysis during the above-mentioned rating calculated.

In the present embodiment, analyze above-mentioned rating crowd and can use any one in program category analysis of threshold method or program category cluster analysis method.

Above-mentioned program category analysis of threshold method comprises the following steps:

Utilize set in advance two threshold value i and j (0 < i < j < during maximum rating length), carries out following classification by rating crowd:

As T≤i, it is loss family, namely seldom watches the potential family of above-mentioned program category;

When i < T < during j, is average family, namely pays close attention to more to the above-mentioned type program, be not again the average family extremely made earnest efforts;

As j≤T, it is loyal family, the loyal family namely the above-mentioned type program extremely paid close attention to; Wherein, when T is above-mentioned rating long.

In addition, above-mentioned program category cluster analysis method comprises the following steps: when setting above-mentioned rating to be clustered, long number is n (n>0), the crowd cluster number k (0<k≤n) of length during above-mentioned rating,

Steps A: when choosing k above-mentioned rating at random, length is as the initial average of each bunch, and the statistical average value of length when above-mentioned average is defined as each bunch of above-mentioned rating,

Step B: definition square error E

$E=\underset{i=1}{\overset{k}{\&Sigma;}}\underset{T\&Element;Ci}{\&Sigma;}|T-m_i{|}^2$

Wherein:

When T represents above-mentioned rating long;

C_iRepresent certain bunch;

m_iRepresent bunch C_iAbove-mentioned rating time length average;

According to as defined above, calculate the above-mentioned square error E of length during each the above-mentioned rating in each bunch, and length during each above-mentioned rating is assigned to the most similar bunch, namely with the distance of bunch average minimum bunch;

Step C: for upgrade after bunch, the average of length when calculating above-mentioned rating in each bunch;

Step D: repeating step B to step C, until bunch no longer changing after upgrading, then obtains the k after above-mentioned cluster analysis processes bunch, i.e. k class crowd;

According to each bunch of average by sorting to little greatly, define each bunch successively for rank 1 family, rank 2 family ... wherein rank 1 family is the highest to this type of program informativeness, and hereafter rank informativeness successively decreases successively.

Then, in step S315, rating crowd's occupation rate is calculated according to the above-mentioned rating crowd that above-mentioned analysis of threshold draws. Above-mentioned rating crowd's occupation rate can utilize following formula to obtain:

$PER=\frac{N^{\&prime;}}{N}\&times;100\%$

Wherein:

N' represents the rating amount (0≤N') of certain class crowd;

N represents the total amount of the rating (N'≤N) in selected area.

In step S316, with reference to above-mentioned rating crowd's occupation rate, it is determined that as the recommendation crowd of the recommended sending individual character programme. Wherein, above-mentioned recommendation crowd can be any crowd or the combination of any several crowds. Like this, just generate the individual character programme to be sent by analytical unit 130, and determine the recommendation crowd as sending object.

(the 2nd example: viewing behavior analytical method)

Viewing behavior analytical method comprises individual viewing behavior analytical method and colony's viewing behavior analytical method, and wherein, individual viewing behavior analytical method selects the audience information of a certain individuality to carry out analytical calculation, obtains the recommendation channel to this individuality recommended program list; Colony's viewing behavior analytical method selects the audience information of a certain rating colony to carry out analytical calculation, obtains the recommendation channel to this colony's recommended program list. Below, the concrete analysis step of above-mentioned individual viewing behavior analytical method and colony's viewing behavior analytical method is described respectively with reference to Fig. 4.

1, individual viewing behavior analytical method

Below, the detailed process of individual viewing behavior analytical method is described with reference to Fig. 4.

First, in step S411, selected as particular user and the time parameter of analytic target, and extract the information of above-mentioned particular user.

Then, in step S412, long when calculating the rating of above-mentioned particular user, during above-mentioned rating, length can be obtained by following formula:

$T_{s1}=\underset{l=1}{\overset{n4}{\&Sigma;}}\underset{k=1}{\overset{n3}{\&Sigma;}}\underset{j=1}{\overset{n2}{\&Sigma;}}\underset{i=1}{\overset{n1}{\&Sigma;}}{T}_{i,j,k,l}$

$T_{s2}=\underset{k=1}{\overset{n3}{\&Sigma;}}\underset{j=1}{\overset{n2}{\&Sigma;}}\underset{i=1}{\overset{n1}{\&Sigma;}}{T}_{i,j,k}$

Wherein:

T_s1Represent that above-mentioned particular user watches that the rating of all programs is long time total;

T_s2Represent that above-mentioned particular user watches that the rating of certain class program is long time total;

n₁Represent that one day, above-mentioned particular user watched the number of programs of certain channel class program;

n₂Represent that one day, above-mentioned particular user watched the number of channels of certain class program;

n₃Represent the input number of days of length when analyzing particular user above-mentioned 3rd rating;

n₄Represent that above-mentioned particular user watches program category sum;

T_i,j,k,lAnd T_i,j,kRepresent long when watching effective rating of certain concrete program.

