A genetic-based pairwise trip planner recommender system

Qomariyah N.N., Kazakov D.

Computer Science Department, Faculty of Computing and Media, Bina Nusantara University, Jakarta, 11480, Indonesia; Computer Science Department, University of York, York, United Kingdom


The massive growth of internet users nowadays can be a big opportunity for the businesses to promote their services. This opportunity is not only for e-commerce, but also for other e-services, such as e-tourism. In this paper, we propose an approach of personalized recommender system with pairwise preference elicitation for the e-tourism domain area. We used a combination of Genetic Agorithm with pairwise user preference elicitation approach. The advantages of pairwise preference elicitation method, as opposed to the pointwise method, have been shown in many studies, including to reduce incosistency and confusion of a rating number. We also performed a user evaluation study by inviting 24 participants to examine the proposed system and publish the POIs dataset which contains 201 attractions used in this study. © 2021, The Author(s).

Genetic algorithm; Pairwise preferences; Preference learning; Recommender system


Journal of Big Data

Publisher: Springer Science and Business Media Deutschland GmbH

Volume 8, Issue 1, Art No 77, Page – , Page Count

Journal Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107034783&doi=10.1186%2fs40537-021-00470-6&partnerID=40&md5=582944eec452e06bba2c921768cf8979

doi: 10.1186/s40537-021-00470-6

Issn: 21961115

Type: All Open Access, Gold, Green


(2020) Temasek and Bain, e-Conomy SEA, , www.thinkwithgoogle.com, Accessed 18 Jan 2021; Pu, P., Chen, L., Hu, R., A user-centric evaluation framework for recommender systems (2011) Proceedings of the Fifth ACM Conference on Recommender Systems (RecSys’11), pp. 57-164. , New York, NY, ACM; Hu, R., Pu, P., Potential acceptance issues of personality-ASED recommender systems (2009) Proceedings of ACM Conference on Recommender Systems (RecSys’09), , New York City, NY, ACM; Pathak, B., Garfinkel, R., Gopal, R., Venkatesan, R., Yin, F., Empirical analysis of the impact of recommender systems on sales (2010) J Manag Inf Syst, 27 (2), pp. 159-188; Ricci, F., Rokach, L., Shapira, B., Introduction to recommender systems handbook (2011) Recommender Systems Handbook, pp. 1-35. , Boston, MA, Springer; Qomariyah, N.N., Fajar, A.N., Learning pairwise preferences from movie ratings (2020) Proceedings of the 2020 International Conference on ICT for Smart Society (ICISS), , IEEE; Liu, N.N., Zhao, M., Yang, Q., Probabilistic latent preference analysis for collaborative filtering (2009) Proceedings of the 18Th ACM Conference on Information and Knowledge Management, pp. 759-766. , ACM; Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.B.P.R., (2009) Bayesian Personalized Ranking from Implicit Feedback, pp. 452-461. , Proceedings of UAI, AUAI Press; Brun, A., Hamad, A., Buffet, O., Boyer, A., (2010) Towards preference relations in recommender systems; Desarkar, M.S., Saxena, R., Sarkar, S., Preference relation based matrix factorization for recommender systems (2012) Proceedings of the International Conference on User Modeling, Adaptation, and Personalization., pp. 63-75. , Springer; Qomariyah, N.N., Kazakov, D., Learning binary preference relations (2017) Proceedings of the 4Th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (Intrs), , CEUR-WS; Qomariyah, N.N., Fajar, A.N., Recommender System for e-Learning based on Personal Learning Style (2019) Proceedings of the 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), , IEEE; Qomariyah, N.N., Kazakov, D., Fajar, A.N., On the benefit of logic-based machine learning to learn pairwise comparisons Bulletin of Electrical Engineering and Informatics (BEEI), 9 (6), p. 2020; Qomariyah, N.N., Sari, S.A., Fajar, A.N., SONIA: An integrated Indonesia online tourism system in new normal era International Journal of Innovative Computing, Information and Control, 16 (6), pp. 1829-1843. , ICIC International; 2020; Kim, K., Ahn, H., A recommender system using GA k-means clustering in an online shopping market (2008) Expert Syst Appl, 34 (2), pp. 1200-1209; Zhang, F., Chang, H., A collaborative filtering algorithm employing genetic clustering to ameliorate the scalability issue (2006) Proceedings of the IEEE International Conference on E-Business Engineering (ICEBE’06, , IEEE; Mohammadpour, T., Bidgoli, A.M., Enayatifar, R., Javadi, H.H.S., Efficient clustering in collaborative filtering recommender system: hybrid method based on genetic algorithm and gravitational emulation local search algorithm (2019) Genomics, 111 (6), pp. 1902-1912; Kilani, Y., Otoom, A.F., Alsarhan, A., Almaayah, M., A genetic algorithms-based hybrid recommender system of matrix factorization and neighborhood-based techniques (2018) Journal of Computational Science, 28, pp. 78-93; Navgaran, D.Z., Moradi, P., Akhlaghian, F., Evolutionary based matrix factorization method for collaborative filtering systems (2013) The 21St Iranian Conference on Electrical Engineering (ICEE). IEEE, pp. 1-5; Alhijawi, B., Kilani, Y., A collaborative filtering recommender system using genetic algorithm (2020) Inf Process Manag, 57 (6), p. 102310; Gasmi, I., Anguel, F., Seridi-Bouchelaghem, H., Azizi, N., (2021) Context-Aware Based Evolutionary Collaborative Filtering Algorithm. Lecture Notes in Networks and Systems, 156, pp. 217-232. , Springer; Xiao, J., Luo, M., Chen, J.M., Li, J.J., (2015) An Item Based Collaborative Filtering System Combined with Genetic Algorithms Using Rating Behavior. Lecture Notes in Computer Science, pp. 453-460. , Springer International Publishing; Gao, L., Li, C., Hybrid personalized recommended model based on genetic algorithm (2008) Proceedings of the 4Th International Conference on Wireless Communications, Networking and Mobile Computing. IEEE; Ho, Y., Fong, S., Yan, Z., A Hybrid GA-based collaborative filtering model for online recommenders (2007) Proceedings of the Second International Conference on E-Business. Scitepress, pp. 200-203; Gangurde, R., Kumar, B., Web page prediction using genetic algorithm and logistic regression based on weblog and web content features (2020) The 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, pp. 68-74; Gupta, S., Kant, V., A comparative analysis of genetic programming and genetic algorithm on multi-criteria recommender systems (2020) The 5Th International Conference on Communication and Electronics Systems (ICCES). IEEE, pp. 1338-1343; Stuart, R.J., Norvig, P., Beyond classical search (2010) Artificial Intelligence: a Modern Approach, , 3rd edn, Essex, Prentice Hall; Pu, P., Chen, L., Hu, R., A user-centric evaluation framework for recommender systems (2011) Proceedings of the 5Th ACM Cconference on Recommender Systems (RecSys’11, pp. 157-164. , ACM

Indexed by Scopus

Leave a Comment