Movie Recommendation Service Based on Preference Correlation Coefficient of Audience in Smart Environment

Songai Xuan, DoHyeun Kim


Recommendation system has been more and more popular recent years. It can help people make decisions easily, and is used in many popular applications include movies, music, news, books, research articles, search queries, social tags, and products in general. Smart homes also get enormous attention in the last decade, due to the important applications like health, energy and security. Different techniques and approaches have been devised by the researchers to make the smart home more efficient and effective. In this paper, we propose the movie recommendation service based on preference correlation coefficient of audience in smart environment, which will lead to the entertainment convenient in smart environment.


Smart Home; Movie Recommendation; Correlation Coefficient

Full Text:



Pieroni, Alessandra, et al. "Smarter City: Smart Energy Grid based on Blockchain Technology." International Journal on Advanced Science, Engineering and Information Technology 8.1 (2018).

S. Chenishkian, “Building Smart Services for Smart Home,†in Proceedings of the IEEE 4th International Workshop on Network Appliances, pp. 215-224, 2002.

J. H. Choi, D. K. Shin, and D. G. Shin, “Research and Implementation of the Context-Aware Middleware for Controlling Home Appliances,†IEEE Transactions on Consumer Electronics, Vol. 51, No. 1, pp. 301-306, 2005.

Y. Isoda, S. Kurakake, H. Nakano, “Ubiquitous Sensors based Human Behavior Modeling and Recognition using a patio-Temporal Representation of User States,†in proceedings of 18th International Conference on Advanced Information Networking and Applications (AINA ’04), pp. 512-517, 2004.

Kang, Byeongkwan, et al. "IoT-based monitoring system using tri-level context making model for smart home services." Consumer Electronics (ICCE), 2015 IEEE International Conference on. IEEE, 2015.

Sivaraman, Vijay, et al. "Network-level security and privacy control for smart-home IoT devices." Wireless and Mobile Computing, Networking and Communications (WiMob), 2015 IEEE 11th International Conference on. IEEE, 2015.

Santoso, Freddy K., and Nicholas CH Vun. "Securing IoT for smart home system." Consumer Electronics (ISCE), 2015 IEEE International Symposium on. IEEE, 2015.

Ling, Guang, Michael R. Lyu, and Irwin King. "Ratings meet reviews, a combined approach to recommend." Proceedings of the 8th ACM Conference on Recommender systems. ACM, 2014.

Szomszor, Martin, et al. "Folksonomies, the semantic web, and movie recommendation." (2007).

Lekakos, George, and Petros Caravelas. "A hybrid approach for movie recommendation." Multimedia tools and applications 36.1 (2008): 55-70.

Diao, Qiming, et al. "Jointly modeling aspects, ratings and sentiments for movie recommendation (jmars)." Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2014.

Jeff Sauro, James R Lewis: Quantifying the User Experience, 2nd Edition. (2016).

Lawrence, I., and Kuei Lin. "A concordance correlation coefficient to evaluate reproducibility." Biometrics (1989): 255-268.

Y. S. Oh, H. S. Yoon, and W. T. Woo, “Simulating Context-Aware Systems based on Personal Devices,†in Proceedings of the International Symposium on Ubiquitous VR (ISUVR-2006), pp. 49-52, 2006.

Bogers, Toine. "Movie recommendation using random walks over the contextual graph." Proc. of the 2nd Intl. Workshop on Context-Aware Recommender Systems. 2010.

Rashid Ahmad, DoHyeun kim, “Modeling of smart multimedia services provisioning in smart homeâ€.



  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development