More Web Proxy on the site http://driver.im/

research-article

Video on demand recommender system for internet protocol television service based on explicit information fusion

Authors:

Young-Gab KimAuthors Info & Claims

Volume 143, Issue C

https://doi.org/10.1016/j.eswa.2019.113045

Published: 01 April 2020 Publication History

Highlights

•

Internet protocol television recommendation based on information fusion is proposed.

•

Over-the-top data are added to solve data sparsity problem.

•

Actual internet protocol television data are used for evaluation.

•

An explicit information fusion approach can improve accuracy of recommendation.

Abstract

Internet protocol television (IPTV) provides video on demand (VOD), internet service, and real-time broadcasting to users as a service that combines broadcasting and communication technology. Among various services, the sales of VOD are profitable because VODs offer relatively strong direct revenue models in IPTV services. However, the development of a VOD recommender system for IPTV service is highly challenging owing to the lack of explicit preference information of users in an IPTV environment. Previous studies for IPTV VOD recommender systems have attempted to solve the data sparsity problem through implicit preference information; however, it is better to utilize explicit preference information to improve the performance of system. Recently, IPTV service providers have provided their own over-the-top (OTT) services such that explicit preference information of users for items can be combined. Therefore, we proposed a novel information fusion method for an IPTV VOD recommender system that integrates the explicit information of both IPTV and OTT services. In addition, we utilized the probabilistic matrix factorization, that guarantees high performance in most recommender systems, as a recommender algorithm in this study. Finally, we conducted comparative evaluations based on various metrics and validated that the information fusion of IPTV and OTT services contribute to the IPTV VOD recommender system.

References

[1]

G. Adomavicius, A. Tuzhilin, Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions, IEEE Transactions on Knowledge & Data Engineering (TKDE) 17 (6) (2005) 734–749.

[2]

S. Alonso, J. Bobadilla, F. Ortega, R. Moya, Robust model-based reliability approach to tackle shilling attacks in collaborative filtering recommender systems, IEEE Access 7 (2019) 41782–41798.

[3]

R. Bambini, P. Cremonesi, R. Turrin, A recommender system for an IPTV service provider: A real large-scale production environment, Recommender systems handbook, Springer, Boston, MA, 2011, pp. 299–331.

[4]

R. Bhatt, Vote stuffing control in IPTV-based recommender systems, in: Proceedings of the First International Conference on Intelligent Human Computer Interaction, Springer, New Delhi, 2009, pp. 176–183.

[5]

J. Bobadilla, F. Ortega, A. Hernando, A. Gutiérrez, Recommender systems survey, Knowledge-Based Systems 46 (2013) 109–132.

Digital Library

[6]

R. Burke, Hybrid recommender systems: Survey and experiments, User Modeling and User-Adapted Interaction 12 (4) (2002) 331–370.

Digital Library

[7]

K.H. Chang, J. Hightower, B. Kveton, Inferring identity using accelerometers in television remote controls, in: International Conference on Pervasive Computing, Springer, Berlin, Heidelberg, 2009, pp. 151–167.

[8]

P. Cremonesi, R. Turrin, Time-evolution of IPTV recommender systems, in: Proceedings of the 8th European Conference on Interactive TV and Video, ACM, 2010, pp. 105–114.

[9]

P. Cremonesi, R. Turrin, F. Airoldi, Hybrid algorithms for recommending new items, in: Proceedings of the 2nd international workshop on information heterogeneity and fusion in recommender systems, ACM, 2011, pp. 33–40.

[10]

T. Dwyer, Y. Shim, H. Lee, J. Hutchinson, Comparing digital media industries in South Korea and Australia: The case of netflix take-up, International Journal of Communication 12 (2018) 20.

[11]

R. Forsati, M. Mahdavi, M. Shamsfard, M. Sarwat, Matrix factorization with explicit trust and distrust side information for improved social recommendation, ACM Transactions on Information Systems (TOIS) 32 (4) (2014) 17.

[12]

E. Inan, F. Tekbacak, C. Ozturk, Moreopt: A goal programming based movie recommender system, Journal of Computational Science 28 (2018) 43–50.

