Predicting Television Ratings and Its Application to Taiwan Cable TV Channels

Transcription

1 2n International Symposium on Computer, Communication, Control an Automation (3CA 2013) Preicting Television Ratings an Its Application to Taiwan Cable TV Channels Hui-Ling Huang Department of Biological Science an Technology, Institute of Bioinformatics an Systems Biology, Hua-Chin Lee Department of Biological Science an Technology, Institute of Bioinformatics an Systems Biology, Li-Sun Shu Department of Multimeia an Game Design, Overseas Chinese University Taichung, Taiwan Shih-Chung Lai Institute of Bioinformatics an Systems Biology, Tse-Ming Tsai Institute for Information Inustry Shih-Chun Chou Institute for Information Inustry Bo-fu Liu Institute for Information Inustry Hong-An Chen Institute of Bioinformatics an Systems Biology, Yun-Ju Yin Institute of Bioinformatics an Systems Biology, Shinn-Ying Ho* Department of Biological Science an Technology, Institute of Bioinformatics an Systems Biology, *Corresponing author Abstract Using forecast television networ ratings, television executives estimate a price to sell time to avertisers. TV rating is an important feebac mechanism because its results greatly affect the immense profits of TV companies, avertisers, an program proucers. Therefore, how to select the samples for TV rating investigation plays an important role in preicting program ratings. How to esign an accurate preicting moel for program rating also is an important investigation. The preicting problem is essentially a bi-objective optimization problem which minimizes the number of samples an maximizes the preicting accuracy of program rating. In this stuy, we propose an evolutionary approach to esigning a rating moel (ERM) by simultaneous optimization of sampling sub-area selection an parameter tuning using an intelligent genetic algorithm (IGA). In this stuy, the ERM is applie to Taiwan Cable TV Channels in Taipei an Taiwan. The experiments show that TV rating preiction of the propose ERM is efficient smaller than that of using the same number of sub-areas with the largest TV ratings an an optimal preiction program rating by using the selecte sub-areas. Keywors-component; formatting; TV rating; igital set-topbox; sampling metho; IGA; rating moel I. INTRODUCTION TV rating has long been seen as an important feebac mechanism. Its results greatly affect the immense profits of The authors - Publishe by Atlantis Press 189

2 TV companies, avertisers, an program proucers, an also etermine the length of the programs. In terms of suppliers, since the evelopment of harware in the auience rating inustry, such as telecommunication apparatuses an iniviual recoring machines, have become stabilize, the suppliers of such harware are lacing in bargaining power. Conversely, once the igital set-top-box becomes the viewing metho for the auience rating maret, its bargaining power will increase. Program rating with avertising impact has become a ey factor in the value of time. Developmental auience rating preiction is in ire nee. Review past research in auience appreciation an evelop a metric from the ratings ata that can be use as an early preictor of program growth or ecline [1]. Linear regression moels for the preiction of population rating have use by several authors [2]. Therefore, collecting the ataset for proucing accurate rating preictions is a challenging tas. One of the most challenging tass of collecting ata is fining target auience from big ata flow. The majority of performance ratings of the choosing sample size can still closely represent entirety program ratings. The existe stuies for sampling methos are escribe as below. Breiman et al. [3] use F-tests to etermine the optimum splitting points for ecision tree moels. Hartigan [4] first publishe CHAID ecision trees to obtain the optimum splitting points with chi-square. Ho et al. [5] propose intelligent evolutionary algorithms base on orthogonal experimental esigns for solving large parameter optimization problems. The intelligent genetic algorithm (IGA) is one customize version of the intelligent evolutionary algorithm for solving specific problems. We propose an evolutionary approach to esigning a rating moel (ERM) by simultaneous optimization of sampling sub-area selection an parameter tuning using IGA. In this stuy, the provie auience behavior ata incluing 140 million ata recors an 185 thousan peoples was collecte from Taiwan igital CATV system in January of The Taiwan region has 125 sub-areas an the Taipei region (the capital in Taiwan) has 29 sub-areas. In this stuy we apply Stylish Man-The Chef (11:00~13:00) program to the propose ERM for preiction from 2013/1/14~18 totally n=5 ay s ratio of channel viewing times. II. METHOD The ability of ERM arises mainly from simultaneous optimization of parameter setting of rating moel an sampling rating sub-areas selection using IGA. IGA aopts an efficient GA-chromosome encoing scheme. ERM may select a minimum number of sub-areas from statistics of the TV ataset an maximum close programing ratings. It is intractable to simultaneously optimize the two objectives. A. IGA for ERM ERM maes the best use of IGA in optimizing system parameters by esigning an efficient GA-chromosome representation as well as an intelligent crossover operation. The intelligent crossover operation is base on an orthogonal experimental esign using a ivie-an-conquer strategy to solve intractable optimization problems comprising lots of system parameters. Fitness function an GA-chromosome representation Fitness function is the only guie for IGA to optimize all system parameters encoe into a GA-chromosome. There are three objectives for esigning ERM using IGA. The first is to minimize an error value which respons to sampling ata how far away from the real channel viewing time s C a, the secon is to minimize the number N f of selecte subareas an the thir is minimize total error of preiction program rating of the ay P r. n C a = (1) R = 1 where R is the th ay of real channel viewing times, S is the th ay of sampling channel viewing times, n is number of ays, N is number of sub-areas, =1,.., N. n P r = (2) R M = 2 where R is the (+1) th ay of real ifferent ratio of channel viewing times, M is the (+1) th ay of ERM moel ratio of preiction of channel viewing times. The equations of R an M are shown as follows: 1 ( S ) R = (3) S 1 2 ( S ) M = α ( 100 %) + β (4) 1 S where α, β are the parameters of ERM which are encoing in GA-chromosome. If S represents the set of parameters to be evolve by IGA, the fitness function y(s) is as follows: Min y(s) = C a (S)+w 1 N f (S)+w 2 P r (S) (5) where w 1 is a positive weight etermine accoring to the preference of iniviual objectives. Generally, high program rating of the ay is the major objective an thus w 2 is set to a small constant value. Let S = {t i, α, β i=1,2,,l} encoe into a GAchromosome. The control GA-genes t i {0,1} are use to select effective sub-areas. α an β are the control parameters of ERM, an l is a prespecifie maximum number of selecte sub-areas. Intelligent genetic algorithm(iga) The use IGA for ERM to optimize the parameters in S using the fitness function y(s) is given as follows: Step 1: Initialization. Ranomly generate an initial population with N pop feasible iniviuals where each gene g i is unique in a GA-chromosome. Step 2: Evaluation. Evaluate fitness values of all iniviuals in the population. Let I best be the best iniviual in the population. Step 3: Use the simple truncation selection that replaces the worst P s N pop iniviuals with the best P s N pop iniviuals to form a new population, where P s is a selection probability. Step 4: Ranomly select P c N pop iniviuals 190

3 incluing I best, where P c is a crossover probability. Perform intelligent crossover operations for all selecte pairs of parents. Step 5: Apply a conventional bit-inverse mutation operator to the population using a mutation probability P m. To prevent the best fitness value from eteriorating, mutation is not applie to the best iniviual. Step 6: Termination test. If a prespecifie termination conition is satisfie, stop the algorithm. Otherwise, go to step 2. B. DATASET The ataset was collecte from Taiwan igital CATV system in January of 2013 an was supporte by the Institute for Information Inustry (III). In orer to further process the suitable an usable ataset, the wrong ata format an inconsistent ata was filtere out. In aition, the unsuitable TV program receiving time was also eliminate by checing if the time is less than 5 minutes or more than 6 hours. Finally, the raw ataset remains 14 imensions of information such as the smartcar i of Settop box (STB), user location, user behavior type an behavior happen time & en time. Besies, the Electronic Program Guie (EPG) of January 2013 aitionally provies the information of program i, program name, program start/en time an program type. TABLE I THE DESCRIPTIVE STATISTICS OF THE DATASET Region ata recor (unit: million) Subarea People (thousan) Taipei region: Taipei an new Taipei city THM region: Taoyuan-Hsinchu-Miaoli region TCN region: Taichung-Changhua-Nantou region, YCT region: Yunlin-Chiayi-Tainan region, KP region: Kaohsiung-Pingtung region, YHT region: Yilan-Hualien-Taitung region, Table 1 shows the escriptive statistics of the ataset in ifferent areas. There are 221 channels which inclue 37 entertainment channels in Taiwan igital TV system, an the 125 sub-areas contain 140 million ata recors from 185 thousan people. C. Preprocessing Step1: Filtering. This ataset contains 140 million ata recors an 185 thousan users. The first step is filtering the invali ata recor. (1) Filter that ata recor which the channel watching time is less than 30 secons. (2) Filter that user which watching ay is less than 3 ays in one month's time. After Filtering, the new ataset contains 67 million ata recors an 147 thousan users. Step2: Data ivie. This step is ivie the huge ataset (totally 125 sub-areas) into 6 regions, which responses to the aministrative area. The 6 areas are: (1) Taipei region (2) THM region (3) TCN region (4) YCT region (5) KP region (6) YHT region Step3: Data statistical analysis. This stuy analyzes the ata recors from January 14 th to January 18 th. (1) Choosing the comprehensive programs from totally 221 channels. (2) Summarize the viewing times for each comprehensive program channel. Channel selection. In Taiwan, most of TV Channels prefer the analysis of the business-orientate information about the application of TV Rating. Viewing time has resulte in affecting gigantic profits for TV companies, avertisers, an program proucers, with the ata offere. This stuy chooses Sanlih Entertainment Television City which is the highest viewing times channel to analyze an the Statistical chart is seen in Fig 1. By analyzing viewing times, it will represent the channel viewership. Viewing times (secon) Fig. 1 Viewing Times Top10 Channels in 2013/1/14~18 Time zones selection. Due to the high ratings will be able to create high-price avertising revenue, TV companies will arrange high-attractive program at high viewing times. However the in of program also affect the ifferent high viewing times. Program an time interacts viewership with each other. This stuy chooses Stylish Man- The Chef (11:00~13:00) program which is the highest viewing program to analyze an the Statistical chart is seen in Fig.2. D. Metho using ERM In this stuy we apply Stylish Man-The Chef (11:00~13:00) program to the propose ERM for preiction from 2013/1/14~18 totally n=5 ay s channel viewing times. 1) Majority region choosing. This stuy selects Taipei region which inclue Taipei the Capital city (l = 29) an whole Taiwan region (l = 125) these two ifferent ranges to be the sampling moels. 2) ERM: The parameter settings of IGA are N pop = 30, P c = 0.5, P s = 0.2 an P m = 0.2. Let the weighting w 1 = 1/l in the fitness function y(s). The stopping conition of IGA is to use 100 generations. Because of the non-eterministic characteristic of GA, R = 30 inepenent runs are performe. Fig.2 Sanlih Entertainment Television Viewing Times in 2013/1/14-1/18 191

4 III. RESULTS A. Training results by Taipei region ERM The program ranting ata from Taipei areas, there are 29 sub-areas ata to be create ERM for preicting the future viewing times ratio. The encoing length l =29, the objective function w 1 = 1/29, w 2 = 10, the IGA parameters are setting. The number of selecte sub-areas N f =10, the selecte subareas are ShihlinDist, Datong Dist, Beitou Dist, Songshan Dist, Sanjhih Dist, Sanchong Dist, Wugu Dist, Sheneng Dist, Sinian Dist an Sinjhuang Dist. The ERM parameters α an β are an , respectively. TABLE II. 1/14-18 REAL AND PREDICTING RATIO OF VIEWING TIMES. 2013/1/ /1/ /1/ /1/ /1/ growing preict growing preict error Figure 3. The growing rate between real an preict viewing times value. Appling the ERM to preicting ratio viewing time of ay, the objective function P r nees two ays before real ratio of viewing time. For example, accoring the ate of 2013/1/14~15 to preict the ratio of viewing times of the ate of 2013/1/16. Table 2 an Fig. 3 show the results of training ERM. The blue line is real viewing times ratio, an re line is the preicting ratio of viewing times in Fig. 3. B. Inepeneent test results by Taipei region ERM Using the wee ata from 2013/1/21~25 to be inepenent test ERM, the preicting ratio of viewing time is shown in Table 3 an Figure 4. The blue line is real ration of viewing times, an re line is the preicting ration of viewing times. The blue line is use to be a control group an compare with the re line to reflect error values. TABLE III. 1/21-25 REAL AND PREDICTING RATIO OF VIEWING TIMES. 2013/1/ /1/ /1/ /1/ /1/ grow preict growing preict error Figure 4. The growing rate between real an preict viewing times value. C. Training results by the whole Taiwan ERM The IGA parameters are setting as follows: the encoing length l =125, the objective function w 1 = 1/125, w 2 = 10. The preicting results of ERM in the whole Taiwan region are as follows: the number of selecte sub-areas N f is 45, the ERM parameters α an β are an , respectively. Appling the whole Taiwan region ERM to preicting ratio viewing time of ay, the objective function P r nees two ays before real ratio of viewing time. Table 4 an Fig. 5 show the results of training ERM. The blue line is real viewing times ratio, an re line is the preicting ratio of viewing times in Fig. 5. TABLE IV. 1/14-18 REAL AND PREDICTING RATIO OF VIEWING TIMES. 2013/1/ /1/ /1/ /1/ /1/ growing preict growing preict error Figure 5. The growing rate between real an preict viewing times value. D. Inepeneent test results by the whole Taiwan ERM Using the wee ata from 2013/1/21~25 to be inepenent test ERM, the preicting ratio of viewing time is shown in Table 5 an Figure 6. The blue line is real ration of viewing times, an re line is the preicting ration of viewing times. The blue line is use to be a control group an compare with the re line to reflect error values. TABLE V. 1/21-25 REAL AND PREDICTING RATIO OF VIEWING TIMES. 2013/1/ /1/ /1/ /1/ /1/ grow preict growing preict error

5 Ministry of Economy Affairs of the Republic of China. The authors woul lie to thans the member of the Innovative DigiTech-Enable Applications & Services Institute at the Institute for Information Inustry (III) New Meia User Lab, an their collaborators, whose contributions were central to this paper. Figure 6. The growing rate between real an preict viewing times value. IV. CONCULSION This paper has propose an efficient evolutionary preicting moel name ERM by the intelligent genetic algorithm (IGA). The automatic sub-area selection an parameter tuning embee in ERM are simultaneously optimal by IGA, which can avance the preicting moel performance from a large number of igital set-top-box ata. ERM can serve not only as a program rating preictor but also as an aaptive sampling extractor. ERM is evelope as an efficient tool of program rating preictor. REFERENCE [1] V. Beal, A. Tanusonjaja, M. Nenycz-Thiel. Behavioural Measures: Can they preict television program growth an ecline?. The Australian an New Zealan Mareting Acaemy Conference 2011 Perth, Australia [2] D. Meyer an R. J. Hynman. The Accuracy of Television Networ Rating Forecasts: The Effects of Data Aggregation an Alternative Moels. MASA, 1(3), , [3] L. Breiman, J. Frieman, R. Olshen an C. Stone. Classification an Regression Trees, Wasworth, [4] J.A. Hartigan, Clustering Algorithms,Wiley: NewYor, [5] S.-Y. Ho et al.. Intelligent evolutionary algorithms for large parameter optimization problems. IEEE Trans. Evol. Comput., 8, ,(2004b). V. ACKNOWLEDGMENT This stuy is conucte uner the "Digital Convergence Key Technology an System Develop Project" of the Institute for Information Inustry which is subsiize by the 193