Improving Collaborative Filtering Recommender System Results and Performance using Combination of Fuzzy Grey Wolf Optimizer Algorithm and Lion Optimization Algorithm

Document Type : Original Article

Authors

1 Ph.D. Candidate in Information Technology Management, Department of Information Technology Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran.

2 Assistant Professor, Department of Industrial Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran.

3 Professor, Department of Industrial Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran

Abstract

Nowadays, recommender systems have reshaped the ways of information filtering between websites and the users in order to identify the users’ interests and generate product suggestions for the active users. Recommender systems are generally divided into three groups: Content-based, Knowledge-based, and collaborative-based, and in some cases hybrid. The main idea of collaborative filtering is that they predict a user’s interest in new items based on the recommendations of other people with similar interests. This Approach does not require having knowledge about items. Collaborative filtering has two main types: Memory-based and Model-based. Memory based Collaborative filtering makes use of user rating dataset to compute similarity index between set of users or set of items. The main purpose of this article is to offer a Memory-based Collaborative recommender system in order to optimize the results of Collaborative filtering algorithm. In the proposed method, the combination of fuzzy Grey Wolf Optimizer algorithm and Lion Optimization Algorithm is used to find the most similar users to the target user. The results of the proposed method confirmed a significant increment in Precision, Recall and F-measure in comparison with baseline methods.

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References

  1. Adomavicius, G., & Tuzhilin, A. (2005). 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, 17(6), 734-749.
  2. Alhijawi, B., & Kilani, Y. (2016(, Using genetic algorithms for measuring the similarity values between users in collaborative filtering recommender systems. In 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS) (pp. 1-6). IEEE.
  3. Beni, G., & Wang, J. (1993). Swarm intelligence in cellular robotic systems. In Robots and biological systems: towards a new bionics? (pp. 703-712). Springer, Berlin, Heidelberg.
  4. Bhaidani A. S. (2008). Recommender System Algorithms.Sc thesis, Department of Mechanical and Industrial Engineering.
  5. Bobadilla, J., Ortega, F., Hernando, A., & Alcalá, J. (2011). Improving collaborative filtering recommender system results and performance using genetic algorithms. Knowledge-based systems, 24(8), 1310-1316.
  6. Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-based systems, 46, 109-132.
  7. Can, Ü., & Alataş, B. (2015). Physics based metaheuristic algorithms for global optimization.
  8. Bonabeau, E., Dorigo, M., Marco, D. D. R. D. F., Theraulaz, G., & Théraulaz, G. (1999). Swarm intelligence: from natural to artificial systems (No. 1). Oxford university press.
  9. Choudhary, V., Mullick, D., & Nagpal, S. (2017). Gravitational search algorithm in recommendation systems. International Conference on Swarm Intelligence (pp. 597-607). Springer, Cham.
  10. Deng, S., Huang, L., Xu, G., Wu, X., & Wu, Z. (2016). On deep learning for trust-aware recommendations in social networks. IEEE transactions on neural networks and learning systems, 28(5), 1164-1177.
  11. Dixit, V. S., & Jain, P. (2019). Weighted Percentile-Based Context-Aware Recommender System. In Applications of Artificial Intelligence Techniques in Engineering (pp. 377-388). Springer,
  12. Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE computational intelligence magazine, 1(4), 28-39.
  13. Faraji Amiri, M, & Behnamian, J. (2020). The basic genetic simulation algorithm for solving the workshop flow scheduling problem by considering the energy cost under uncertainty conditions. The Journal of Industrial Management Perspective, 10(2), 9-32 (In Persian).
  14. Feng, C., Liang, J., Song, P., & Wang, Z. (2020). A fusion collaborative filtering method for sparse data in recommender systems. Information Sciences, 521, 365-379.
  15. Gholami, H., Mehdizadeh, I., Naderi, B. (2018). Mathematical modeling and colonial competition algorithm for the workshop flow assembly line problem. The Journal of Industrial Management Perspectives, 8 (1), 93-111. (In Persian).
  16. Hatami, M., & Pashazadeh, S. (2014). Improving results and performance of collaborative filtering-based recommender systems using cuckoo optimization algorithm. International Journal of Computer Applications, 88(16), 46-51.
  17. Herlocker, J., Konstan, J. A., & Riedl, J. (2002). An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Information retrieval, 5(4), 287-310.
  18. Islamia, A., & Azimi, P. (2019). Solving the problem of routing electric vehicles by considering the volume limit of the vehicle using the simulated annealing algorithm. The Journal of Industrial Management Perspective, 9(4), 165-188.(In Persian).
  19. Katarya, R., & Verma, O. P. (2017). An effective collaborative movie recommender system with cuckoo search. Egyptian Informatics Journal, 18(2), 105-112.
  20. Katarya, R., & Verma, O. P. (2018). Recommender system with grey wolf optimizer and FCM. Neural Computing and Applications, 30(5), 1679-1687.
  21. Katarya, R. (2018). Movie recommender system with metaheuristic artificial bee. Neural Computing and Applications, 30(6), 1983-1990.
  22. Kennedy, J., & Eberhart, R. (1995). Particle swarms optimization. IEEE International Conference on Neural Networks, vol. 4.
  23. Kumar, M.S. & Jayagopal, P. (2019). Hybrid Model for Movie Recommendation System Using Fireflies and Fuzzy C-Means. International Journal of Web Portals, 11, 1-13.
  24. Logesh, R., Subramaniyaswamy, V., Malathi, D., Sivaramakrishnan, N., & Vijayakumar, V. (2020). Enhancing recommendation stability of collaborative filtering recommender system through bio-inspired clustering ensemble method. Neural Computing and Applications, 32(7), 2141-2164.
  25. Maier, H. R., Kapelan, Z., Kasprzyk, J., Kollat, J., Matott, L. S., Cunha, M. C., & Ostfeld, A. (2014). Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions. Environmental Modelling & Software, 62, 271-299.
  26. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.
  27. Rad, H.S., & Lucas, C. )2007). A recommender system based on invasive weed optimization algorithm. IEEE Congress on Evol. Comput., Singapore 4297– 4304.
  28. Resnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the ACM, 40(3), 56-58.
  29. Rodríguez, L., Castillo, O., & Soria, J. (2016). A study of parameter dynamic adaptation with fuzzy logic for the grey wolf optimizer algorithm. In Mexican International Conference on Artificial Intelligence (pp. 228-238). Springer, Cham.
  30. Sarwt, M., Levandoski, J.J., Eldawy, A., & Mokbel, MF. (2012). LARS*: An Efficient and Scalable Location- Aware Recommender System. Knowl. Data Eng., 26(6), 1384–1399.
  31. Sinha, B. B., & Dhanalakshmi, R. (2019), Evolution of recommender paradigm optimization over time (In Press), Journal of King Saud University – Computer and Information Sciences
  32. Tohidi, N., & Dadkhah, C. (2020). Improving the performance of video collaborative filtering recommender systems using optimization algorithm. International Journal of Nonlinear Analysis and Applications, 11(1), 483-95.
  33. Ujjin, S., & Bentley, P. J. (2003). Particle swarm optimization recommender system. In Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No. 03EX706) (pp. 124-131). IEEE.
  34. Vimala, S. V., & Vivekanandan, K. (2019). A Kullback–Leibler divergence-based fuzzy C-means clustering for enhancing the potential of an movie recommendation system. SN Applied Sciences, 1(7), 1-11.
  35. Wang, T., Manogaran, G., & Wang, M. (2019). Framework for social tag recommendation using Lion Optimization Algorithm and collaborative filtering techniques. Cluster Computing, 23(3), 2009-2019.
  36. Yadav, S., Vikesh, Shreyam & Nagpal, S. (2018). An improved collaborative filtering based recommender system using bat algorithm. Procedia computer science, 132, 1795-1803.
  37. Yang, X. S., & Karamanoglu, M. (2013). Swarm intelligence and bio-inspired computation: an overview. In Swarm intelligence and bio-inspired computation (pp. 3-23). Elsevier.
  38. Yazdani, M. & Jolai, F. (2015). Lion Optimization Algorithm (LOA): A Nature-Inspired Metaheuristic Algorithm. Journal of Computational Design and Engineering, 4.1016/j.jcde.2015.06.003.

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