An Effective Algorithm in a Recommender System Based on a Combination of Imperialist Competitive and Firey Algorithms


1 Islamic Azad University, Damghan Branch, Damghan, Iran

2 School of Engineering, Damghan University, Damghan, Iran


With the rapid expansion of the information on the Internet, recommender systems play an important role in terms of trade and research. Recommender systems try to guess the user's way of thinking, using the in-formation of user's behavior or similar users and their views, to discover and then propose a product which is the most appropriate and closest product of user's interest. In the past decades, many studies have been done in the field of recommender systems, most of which have focused on designing new recommender algorithms based on computational intelligence algorithms. The success of a recommender system besides the quality of the algorithm depends on other factors such as: Sparsity, Cold start and Scalability in the performance of a recommender system, which can affect the quality of the recommendation. Consequently, the main motivation for this research is to providing an effective meta heuristic algorithm based on a combination of imperialist competitive and firefly algorithms using clustering technique. The simulation results of the proposed algorithm on real data sets Move Lens and Film Trust have shown better forecast accuracy in the item recommendation to users than other algorithms presented in subject literature. Also the proposed algorithm can choose appropriate items among the wide range of data and give it to output in a reasonable time.


1. N. L. M. Shuib, N. Baiti, A. B. Normadhi, L. F. H. B. M. Alias, and N. S. Binti, Collaborative recommender system: A review," in International Conference On Advances In Computing, Electronics, And Electrical Technology, 2015.

2. T.-P. Liang, H.-J. Lai, and Y.-C. Ku, Personalized content recommendation and user satisfaction: Theoretical synthesis and empirical findings," Journal of Management Information Systems, vol. 23, no. 3, pp. 45-70, 2006.

3. J. Lu, D. Wu, M. Mao, W. Wang, and G. Zhang, Recommender system application developments: a survey," Decision Support Systems, vol. 74, pp. 12-32, 2015.

4. O. Khalid, M. U. S. Khan, S. U. Khan, and A. Y. Zomaya, Omnisuggest: A ubiquitous cloud-based context-aware recommendation system for mobile social networks," IEEE Transactions on Services Computing, vol. 7, no. 3, pp. 401-414, 2014.

5. M. Sharma and S. Mann, A survey of recommender systems: approaches and limitations," International Journal of Innovations in Engineering and Technology, vol. 2, no. 2, pp. 8-14, 2013.

6. M. J. Pazzani and D. Billsus, Content-based recommendation systems," in The adaptive web. Springer, 2007, pp. 325-341.

7. X. Su and T. M. Khoshgoftaar, A survey of collaborative filtering techniques," Advances in artificial intelligence, vol. 2009, p. 4, 2009.

8. S. Trewin, Knowledge-based recommender systems," Encyclopedia of library and information science, vol. 69, no. Supplement 32, p. 180, 2000.

9. S. Ujjin and P. J. Bentley, Particle swarm optimization recommender system," in Swarm Intelligence Symposium, 2003. SIS'03. Proceedings of the 2003 IEEE. IEEE, 2003, pp. 124-131.

10. F. Lorenzi12, D. S. dos Santos, and A. L. Bazzan, Case-based recommender system inspired by social insects," 2005.

11. J. Sobecki, Web-based system user interface hybrid recommendation using ant colony metaphor," in International Conference on Knowledge Based and Intelligent Information and Engineering Systems. Springer, 2007, pp. 1033-1040.

12. M. Y. H. Al-Shamri and K. K. Bharadwaj, Fuzzy-genetic approach to recommender systems based on a novel hybrid user model," Expert systems with applications, vol. 35, no. 3, pp. 1386-1399, 2008.

13. J. Handl and B. Meyer, Ant-based and swarm-based clustering," Swarm Intelligence, vol. 1, no. 2, pp. 95-113, 2007.

14. J. Bobadilla, F. Ortega, A. Hernando, and J. Alcala, Improving collaborative filtering recommender system results and performance using genetic algorithms," Knowledge- based systems, vol. 24, no. 8, pp. 1310-1316, 2011.

15. M. Salehi, M. Pourzaferani, and S. A. Razavi, Hybrid attribute-based recommender system for learning material using genetic algorithm and a multidimensional information model," Egyptian Informatics Journal, vol. 14, no. 1, pp. 67-78, 2013.

16. N. Badhe, D. Mishra, C. Joshi, and N. Shukla, Recommender system for music data using genetic algorithm," Int J Innov Adv Comput Sci, vol. 3, no. 2, pp. 66-69, 2014.

17. A. Shrivastava and S. Rajawat, An implementation of hybrid genetic algorithm for clustering based data for web recommendation system," Int J Comput Sci Eng, vol. 2, no. 4, pp. 6-11, 2014.

18. Z. Zhang, H. Lin, K. Liu, D. Wu, G. Zhang, and J. Lu, A hybrid fuzzy-based personalized recommender system for telecom products/services," Information Sciences, vol. 235, pp. 117-129, 2013.

19. J. Lu, Q. Shambour, Y. Xu, Q. Lin, and G. Zhang, a web-based personalized business partner recommendation system using fuzzy semantic techniques," Computational Intelligence, vol. 29, no. 1, pp. 37-69, 2013.

20. L.-C. Cheng and H.-A. Wang, A fuzzy recommender system based on the integration of subjective preferences and objective information," Applied Soft Computing, vol. 18, pp. 290-301, 2014.

21. C. Porcel, A. G. Lopez-Herrera, and E. Herrera-Viedma, A recommender system for re- search resources based on fuzzy linguistic modeling," Expert Systems with Applications, vol. 36, no. 3, pp. 5173-5183, 2009.

22. E. Atashpaz-Gargari and C. Lucas, Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition," in 2007 IEEE Congress on Evolutionary Computation. IEEE, 2007, pp. 4661-4667.

23. X.-S. Yang, Firey algorithm," Engineering Optimization, pp. 221-230, 2010.