Computer Science & Electrical
Received: 03 Jul 2018 , Published: 06 July 2018
Views: 83 , Download: 107
Profiling plays an important role in
personalized recommendation in research social networking
platforms and variety of big data sources. Dynamic changes
of user interests posted in social media and trillions of data
generated through big data sources lead to significant
challenges on comprehensive user profiling. Existing
approaches mostly rely on content-based analysis or itembased
rating mechanisms. Most of these approaches rely on
single sources and largely overlook the power of integration
of multiple data sources especially social media. In light of this
deficiency, this research proposes an integrated novel
approach to automatically capture dynamic user interests.
The proposed method facilitates automated learning of user
profiles based on the proposed approach which integrates
preprocessing and electronic symbol extraction model,
opinion-confidence mining hybrid model, feature extraction
model and profile data aggregation weighted k-means
clustering model and analysis of social data. According to our
experimental analysis the proposed method outperforms
existing benchmark methods.
 Chia-Chuan Hung, Yi-Ching Huang, Jane Yung-jen Hsu, David Kuan-
Chun Wu,Tag-Based User Profiling for Social Media Recommendation
 G. Araniti, P. D. Meo, A. Iera and D. Ursino (2003). Adaptive
controlling the QoS of multimedia wireless applications through user
profiling techniques, IEEE Journal on selected areas in communication,
21(10), pp. 1546-1556.
 T. Kuflik and P. Shoval (2000). Generation of user profiles for
information filtering-research agenda, Annual International ACM SIGIR
Conference on Research and Development in Information Retrieval, pp.
 M. J. Martin-Bautista, D. H. Kraft, M. A. Vila, J. Chen and J. Cruz
(2002). User profiles and fuzzy logic for web retrieval issues, Soft
Computing (Focus), 15(3-4), pp. 365-372.
 European Telecommunications Standards Institute (ETSI) (2005).
Human Factors (HF); User Profile Management, pp.1-100.
 S. Henczel (2004). Creating user profiles to improve information
quality, Factiva, 28(3), p. 30.
 C. Gena (2005). Methods and techniques for the evaluation of useradaptive
systems, The Knowledge Engineering, 20(1), pp.1-37.
 D. Poo, B. Chng and J. M. Goh (2003). A hybrid approach for user
profiling, Annual Hawaii International Conference onSystem Sciences,
4(4), pp. 1-9.
 D. Godoy and A. Amandi (2005). User profiling in personal
information agents: a survey, The Knowledge Engineering Review
Journal, 20(4), pp. 329-361.
 S. Steward and J. Davies (1997). User profiling techniques: a critical
review, British Computer Society, BCS-IRSG Annual Colloquium on IR
Research, pp. 1-22.
 Susan Gauch (2007). Ontology-Based User Profiles for Personalized
 Mohammed Abdellah Alimam,Yasyn Elyusufi, Hamid Seghiouer
(2017). Ontology-Based User Profiling for Personalized Acces to
Information within Collaborative Learning System.
 Anett Hoppe,Automatic ontology-based User Profile Learning from
heterogeneous Web Resources in a Big Data Context.
 Manoj Kumar, D.K. Yadav, Ankur Singh, Vijay Kr. Gupta, A Movie
Recommender System: MOVREC, International Journal of Computer
Applications (0975 – 8887).
 Bhumika Bhatt, Premal J Patel, Hetal Gaudani, A Review Paper on
Machine Learning Based Recommendation System.
 Purnima Bholowalia, Arvind Kumar,Review on determining number
of Cluster in K-Means Clustering, International Journal of Advance
Research in Computer Science and Management Studies