Data mining techniques and information personalization have made significant growth in the past decade. Enormous volume of data is generated every day. Recommender systems can help users to find their specific information in the extensive volume of information. Several techniques have been presented for development of Recommender System (RS). One of these techniques is the Evolutionary Computing (EC), which can optimize and improve RS in the various applications. This study investigates the number of publications, focusing on some aspects such as the recommendation techniques, the evaluation methods and the datasets which are used.
KeywordsEvolutionary ComputingGenetic AlgorithmRecommender System
Lu, J., Wu, D., Mao, M., Wang, W. and Zhang, G. (2015) Recommender System Application Developments: A Survey. Decision Support Systems, 74, 12-32.
Suganeshwari, G. and Syed Ibrahim, S.P. (2016) A Survey on Collaborative Filtering Based Recommendation System. Proceedings of the 3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC’16), 49, 503-518. https://doi.org/10.1007/978-3-319-30348-2_42
Bobadilla, J., Ortega, F. Hernando, A. and Gutierrez, A. (2013) Recommender Systems Survey. Knowledge-Based System, 46, 109-132.
Park, D., Kim, H., Choi, I. and Kim, J. (2012) A Literature Review and Classification of Recommender Systems Research. Expert Systems with Applications, 39, 10059-10072.
Pero, S. and Horvath, T. (2013) Opinion-Driven Matrix Factorization for Rating Prediction. In: Carberry, S., Weibelzahl, S., Micarelli, A. and Semeraro, G., Eds., User Modeling, Adaptation, and Personalization. UMAP 2013. Lecture Notes in Computer Science, Vol. 7899, Springer, Berlin, Heidelberg, 1-13.
Kelly, D. and Teevan, J. (2003) Implicit Feedback for Inferring User Preference: A Bibliography. SIGIR Forum, 37, 18-28. https://doi.org/10.1145/959258.959260
Bobadilla, J., Ortega, F., Hernando, A. and Arroyo, A. (2012) A Balanced Memory Based Collaborative Filtering Similarity Measure. International Journal of Intelligent Systems, 27, 939-946. https://doi.org/10.1002/int.21556
Ashley-Dejo, E., Ngwira, S. and Zuva, T. (2015) A Survey of Context-Aware Recommender System and Services. International Conference on Computing, Communication and Security (ICCCS), Pamplemousses, 4-5 December 2015, 1-6.
Abbas, A., Zhang, L. and Khan, S.U. (2015) A Survey on Context-Aware Recommender Systems Based on Computational Intelligence Techniques. Computing, 97, 667-690. https://doi.org/10.1007/s00607-015-0448-7
Kwon, J. (2012) A Study on Effective Proactive Service in Pervasive Computing Environment. International Journal of Computer Science and Network Security, 12, 35-39.
Smirnov, A., Kashevnik, A., Ponomarev, A., Shilov, N., Schekotov, M. and Teslya, N. (2013) Recommendation System for Tourist Attraction Information Service. 14th Conference of Open Innovation Association FRUCT, Espoo, 11-15 November 2013, 148-155. https://doi.org/10.1109/FRUCT.2013.6737957
Yeung, K.F. and Yang, Y. (2010) A Proactive Personalized News Recommender System. Presented at the Development in E-Systems Engineering (DESE), London.
Dehghani Champiria, Z., Shahamirib, R. and Salima, S. (2015) A Systematic Review of Scholar Context-Aware Recommender Systems. Expert Systems with Applications, 42, 1743-1758.
Adomavicius, G., Sankaranarayanan, R., Sen, S. and Tuzhilin, A. (2005) Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach. ACM Transactions on Information Systems (TOIS), 23, 103-145. https://doi.org/10.1145/1055709.1055714
Campos, P.G., Fernández-Tobias, I., Cantador, I. and Diez, F. (2013) Context-Aware Movie Recommendations: An Empirical Comparison of Pre-Filtering, Post-Filtering and Contextual Modeling Approaches. E-Commerce and Web Technologies, 152, 137-149. https://doi.org/10.1007/978-3-642-39878-0_13
Verbert, K., Manouselis, N., Ochoa, X., Wolpers, M., Drachsler, H., Bosnic, I. and Duval, E. (2012) Context-Aware Recommender Systems for Learning: A Survey and Future Challenges. IEEE Transactions on Learning Technologies, 5, 318-335. https://doi.org/10.1109/TLT.2012.11
Panniello, U., Tuzhilin, A. and Gorgoglione, M. (2014) Comparing Context-Aware Recommender Systems in Terms of Accuracy and Diversity. User Modeling and User-Adapted Interaction, 24, 35-65.
Asabere, N.Y. (2013) Towards a Viewpoint of Context-Aware Recommender Systems (CARS) and Services. International Journal of Computer Science and Telecommunications, 4, 19-29.
Gallego, D., Barra, E., Rodriguez, P. and Huecas, G. (2013) Incorporating Proactivity to Context-Aware Recommender Systems for E-Learning. 2013 World Congress on Computer and Information Technology (WCCIT), Sousse, 22-24 June 2013, 1-6.
Champiri, Z.D., Salim, S.S.B. and Shahamiri, S.R. (2015) The Role of Context for Recommendations in Digital Libraries. International Journal of Social Science and Humanity, 5, 948-954.
Yu, X. and Gen, M. (2010) Introduction to Evolutionary Algorithms. Springer-Verlag, London.
Eiben, A.E. and Smith, J.E. (2003) Introduction to Evolutionary Computing. Springer-Verlag, Berlin, Heidelberg.
Fogel, D.B. (1995) Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. IEEE Press, Piscataway, NJ.
Horvath, T. and Carvalh, A. (2016) Evolutionary Computing in Recommender System: A Review of Recent Research. Natural Computing, 1-22.
Bhattacharya, M. (2013) Evolutionary Approaches to Expensive Optimisation. International Journal of Advanced Research in Artificial Intelligence, 2, 53-59. https://doi.org/10.14569/ijarai.2013.020308
Floreano, D. and Mattiussi, C. (2008) Bio-Inspired Artificial Intelligence. Theories, Methods, and Technologies. The MIT Press, Cambridge.
Ong, Y.S., Lim, M.H. and Chen, X. (2010) Memetic Computation—Past, Present and Future. IEEE Computational Intelligence Magazine, 5, 24-31. https://doi.org/10.1109/MCI.2010.936309
Velez-Langs, O. and De Antonio, A. (2014) Learning User’s Characteristics in Collaborative Filtering through Genetic Algorithms: Some New Results. In: Jamshidi, M., Kreinovich, V. and Kacprzyk, J., Eds., Advance Trends in Soft Computing. Studies in Fuzziness and Soft Computing, Vol. 312, Springer, Cham, 309-326. https://doi.org/10.1007/978-3-319-03674-8_30
Venturini, V., Carb, J. and Molina, J.M. (2008) Learning User Profile with Genetic Algorithm in AmI Applications. In: Corchado, E., Abraham, A. and Pedrycz, W., Eds., Hybrid Artificial Intelligence Systems. HAIS 2008. Lecture Notes in Computer Science, Vol. 5271, Springer, Berlin, Heidelberg, 124-131. https://doi.org/10.1007/978-3-540-87656-4_16
Ujjin, S. and Bentley, P. (2002) Learning User Preferences Using Evolution. Asia-Pacific Conference on Simulated Evolution and Learning.
Ho, Y., Fong, S. and Hang, Y. (2007) A Hybrid GA-Based Collaborative Filtering Model for Online Recommenders. Proceedings of the International Conference on e-Business, Barcelona, 28-31 July 2007, 200-203.
Dao, T.H., Jeong, S.R. and Ahn, H. (2012) A Novel Recommendation Model of Location-Based Advertising Context-Aware Collaborative Filtering Using GA Approach. Expert Systems with Applications: An International Journal, 39, 3731-3739.
Agarwal, V. and Bharadwaj, K. (2011) Trust-Enhanced Recommendation of Friends in Web Based Social Networks Using Genetic Algorithms to Learn User Preferences. In: Nagamalai, D., Renault, E. and Dhanuskodi, M., Eds., Trends in Computer Science, Engineering and Information Technology. Communications in Computer and Information Science, Vol. 204, Springer, Berlin, Heidelberg, 476-485. https://doi.org/10.1007/978-3-642-24043-0_48
Agarwal, V. and Bharadwaj, K. (2012) A Collaborative Filtering Framework for Friends Recommendation in Social Networks Based on Interaction Intensity and Adaptive User Similarity. Social Network Analysis and Mining, 3, 359-379. https://doi.org/10.1007/s13278-012-0083-7
Liang, Y. and Li, Q. (2011) Incorporating Interest Preference and Social Proximity into Collaborative Filtering for Folk Recommendation. SIGIR 2011 Workshop on Social Web Search and Mining, Analysis under Crisis, Beijing, 28 July 2011.
Silva, E., Camilo Jr., C., Pascoal, L. and Rosa, T. (2016) An Evolutionary Approach for Combining Results of Recommender Systems Techniques Based on Collaborative Filtering. IEEE Congress on Evolutionary Computation, Beijing, 6-11 July 2014, 959-966.
Xiao, J., Luo, M., Chen, J.M. and Li, J.J. (2015) An Item Based Collaborative Filtering System Combined with Genetic Algorithms Using Rating Behavior. In: Huang, D.S. and Han, K., Eds., Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science, Vol. 9227, Springer, Cham, 453-460. https://doi.org/10.1007/978-3-319-22053-6_48
Bobadilla, J., Ortega, F., Hernando, A. and Alcala, J. (2011) Improving Collaborative Filtering Recommender System Results and Performance Using Genetic Algorithms. Knowledge-Based Systems, 24, 1310-1316.