Then, in step S413, utilize following formula to calculate the rating preference of this particular user:

And by above-mentioned rating preference by by greatly to little sequence, to the program category that above-mentioned particular user recommends rating preference maximum.

In step S414, according to the calculation result of above-mentioned rating preference, choose the program category that above-mentioned rating preference is maximum, utilize the result looked when calculating above-mentioned rating, utilize following formula to calculate channel contributions rate:

${\mathrm{\&psi;}}_s=\frac{T_{s3}}{T_{s2}}$

Wherein:

$T_{s3}=\underset{k=1}{\overset{n3}{\&Sigma;}}\underset{i=1}{\overset{n1}{\&Sigma;}}{T}_{i,k}$

T_s3When representing that the rating of certain such program of channel is watched at the concrete family of above-mentioned use long;

n₁Represent that one day, above-mentioned particular user watched the number of programs of certain such program of channel;

n₃Represent the input number of days of length during the rating analyzing above-mentioned particular user;

T_i,kRepresent long when watching effective rating of certain concrete program.

Then, in step S415, above-mentioned channel contributions rate is sorted, recommended program list is generated according to above-mentioned channel contributions rate and above-mentioned rating preference, determining to recommend the channel of above-mentioned recommended program list, wherein, above-mentioned channel can select single channel or the combinations of channels of arbitrary channel contribution rate.

2, colony's viewing behavior analytical method

Colony's viewing behavior analytical method is similar with the general steps of individual viewing behavior analytical method, therefore, below the same detailed process that colony's viewing behavior analytical method is described with reference to Fig. 4.

First, in step S411, selected as rating group types and the time parameter of analytic target, and extract the information of this rating colony, such as, group classification comprise real estate/building, service sector, industry/geology, broadcast television/culture and arts, computer (IT/ internet), traffic/transport, education/training, finance (bank/security/insurance), hotel/tourism/food and drink, trade/import and export, media/advertisement/consulting, agricultural/aquatic products, retirement, student, medical treatment/health care/pharmacy, government bodies and other etc. totally 17 class crowd.

Then, in step S412, utilize following formula calculate selected by the rating of above-mentioned rating colony time long:

$T_{c1}=\underset{p=1}{\overset{n5}{\&Sigma;}}\underset{l=1}{\overset{n4}{\&Sigma;}}\underset{k=1}{\overset{n3}{\&Sigma;}}\underset{j=1}{\overset{n2}{\&Sigma;}}\underset{i=1}{\overset{n1}{\&Sigma;}}{T}_{i,j,k,l,p}$

$T_{c2}=\underset{p=1}{\overset{n5}{\&Sigma;}}\underset{k=1}{\overset{n3}{\&Sigma;}}\underset{j=1}{\overset{n2}{\&Sigma;}}\underset{i=1}{\overset{n1}{\&Sigma;}}{T}_{i,j,k,p}$

Wherein:

T_c1Represent that above-mentioned rating colony watches that the rating of all programs is long time total;

T_c2Represent that above-mentioned rating colony watches that the rating of certain class program is long time total;

n₁Represent that one day, the number of programs of certain class program of certain channel was watched by above-mentioned rating colony;

n₂Represent that one day, the number of channels of certain class program was watched by above-mentioned rating colony;

n₃Represent the input number of days of length when analyzing above-mentioned rating colony rating;

n₄Represent that the sum of program category is watched by above-mentioned rating colony;

n₅Represent the individual number of rating that above-mentioned rating colony comprises;

T_i,j,k,l,pAnd T_i,j,k,pRepresent long when watching effective rating of certain concrete program.

Then, in step S413, following formula is utilized to calculate the rating preference of above-mentioned rating colony

Further, by rating preference by by greatly to little order, to the program category that group of subscribers recommends rating preference maximum.

