基于收视行为的互联网电视节目流行度预测模型

朱琛刚; 程光

doi:10.11999/JEIT161310

基于收视行为的互联网电视节目流行度预测模型

doi: 10.11999/JEIT161310

朱琛刚¹,
程光¹

基金项目:

国家计划项目(863)(2015AA015603)，江苏省未来网络创新研究院未来网络前瞻性研究项目(BY2013095-5-03)，江苏省六大人才高峰高层次人才项目(2011-DZ024)

计量
- 文章访问数: 1411
- HTML全文浏览量: 161
- PDF下载量: 251
- 被引次数: 0
出版历程
- 收稿日期: 2016-12-08
- 修回日期: 2017-06-15
- 刊出日期: 2017-10-19

Program Popularity Prediction Model of Internet TV Based on Viewing Behavior

ZHU Chengang¹,
CHENG Guang¹

Funds:

The National 863 Program of China (2015AA 015603), The Prospective Research Program on Future Networks of Jiangsu Province (BY2013095-5-03), The Six Industries Talent Peaks Plan of Jiangsu Province (2011-DZ024)

摘要

摘要: 准确预测节目流行度是互联网电视节目系统设计与优化所要解决的关键问题之一。针对现有预测方法存在模型训练时间长、样本数量多、且对突发热点节目流行度预测效果差等问题，该文测量了某互联网电视平台280万用户的60亿条收视行为数据，采用行为动力学分类方法将节目流行度演化过程分为内源临界、内源亚临界、外源临界和外源亚临界4种类型，运用双种群粒子优化的最小二乘支持向量机对每种类型分别构建了一种互联网电视节目流行度预测模型BD3P，并将BD3P模型应用于实际数据测验。实验结果表明，与现有其他方法相比，BD3P模型预测精度可提升17%以上，并能有效缩短预测周期。
- 互联网电视 /
- 流行度预测 /
- 行为动力学 /
- 最小二乘支持向量机 /
- 双种群粒子群优化
Abstract: Predicting program popularity is a key issue for design and optimization of Internet TV system. Existing prediction methods usually need large quantity of samples and long training time, while the prediction accuracy is poor for the burst hot programs. This paper introduces an Internet TV Program Popularity Prediction model based on viewing Behavioral Dynamics features (BD3P). 6 billion view behavior records from 2.8 million subscribers of a certain Internet TV platform are measured, and the evolution process of program popularity is divided into 4 types based on behavioral dynamics features, which is endogenous, internal subcritical, exogenous and exogenous subcritical. The prediction models of Internet TV program popularity are constructed for each type using Least Squares Support Vector Machines (LSSVM) with double population Particle Swarm Optimization (PSO), and these models are applied to the actual data test. The experimental results show that, compared to the existing prediction model, the prediction accuracy can be increased by more than 17%, and the forecast period can be effectively shortened.
- Internet TV /
- Popularity prediction /
- Behavioral Dynamics (BD) /
- Least Squares Support Vector Machines (LSSVM) /
- Double population Particle Swarm Optimization (PSO)

HTML全文

参考文献(15)

朱轶, 糜正琨, 王文鼐. 一种基于内容流行度的内容中心网络缓存概率置换策略[J]. 电子与信息学报, 2013, 35(6): 1305-1310. doi: 10.3724/SP.J.1146.2012.01143.

ZHU Yi, MI Zhengkun, and WANG Wennai. A cache probability replacement policy based on content popularity in content centric networks[J]. Journal of Electronics Information Technology, 2013, 35(6): 1305-1310. doi: 10.3724 /SP.J.1146.2012.01143.

芮兰兰, 彭昊, 黄豪球, 等. 基于内容流行度和节点中心度匹配的信息中心网络缓存策略[J]. 电子与信息学报, 2016, 38(2): 325-331. doi: 10.11999/JEIT150626.

RUI Lanlan, PENG Hao, HUANG Haoqiu, et al. Popularity and centrality based selective caching scheme for information- centric networks[J]. Journal of Electronics Information Technology, 2016, 38(2): 325-331. doi: 10.11999/JEIT150626.

GMEZ V, KALTENBRUNNER A, and LPEZ V. Statistical analysis of the social network and discussion threads in slashdot[C]. ACM International Conference on World Wide Web, Beijing, China, 2008: 645-654. doi: 10.1145 /1367497.1367585.

SZABO G and HUBERMAN B A. Predicting the popularity of online content[J]. Communications of the ACM, 2010, 53(8): 80-88. doi: 10.1145/1787234.1787254.

CASTILLO C, ELHADDAD M, PFEFFER J, et al. Characterizing the life cycle of online news stories using social media reactions[C]. ACM International Conference on Computer Supported Cooperative Work Social Computing, Baltimore, MD, USA, 2014: 211-223. doi: 10.1145/2531602. 2531623.

PINTO H, ALMEIDA J M, and GONALVES M A. Using early view patterns to predict the popularity of YouTube videos[C]. ACM International Conference on Web Search and Data Mining, Rome, Italy, 2013: 365-374. doi: 10.1145/ 2433396.2433443.

GAO S, MA J, and CHEN Z. Modeling and predicting retweeting dynamics on microblogging platforms[C]. ACM International Conference on Web Search and Data Mining, Shanghai, China, 2015: 107-116. doi: 10.1145/2684822. 2685303.

CRANE R and SORNETTE D. Robust dynamic classes revealed by measuring the response function of a social system[J]. Proceedings of the National Academy of Sciences of the United States of America, 2008, 105(41): 15649-15653. doi: 10.1073/pnas.0803685105.

WU B, MEI T, CHENG W H, et al. Unfolding temporal dynamics: Predicting social media popularity using multi-scale temporal decomposition[C]. Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA, 2016: 32-38. doi: 10.13140/RG.2.2.27504.66565.

WU J, ZHOU Y, CHIU D M, et al. Modeling dynamics of online video popularity[C]. IEEE International Symposium on Quality of Service, Portland, OR, USA, 2015: 141-146. doi: 10.1109/IWQoS.2015.7404724.

FONTANINI G, BERTINI M, and DEL BIMBO A. Web video popularity prediction using sentiment and content visual features[C]. ACM International Conference on Multimedia Retrieval, New York, NY, USA, 2016: 289-292. doi: 10.1145/2911996.2912053.

ZAMAN T, FOX E B, and BRADLOW E T. A Bayesian approach for predicting the popularity of tweets[J]. The Annals of Applied Statistics, 2014, 8(3): 1583-1611. doi: 10.1214/14-AOAS741.

WANG J, ZHANG Z, and ZHANG W. Support vector machine based on double-population particle swarm optimization[J]. Journal of Convergence Information Technology, 2013, 8(8): 33-43. doi: 10.4156/jcit.vol8.issue8. 106.

施引文献

资源附件(0)

访问统计