Ribeiro, M.T., Lacerda, A., Veloso, A. and Ziviani, N. (2012) Pareto-Efficient Hybridization for Multi-Objective Recommender Systems. Proceedings of the 6th ACM Conference on Recommender Systems, Dublin, 9-13 September 2012, 19-26.
Kim, K. and Ahn, H. (2004) Using a Clustering Genetic Algorithm to Support Customer Segmentation for Personalized Recommender Systems. In: Kim, T.G., Eds., Artificial Intelligence and Simulation. AIS 2004. Lecture Notes in Computer Science, Vol. 3397, Springer, Berlin, Heidelberg, 409-415. https://doi.org/10.1007/978-3-540-30583-5_44
Kim, K. and Ahn, H. (2008) A Recommender System Using GA k-Means Clustering in an Online Shopping Market. Expert Systems with Applications, 34, 1200-1209.
Banati, H. and Mehta, S. (2010) Memetic Collaborative Filtering Based Recommender System. 2010 2nd Vaagdevi International Conference on Information Technology for Real World Problems, Warangal, 9-11 December 2010, 102-107.
Banati, H. and Mehta, S. (2010) A Multi-Perspective Evaluation of MA and GA for Collaborative Filtering Recommender System. International Journal of Computer Science & Information Technology (IJCSIT), 2, 102-122.
Chen, X., Ong, Y.S., Lim, M.H. and Tan, K.C. (2011) A Multi-Facet Survey on Memetic Computation. IEEE Transactions on Evolutionary Computation, 15, 591-607. https://doi.org/10.1109/TEVC.2011.2132725
Marung, U., Theera-Umpon, N. and Auephanwiriyakul, S. (2014) Applying Memetic Algorithm-Based Clustering to Recommender System with High Sparsity Problem. Journal of Central South University, 21, 3541-3550. https://doi.org/10.1007/s11771-014-2334-4
Georgiou, O. and Tsapatsoulis, N. (2010) Improving the Scalability of Recommender Systems by Clustering Using Genetic Algorithms. In: Diamantaras, K., Duch, W. and Iliadis, L.S., Eds., Artificial Neural Networks, ICANN 2010. Lecture Notes in Computer Science, Vol. 6352, Springer, Berlin, Heidelberg, 442-449.
Salehi, M., Kamalabadi, I. and Ghaznavi-Ghoushchi, M. (2013) Attribute-Based Collaborative Filtering Using Genetic Algorithm and Weighted C-Means Algorithm. International Journal of Business Information Systems, 13, 265-283. https://doi.org/10.1504/IJBIS.2013.054465
Salehi, M., Pourzaferani, M. and Razavi, S. (2013) Hybrid Attribute-Based Recommender System for Learning Material Using Genetic Algorithm and a Multidimensional Information Model. Egyptian Informatics Journal, 14, 67-78.
Hofmann, T. (2004) Latent Semantic Models for Collaborative Filtering. ACM Transactions on Information Systems (TOIS), 22, 89-115. https://doi.org/10.1145/963770.963774
Li, Q., Yao, M., Yang, J. and Xu, N. (2014) Genetic Algorithm and Graph Theory Based Matrix Factorization Method for Online Friend Recommendation. The Scientific World Journal, 2014, Article ID: 162148. https://doi.org/10.1155/2014/162148
Plassman, G.E. (2013) A Survey of Singular Value Decomposition Methods and Performance Comparison of Some Available Serial Codes. NASA Technical Reports Server (NTRS).
Clemencon, S., Bertail, P., Chautru, E. and Papa, G. (2015) Survey Schemes for Stochastic Gradient Descent with Applications to M-Estimation. Journal of Multivariate Analysis.
Guimaraes, A., Costa, T.F., Lacerda, A., Pappa, G.L. and Ziviani, N. (2013) Guard: A Genetic Unified Approach for Recommendation. Journal of Information and Data Management, 4, 295-310.
Cremonesi, P., Koren, Y. and Turrin, R. (2010) Performance of Recommender Algorithms on Top-n Recommendation Tasks. 4th ACM Conference on Recommender Systems, Barcelona, 26-30 September 2010, 39-46.
Zuo, Y., Gong, M., Zeng, J., Ma, L. and Jiao, L. (2015) Personalized Recommendation Based on Evolutionary Multi-Objective Optimization [Research Frontier]. IEEE Computational Intelligence Magazine, 10, 52-62. https://doi.org/10.1109/MCI.2014.2369894
Wang, S., Gong, M., Ma, L., Cai, Q. and Jiao, L. (2014) Decomposition Based Multiobjective Evolutionary Algorithm for Collaborative Filtering Recommender Systems. IEEE Congress on Evolutionary Computation (CEC), Beijing, 6-11 July 2014, 672-679.
Geng, B., Li, L., Jiao, L., Gong, M., Cai, Q. and Wu, Y. (2015) NNIA-RS: A Multi-Objective Optimization Based Recommender System. Physica A: Statistical Mechanics and Its Applications, 424, 383-397.