[13]

J. Jang, D. Zhao, W. Hong, Y. Park, M.Y. Yi, Uncovering the underlying factors of smart TV UX over time: A multi-study, mixed-method approach, in: Proceedings of the ACM International Conference on Interactive Experiences for TV and Online Video, ACM, 2016, pp. 3–12.

[14]

Y. Jin, G. Junhua, Z. Suqi, Z. Jian, Spark-based distributed multi-features hybrid iptv viewing implicit feedback scoring model, Procedia computer science 111 (2017) 441–447.

[15]

D. Kim, Age of media convergence, in: Media Governance in Korea 1980–2017, Palgrave Macmillan, Cham, 2018, pp. 111–131.

[16]

J. Kim, S. Kang, An ontology-based personalized target advertisement system on interactive TV, Multimedia tools and applications 64 (3) (2013) 517–534.

[17]

J. Kim, E. Kwon, Y. Cho, S. Kang, Recommendation system of iptv tv program using ontology and k-means clustering, in: International Conference on Ubiquitous Computing and Multimedia Applications, Springer, Berlin, Heidelberg, 2011, pp. 123–128.

[18]

M.W. Kim, E.J. Kim, W.M. Song, S.Y. Song, A.R. Khil, Efficient recommendation for smart TV contents, in: International Conference on Big Data Analytics, Springer, Berlin, Heidelberg, 2012, pp. 158–167.

[19]

M.W. Kim, W.M. Song, S.Y. Song, E.J. Kim, Efficient collaborative recommendation with users clustered for IPTV services, in: International Conference on Hybrid Information Technology, Springer, Berlin, Heidelberg, 2012, pp. 409–416.

[20]

H.G. Ko, E. Kim, I.Y. Ko, D. Chang, Semantically-based recommendation by using semantic clusters of users' viewing history, in: 2014 international conference on big data and smart computing (BIGCOMP), IEEE, 2014, pp. 83–87.

[21]

I. Konstas, V. Stathopoulos, J.M. Jose, On social networks and collaborative recommendation, in: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, ACM, 2009, pp. 195–202.

[22]

Y. Koren, Factorization meets the neighborhood: A multifaceted collaborative filtering model, in: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 2008, pp. 426–434.

[23]

Y. Koren, R. Bell, C. Volinsky, Matrix factorization techniques for recommender systems, Computer 8 (2009) 30–37.

[24]

Lee, J., Sun, M., & Lebanon, G. (2012). A comparative study of collaborative filtering algorithms. arXiv:1205.3193.

[25]

X. Luo, M. Zhou, Y. Xia, Q. Zhu, An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems, IEEE Transactions on Industrial Informatics 10 (2) (2014) 1273–1284.

[26]

H. Ma, An experimental study on implicit social recommendation, in: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, ACM, 2013, pp. 73–82.

[27]

A. Mnih, R.R. Salakhutdinov, Probabilistic matrix factorization, in: Advances in neural information processing systems, 2008, pp. 1257–1264.

[28]

R. Oliveira, T. Silva, J.F. de Abreu, M. Almeida, Automatic identification in accessible iTV services: Proposal of an IPTV interface, in: Proceedings of the 7th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion, ACM, 2016, pp. 384–391.

[29]

E.A. Park, Business strategies of Korean TV players in the age of Over-The-Top (OTT) video service, International Journal of Communication 12 (2018) 22.

[30]

H. Park, K. Han, J. Yang, J.K. Choi, Enhanced metadata creation and utilization for personalized iptv service, in: International Conference on Information Science and Applications, Springer, Singapore, 2017, pp. 763–769.

[31]

K. Park, J. Choi, D. Lee, IPTV-VOD program recommendation system using single-scaled hybrid filtering, in: Proceedings of the 10th WSEAS international conference on Signal processing, computational geometry and artificial vision, ACM, 2010, pp. 128–133.

[32]

Y. Park, J. Oh, H. Yu, RecTime: Real-time recommender system for online broadcasting, Information Sciences 409 (2017) 1–16.

[33]

B. Sarwar, G. Karypis, J. Konstan, J. Riedl, Application of dimensionality reduction in recommender system-a case study (No. TR-00-043), Minnesota Univ Minneapolis Dept of Computer Science, Minneapolis, 2000.

