Interest Based Interactivity Through Cross Platform in Big Data
Keywords:
Cross platform, Data security, Big DataAbstract
Given the ubiquity of social media, interest-based interactivity as a main element to intensify user experience. Interest-based relevance modeling is taken out from user influence in multiple-platform social network Big Data container. The main goal of this work is to implement a platform for providing recommendation across different social network based on user interest. The streams consisted of tags from social media content through a discovery process and the application is tested on social media content streams to generate a Big Data scenario.
References
[1] AstaZelenkauskaite and Bruno Simoes, “Big data through cross-platform interest based interactivity”Big Data and Smart Computing, vol. 47, no. 2, pp. 191-196,2014.
[2] H. M. Inc. (2013) Social media management. [Online]. Available:
[3] C. J. Jacoby, “Understanding the limitations of keyword search,” Equivio, white paper, 2012.
[4] Z. D. Zhao, and M. S. Shang, “User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop,”In the third International Workshop on Knowledge Discovery and Data Mining, pp. 478-481, 2010.
[5] G.Adomavicius, and A. Tuzhilin, “Toward the Next Generation of Recommender Systems: A Survey of the State-of- the-Art and Possible Extensions,” IEEE Transactions on Knowledge and Data Engineering, Vol.17, No.6 pp. 734-749, 2005
[6] https://hootsuite.com/E. P. Bucy, “Interactivity in society: Locating an elusive concept,” The information society, vol. 20, no. 5, pp. 373–383, 2004.
[7] O. Foundation. (2013, Sep 13) Openid foundation website.[Online]. Available:http://openid.net/.
[8] S. J. McMillan, “A four-part model of cyber-interactivity some cyberplaces are more interactive than others,” New Media & Society, vol. 4, no. 2, pp. 271–291, 2002.
[9] E. J. Downes and S. J. McMillan, “Defining interactivity a qualitative identification of key dimensions,” New Media & Society, vol. 2, no. 2, pp. 157–179, 2000.
[10] P. Wilson, “Interdisciplinary research and information overload.” Library Trends, vol. 45, no. 2, pp. 192–203, 1996.
[11] Mohamed sarwat, Justin J. Levandoski , Ahmed Eldawy , and Mohamed F.Mokbel,” LARS*: An efficient and scalable Location –Aware Recommender system.
[12] Daniele Dell`Aglio, Irene Celino, and Dario Cerizza, “Anatomy of a Semantic Web- enabled Knowledge-based Recommender System
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