Then, in step S414, according to the above-mentioned rating preference calculated, choose the program category that rating preference is maximum, and utilize the result that rating duration calculation obtains, calculate channel contributions rate Ψ based on following formula_c:

${\mathrm{\&psi;}}_c=\frac{T_{c3}}{T_{c2}}$

Wherein:

$T_{c3}=\underset{p=1}{\overset{n5}{\&Sigma;}}\underset{k=1}{\overset{n3}{\&Sigma;}}\underset{i=1}{\overset{n1}{\&Sigma;}}{T}_{i,k,p}$

T_c3When representing that the rating of certain such program of channel is watched by above-mentioned rating colony long;

n₁Represent that one day, the number of programs of such program was watched by above-mentioned rating colony;

n₃Represent the input number of days of length when analyzing above-mentioned rating colony rating;

n₅Represent the individual number comprised in above-mentioned rating colony;

T_i,k,pRepresent long when watching effective rating of certain concrete program.

Finally, in step S415, channel contributions rate being sorted, the more big channel contributions rate of gained value is more high, and, according to channel contributions rate, select the channel of recommended program list, wherein, it is possible to select single channel or the combinations of channels of arbitrary channel contribution rate.

According to aforesaid method, it is possible to select individual character program commending method flexibly according to the different demand of broadcast television user, to reach the object of promotion individual character program.

Preferably, the user that the present embodiment is above-mentioned can be individual user, it is also possible to be group of subscribers.

According to as above above-mentioned the present invention, it is achieved that select the solution of individual character program commending method flexibly according to the different demand of broadcast television user. The program category analysis of threshold method provided or clustering method, it is possible to realize assisting program making business to stablize the loyal spectators of program, find the object of the potential spectators of program. User watched behavior analysis method is by the analysis of particular user viewing behavior, it is possible to realizes the rating preference effectively holding user, recommends the object of individual character program.

Under the above-mentioned instruction of the present invention, those skilled in the art can improve individual character program commending method and system to broadcast television provided by the present invention on the basis of above-described embodiment, and these improvement all drop in protection scope of the present invention. Those skilled in the art it is to be appreciated that above-mentioned specific descriptions just explain the object of the present invention better, protection scope of the present invention by claim and etc. jljl limit.

Claims (4)

1. a broadcast television system, it is characterised in that, comprising:

Input portion, carries out the various parameter needed for individual character program commending and various instruction for inputting above-mentioned broadcast television system;

Programme information storage portion, for storing the information about various broadcast TV program and data;

Analytical unit, the information about broadcast TV program utilizing the various parameter inputted by input portion and read from above-mentioned programme information storage portion, the individual character programme that generation to be sent, and determine the recommendation crowd as sending object, wherein, analytical unit utilizes individual viewing behavior analytical method, the channel of recommended program list is determined by channel contributions rate, thus generate the individual character programme for recommending, described individual viewing behavior analytical method selects the audience information of a certain individuality to carry out analytical calculation, obtains the recommendation channel to this individuality recommended program list; And

Recommendation information transmission portion, to the above-mentioned individual character programme of above-mentioned referrer's pocket transmission determined.

2. broadcast television system as claimed in claim 1, it is characterised in that, the execution of above-mentioned analytical unit is following analyzes process:

(1) select particular user and time parameter, and extract the information of above-mentioned particular user;

(2) when utilizing following formula to calculate the rating of above-mentioned particular user long:

$T_{s1}=\underset{l=1}{\overset{n4}{\&Sigma;}}\underset{k=1}{\overset{n3}{\&Sigma;}}\underset{j=1}{\overset{n2}{\&Sigma;}}\underset{i=1}{\overset{n1}{\&Sigma;}}{T}_{i,j,k,l}$

$T_{s2}=\underset{k=1}{\overset{n3}{\&Sigma;}}\underset{j=1}{\overset{n2}{\&Sigma;}}\underset{i=1}{\overset{n1}{\&Sigma;}}{T}_{i,j,k}$

Wherein:

T_s1Represent that above-mentioned particular user watches that the rating of all programs is long time total;

T_s2Represent that above-mentioned particular user watches that the rating of certain class program is long time total;

n₁Represent that one day, above-mentioned particular user watched the number of programs of certain channel class program;

n₂Represent that one day, above-mentioned particular user watched the number of channels of certain class program;

n₃Represent the input number of days of length when analyzing above-mentioned particular user above-mentioned rating;

n₄Represent that above-mentioned particular user watches program category sum;

T_i,j,k,lAnd T_i,j,kRepresent long when watching effective rating of certain concrete program;

(3) following formula is utilized to calculate rating preference:

And by above-mentioned rating preference by by greatly to little sequence;