[34]

Y.D. Seo, Y.G. Kim, E. Lee, D.K. Baik, Personalized recommender system based on friendship strength in social network services, Expert Systems with Applications 69 (2017) 135–148.

[35]

J. Song, T. Jang, S.Y. Sohn, Conjoint analysis for IPTV service, Expert Systems with Applications 36 (4) (2009) 7860–7864.

[36]

H. Sumiyoshi, M. Sano, J. Goto, T. Mochizuki, M. Miyazaki, M. Fujii, et al., CurioView: TV recommendations related to content being viewed, in: 2010 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), IEEE, 2010, pp. 1–6.

[37]

F. Ullah, G. Sarwar, S. Lee, Social network and device aware personalized content recommendation, Procedia Technology 17 (2014) 528–533.

[38]

J. Vanattenhoven, D. Geerts, Designing TV recommender interfaces for specific viewing experiences, in: Proceedings of the ACM International Conference on Interactive Experiences for TV and Online Video, ACM, 2015, pp. 185–190.

[39]

P. Wang, X. Zuo, C. Guo, R. Li, X. Zhao, C. Luo, A multiobjective genetic algorithm based hybrid recommendation approach, in: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2017, pp. 1–6.

[40]

J. Xiao, L. He, An expandable recommendation system on IPTV, in: International Conference in Swarm Intelligence, Springer, Berlin, Heidelberg, 2012, pp. 33–40.

[41]

Y. Xin, H. Steck, Multi-value probabilistic matrix factorization for IPTV recommendations, in: Proceedings of the fifth ACM conference on Recommender systems, ACM, 2011, pp. 221–228.

[42]

Y. Yang, Q. Hu, L. He, M. Ni, Z. Wang, Adaptive temporal model for IPTV recommendation, in: International Conference on Web-Age Information Management, Springer, Cham, 2015, pp. 260–271.

[43]

Y. Yang, C. Liu, C. Li, Y. Hu, Y. Niu, L. Li, The recommendation systems for smart tv, in: Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT), IEEE, 2014, July, pp. 1–6.

[44]

R. Zhang, Y. Deng, L. Shi, User research and design for live TV UX in China, in: Adjunct Publication of the 2017 ACM International Conference on Interactive Experiences for TV and Online Video, ACM, 2017, pp. 9–14.

[45]

D. Zibriczky, Z. Petres, M. Waszlavik, D. Tikk, EPG content recommendation in large scale: A case study on interactive TV platform, in: 2013 12th International Conference on Machine Learning and Applications, Vol. 2, IEEE, 2013, pp. 315–320.

Cited By

Yin FJi MLi SWang Y(2022)Neural TV program recommendation with heterogeneous attentionKnowledge and Information Systems10.1007/s10115-022-01695-464:7(1759-1779)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1007/s10115-022-01695-4

Index Terms

Video on demand recommender system for internet protocol television service based on explicit information fusion
1. Computing methodologies
2. Information systems

Index terms have been assigned to the content through auto-classification.

Recommendations

A Hybrid Multigroup Coclustering Recommendation Framework Based on Information Fusion
Special Section on Visual Understanding with RGB-D Sensors

Collaborative Filtering (CF) is one of the most successful algorithms in recommender systems. However, it suffers from data sparsity and scalability problems. Although many clustering techniques have been incorporated to alleviate these two problems, ...
Online recommender system for radio station hosting based on information fusion and adaptive tag-aware profiling

A new implicit feedback recommender system for the interactive radio network FMhost.A collaborative approach paired with dynamic tag-aware profiles or users and radios.An adaptive online learning strategy based on user history and information fusion.We ...
Interactive television: new genres, new format, new content
IE '05: Proceedings of the second Australasian conference on Interactive entertainment

The aim of this paper is to discuss some of the main issues associated with interactive genres, formats and content in the context of interactive television (ITV). First, a set of new forms or categorizations of ITV will be presented. Second, the suite ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 143, Issue C

Apr 2020

425 pages

ISSN:0957-4174

Issue’s Table of Contents

Copyright © 2019.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 April 2020

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 06 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Yin FJi MLi SWang Y(2022)Neural TV program recommendation with heterogeneous attentionKnowledge and Information Systems10.1007/s10115-022-01695-464:7(1759-1779)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1007/s10115-022-01695-4

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents