SyntcRec: a Syntactic Recommender System Based on Improved Feature Selection Technique in Large Scholarly Data
In recent years, the amount of academic literature has been increasing, which results in information overloading. Another result of this fast data progress is that, one can feel lost in the variety of literature and realize the difficulties to yield decisions. To solve these issues, it is needed to filter, rank and competently deliver relevant material to several Internet operators. Recommendation schemes rectify this problem by providing users with personalized content and services. This paper presents a novel recommender system called syntactic Recommender, which employs improved feature selection techniques, a combination of ACO-GA (Ant Colony Optimization-Genetic Algorithm) algorithms aimed at feature selection in order to raise the accuracy of recommendations in recommender systems. The paper focuses on two key concepts, the first is performing and giving more exact object predictions to the operator and the second is handling a huge volume of data. The aim of this paper is to offer recommendation outcomes based on the users requests with accuracy and competency. The proposed ACO-GA feature selection algorithm and the Cosine Similarity technique increase accuracy of the Recommender system up to 92%.
Copyright © 2017 Praise Worthy Prize - All rights reserved.
S. Khan, K. A. Shakil, and M. Alam, Cloud-Based Big Data Management and Analytics for Scholarly Resources: Current Trends, Challenges and Scope for Future Research, 2016.
F.O. Isinkaye , Y.O. Folajimi , B.A. Ojokoh, Recommendation systems: Principles, methods and Evaluation, Egyptian Informatics Journal, Vol. 16, n. 3, pp. 261–273,2015.
J. D. West, I Wesley-Smith, and C. T. Bergstrom, A Recommendation System Based on Hierarchical Clustering of an Article-Level Citation Network, IEEE Transactions On Big Data, Vol. 2, n. 2, pp. 113-123, 2016.
P. Bedi, R. Sharma, H. Kaur, Recommender System Based on Collaborative Behaviour of Ants, Journal of Artificial Intelligence, Vol. 2, n. 2, pp. 40-55, 2009.
A. Hossein N. Rafsanjani, N. Salim, A. Rezaei Aghdam, Karamollah Bagheri Fard , Recommendation Systems: a review , International Journal of Computational Engineering Research, Vol. 3, n. 5, pp. 47-52, 2013.
J. Ben Schafer, Joseph Konstan, John Riedl, On recommender Systems in E-Commerce, Proc. of the 1st ACM conference on Electronic commerce, New York, 1998, pp. 158-166.
L. S. Chen, F. H. Hsu, M. C. Chen, Y. C. Hsu, Developing recommender systems with the consideration of product profitability for sellers, Information Sciences, Vol. 178, n. 4 , pp. 1032–1048, 2008.
R. J. Mooney, L. Roy, Content Based Book Recommending Using Learning for Text Categorization, Proc. of the Fifth ACM Conference on Digital Libraries, San Antonio, 2000, pp. 195-240.
L. Yao, Q. Z. Sheng, A. H.H. Ngu, J. Yu, And A. Segev, Unified Collaborative And Content-Based Web Service Recommendation, IEEE Transactions On Services Computing, Vol. 8, n. 3, pp. 453-466, 2015.
R. Prasad and V. V. Kumari, A Categorical Review Of Recommender Systems, International Journal of Distributed and Parallel Systems (IJDPS), Vol. 3, n. 5, pp. 73-83, 2012.
S. Ujjin and P. J. Bentley, Particle Swarm Optimization Recommender System, University College London, Department of Computer Science, 2003.
Z. Huang, W. Chung, and H. Chen, A Graph Model for E-Commerce Recommender Systems, Journal Of The American Society For Information Science And Technology, Vol. 55, n. 3, pp. 259-274, 2004.
A. Al-Ani, Ant Colony Optimization for Feature Subset Selection, International Journal of Computer, Electrical, Automation, Control and Information Engineering, Vol. 1, n. 4, pp. 999-1002, 2007.
J. Sobecki, J. M. Tomczak, On Student Courses Recommendation Using Ant Colony Optimization, Proc. 2nd International Conference on intelligent Information and Database System, Springer-Verlag, Berlin, 2010, pp. 124-133.
D. H. Park, H. K. Kim, Il Y. Choi, J. K. Kim, A literature review and classification of recommender systems research, Expert Systems with Applications, Vol. 39, n. 11, pp. 10059-10072, 2012
M. Salehi, A. Fathi, F. Abdali- Mohammadi, ANTSREC: A Semantic Recommender System Based on Ant Colony Meta-Heuristic in Electronic Commerce, International Journal of Advanced Science and Technology, Vol. 56, pp. 119-130, 2013.
M. Sajwan, K. Acharya, Sanjay Bhargava, Swarm Intelligence Based Optimization for Web Usage Mining, International Journal of Computer Applications Technology and Research, Vol. 3, n. 2, pp. 119 - 124, 2014.
A. Bellaachia, D. Alathel, Improving the Recommendation Accuracy for Cold Start Users in Trust-Based Recommender Systems, International Journal of Computer and Communication Engineering, Vol. 5, n. 3, pp. 206-214, 2016.
G. Pole, P. Gera, On A Recent Study of Emerging Tools and Technologies Boosting Big Data Analytic,. Proc. Innovations in Computer Science and Engineering, Advances in Intelligent Systems and Computing 413, © Springer Science+Business Media Singapore , 2016, pp. 29-36.
Kumar, A., A Collaborative Web Recommendation System Based on Fuzzy Association Rule Mining Techniques, (2014) International Journal on Communications Antenna and Propagation (IRECAP), 4 (6), pp. 229-233.
Alphy, A., Prabakaran, S., A Two-Phase Dynamic Recommender System for Improved Web Usage Mining and Personalization, (2015) International Review on Computers and Software (IRECOS), 10 (12), pp. 1244-1254.
M. J. Pazzani and D. Billsus, Content-Based Recommendation Systems, Lecture Notes in Computer Science, LNCS, Volume 4321, (Berlin, Springer-Verlag, 2007, 325-341).
P. Lops, M. de Gemmis and G. Semeraro, Content-based Recommender Systems: State of the Art and Trends, Recommender Systems Handbook, (Italy, Springer Science, 2011, 73-105).
V. Diviya Prabha, R. Rathipriya, On A Study on Swarm Intelligence Techniques in Recommender System, Proc. published in International Journal of Computer Applications® (IJCA) ,2013, pp. 0975 – 8887.
Vorgelegt von, Evaluating the Accuracy and Utility of Recommender Systems, Master of Science, Dissertation, Technische Universitat , Berlin, 2013.
S. Kashef, H. Nezamabadi-pour, An advanced ACO algorithm for feature subset selection, Neurocomputing, Vol. 147, pp. 271-279, 2014.
Aruchamy, S., Vijayakumar, P., Senthilkumar, A., Design of Ant Colony Optimized Shunt Active Power Filter for Load Compensation, (2014) International Review of Electrical Engineering (IREE), 9 (4), pp. 725-734.
Vasundara, M., Padmanaban, K., Ramachandran, T., Saravanan, M., Prediction of Machining Fixture Layout through FEM and ANN and Comparison of Optimal Fixture Layouts of GA and ACA, (2014) International Review of Mechanical Engineering (IREME), 8 (3), pp. 537-546.
Ibrahim, H., Mahmoud, A., DC Motor Control Using PID Controller Based on Improved Ant Colony Algorithm, (2014) International Review of Automatic Control (IREACO), 7 (1), pp. 1-6.
Hannane, A., Fizazi, H., Metaheuristics and Neural Network for Satellite Images Classification, (2016) International Review of Aerospace Engineering (IREASE), 9 (4), pp. 107-113.
Saraereh, O., Al Saraira, A., Alsafasfeh, Q., Arfoa, A., Bio-Inspired Algorithms Applied on Microstrip Patch Antennas: a Review, (2016) International Journal on Communications Antenna and Propagation (IRECAP), 6 (6), pp. 336-347.
Zongo, O., Oonsivilai, A., Comparison between Harmony Search Algorithm, Genetic Algorithm and Particle Swarm Optimization in Economic Power Dispatch, (2015) International Review of Electrical Engineering (IREE), 10 (2), pp. 286-292.
Gosalbez, I., Lehtonen, M., Stochastic Genetic Algorithm and its Application as a Demand Control Tool for Houses with Thermal Energy Storage Systems, (2015) International Review on Modelling and Simulations (IREMOS), 8 (3), pp. 284-292.
Abdulghani, M., Tiun, S., An Optimized Feature Set Based on Genetic Algorithm for Business Web Pages Named Entity Recognition, (2016) International Review of Automatic Control (IREACO), 9 (5), pp. 298-303.
Kassem, A., El-Bayoumi, G., Habib, T., Kamalaldin, K., Improving Satellite Orbit Estimation Using Commercial Cameras, (2015) International Review of Aerospace Engineering (IREASE), 8 (5), pp. 174-178.
- There are currently no refbacks.
Please send any question about this web site to firstname.lastname@example.org
Copyright © 2005-2020 Praise Worthy Prize