(4) according to the calculation result of above-mentioned rating preference, choose the program category that above-mentioned rating preference is maximum, utilize the result looked when calculating above-mentioned rating, be calculated as follows channel contributions rate:

${\mathrm{\&psi;}}_s=\frac{T_{s3}}{T_{s2}}$

Wherein:

$T_{s3}=\underset{k=1}{\overset{n3}{\&Sigma;}}\underset{i=1}{\overset{n1}{\&Sigma;}}{T}_{i,k}$

T_s3When representing that above-mentioned particular user watches the rating of certain such program of channel long;

n₁Represent that one day, above-mentioned particular user watched the number of programs of certain such program of channel;

n₃Represent the input number of days of length during the rating analyzing above-mentioned particular user;

T_i,kRepresent long when watching effective rating of certain concrete program;

(5) above-mentioned channel contributions rate is sorted, recommended program list is generated according to above-mentioned channel contributions rate and above-mentioned rating preference, and determine to recommend the channel of above-mentioned recommended program list, and wherein, the single channel of above-mentioned channel selection arbitrary channel contribution rate or combinations of channels.

3. the individual character program commending method in broadcast television system, this broadcast television system comprises input portion, programme information storage portion, analytical unit and recommendation information transmission portion, it is characterised in that, the method comprises the following steps:

Input above-mentioned broadcast television system by input portion and carry out the various parameter needed for individual character program commending and various instruction;

Analytical procedure, the information about broadcast TV program utilizing the various parameter inputted by input portion and read from above-mentioned programme information storage portion, the individual character programme that generation to be sent, and determine the recommendation crowd as sending object, wherein, analytical unit utilizes individual viewing behavior analytical method, is determined the channel of recommended program list by channel contributions rate, thus generates the individual character programme for recommending; And send portion by recommendation information, to the above-mentioned individual character programme of above-mentioned referrer's pocket transmission determined.

4. individual character program commending method as claimed in claim 3, it is characterised in that, the execution of above-mentioned analytical procedure is following analyzes process:

(1) select particular user and time parameter, and extract the information of above-mentioned particular user;

(2) when utilizing following formula to calculate the rating of above-mentioned particular user long:

$T_{s1}=\underset{l=1}{\overset{n4}{\&Sigma;}}\underset{k=1}{\overset{n3}{\&Sigma;}}\underset{j=1}{\overset{n2}{\&Sigma;}}\underset{i=1}{\overset{n1}{\&Sigma;}}{T}_{i,j,k,l}$

$T_{s2}=\underset{k=1}{\overset{n3}{\&Sigma;}}\underset{j=1}{\overset{n2}{\&Sigma;}}\underset{i=1}{\overset{n1}{\&Sigma;}}{T}_{i,j,k}$

Wherein:

T_s1Represent that above-mentioned particular user watches that the rating of all programs is long time total,

T_s2Represent that above-mentioned particular user watches that the rating of certain class program is long time total,

n₁Represent that one day, above-mentioned particular user watched the number of programs of certain channel class program,

n₂Represent that one day, above-mentioned particular user watched the number of channels of certain class program,

n₃Represent the input number of days of length when analyzing particular user above-mentioned rating,

n₄Represent that above-mentioned particular user watches program category sum,

T_i,j,k,lAnd T_i,j,kRepresent long when watching effective rating of certain concrete program,

(3) following formula is utilized to calculate rating preference:

And by above-mentioned rating preference by by greatly to little sequence;

(4) according to the calculation result of above-mentioned rating preference, choose the program category that above-mentioned rating preference is maximum, utilize the result looked when calculating above-mentioned rating, be calculated as follows channel contributions rate:

${\mathrm{\&psi;}}_s=\frac{T_{s3}}{T_{s2}}$

Wherein:

$T_{s3}=\underset{k=1}{\overset{n3}{\&Sigma;}}\underset{i=1}{\overset{n1}{\&Sigma;}}{T}_{i,k}$

T_s3When representing that above-mentioned user watches the rating of certain such program of channel long,

n₁Represent that one day, above-mentioned user watched the number of programs of certain such program of channel,

n₃Represent the input number of days of length during the rating analyzing above-mentioned user,

T_i,kRepresent long when watching effective rating of certain concrete program,

(5) above-mentioned channel contributions rate is sorted, recommended program list is generated according to above-mentioned channel contributions rate and above-mentioned rating preference, and determine to recommend the channel of above-mentioned recommended program list, and wherein, the single channel of above-mentioned channel selection arbitrary channel contribution rate or combinations of channels.