References
- [1] K. Matsuoka, “Effects of revenue management on perceived value, customer satisfaction, and customer loyalty,” Journal of Business Research, vol. 148, pp. 131–148, Sep. 2022. https://doi.org/10.1016/j.jbusres.2022.04.052
- [2] D. Mensouri, A. Azmani, and M. Azmani, “K-Means customers clustering by their RFMT and score satisfaction analysis,” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 13, no. 6, 2022, Art no. 6. https://doi.org/10.14569/IJACSA.2022.0130658
- [3] P. K. Singh, E. Othman, R. Ahmed, A. Mahmood, H. Dhahri, and P. Choudhury, “Optimized recommendations by user profiling using apriori algorithm,” Applied Soft Computing, vol. 106, Jul. 2021, Art. no. 107272. https://doi.org/10.1016/j.asoc.2021.107272
- [4] B. Tilahun, C. Awono, and B. Batchakui, “A survey of state-of-the-art: Deep learning methods on recommender system,” IJCA, vol. 162, no. 10, pp. 17–22, Mar. 2017. https://doi.org/10.5120/ijca2017913361
- [5] I. Y. Choi, M. G. Oh, J. K. Kim, and Y. U. Ryu, “Collaborative filtering with facial expressions for online video recommendation,” International Journal of Information Management, vol. 36, no. 3, pp. 397–402, Jun. 2016. https://doi.org/10.1016/j.ijinfomgt.2016.01.005
- [6] N. Jing, T. Jiang, J. Du, and V. Sugumaran, “Personalized recommendation based on customer preference mining and sentiment assessment from a Chinese e-commerce website,” Electronic Commerce Research, vol. 18, no. 1, pp. 159–179, