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Reseach Article

TV Program Popularity Prediction using Time Base

by Tatineni Anusha, Garimella Bharathi, Tatineni Poojitha
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 178 - Number 26
Year of Publication: 2019
Authors: Tatineni Anusha, Garimella Bharathi, Tatineni Poojitha
10.5120/ijca2019919086

Tatineni Anusha, Garimella Bharathi, Tatineni Poojitha . TV Program Popularity Prediction using Time Base. International Journal of Computer Applications. 178, 26 ( Jun 2019), 21-24. DOI=10.5120/ijca2019919086

@article{ 10.5120/ijca2019919086,
author = { Tatineni Anusha, Garimella Bharathi, Tatineni Poojitha },
title = { TV Program Popularity Prediction using Time Base },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2019 },
volume = { 178 },
number = { 26 },
month = { Jun },
year = { 2019 },
issn = { 0975-8887 },
pages = { 21-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume178/number26/30699-2019919086/ },
doi = { 10.5120/ijca2019919086 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:51:30.490756+05:30
%A Tatineni Anusha
%A Garimella Bharathi
%A Tatineni Poojitha
%T TV Program Popularity Prediction using Time Base
%J International Journal of Computer Applications
%@ 0975-8887
%V 178
%N 26
%P 21-24
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The main goal is to the provide better recommendations system to TV viewers here the predictions are based on VOD streaming .and recommender systems have great achievements in the fields of VOD, (VIDEO ON DEMAND) .We have provide an effective recommended programs. Based on trending data .the rank wiil change based on time series.so that timely prediction of program popularity is of great value for content providers ,advertisers, and broadcast TV operators This information can be beneficial for operators in TV program purchasing decisions and can help advertisers formulate reasonable advertisement investment plans. Prediction models have been proposed based on video-on-demand (VOD) K spectral clustering and Kmedoids with Dynamic time warping are used for popularity prediction .and . file creation with multi grained is used for optimizing the streaming time. Cache replacement strategy is used to free up memory constraints. So it can speed up the streaming data.

References
  1. P.J. Danaher and T.S. Dagger, “Using a NestedLogit Model to Forecast Television Ratings,” Int’l J. Forecasting, vol. 28, no. 3, 2012, pp. 607–622.
  2. P.J. Danaher, T.S. Dagger, and M.S. Smith,“Forecasting Television Ratings,” Int’l J. of Forecasting, vol. 27, no. 4, 2011, pp.1215–1240.
  3. H. Yin et al., “A Temporal Context-AwareModel for User Behavior Modeling In Social Media Systems,” Proc. 2014 ACM SIGMOD Int’l Conf . Management of Data (SIGMOD), 2014, pp. 1543–1554.
  4. Ferraz Costa et al., “RSC: Mining and Modeling Temporal Activity in Social Media,” Proc. 21th ACM SIGKDD Int’l Conf. Knowledge Discovery and DataMining (SIGKDD), 2015, pp. 269–278.
  5. R. Hinami and S. Satoh, “Audience Behavior Mining by Integrating TV Ratings with Multimedia Contents,” IEEE Int’l Symp. Multimedia (ISM), 2016, pp. 44–51.
  6. U. Sedlar, M. Volk, J. Sterle, A. Kos, and R.Sernec, “Contextualized monitoring and root cause discovery in IPTV systems using datavisualization,” IEEENetwork, vol. 26, no. 6, pp. 40–46, 2012.
  7. A. Tatar, P. Antoniadis, M. D. de Amorim, and S. Fdida, ‘‘From popularity prediction to ranking online news,’’ Social Netw. Anal. Mining, vol. 4,pp. 174–183, Dec. 2014.
  8. H. Pinto, J. M. Almeida, and M. A. Gonç alves,̧‘‘Using early view patterns to predict the popularity of youtube videos,’’ in Proc. 6th ACM Int. Conf.Web Search Data Mining, 2013, pp. 365–374.
  9. A. Kaltenbrunner, V. Gómez, and V. López,‘‘Description and prediction of slashdot activity,’’ inProc. Latin Amer. Web Conf. (LA-WEB), 2007,pp. 57– 66.
Index Terms

Computer Science
Information Sciences

Keywords

TV programs time context recommender systems trending audience predictionwatch it regardless to the fact that a more interesting program could be scheduled on a different channel or at a different time slot. A user cannot watch different TV channels simultaneously In non linear TV common recommender systems programs are always accessible and a user can absorbs more content at the simultaneous time in linear TV a users can look only one program at the particular time and the programs are left are not be convey in upcoming . consequently we recognize the viewing habits a recommender system should indentify that some of the TV programs are common because scheduled simultaneously. Television Audience Ratings Television crowd evaluations (TV appraisals) are the major marker in the concept of TV broadcasting they're utilized to survey the prevalence of TV programs. program's TV rating shows the level of all TV families checked out that program. Television evaluations are a standard measure use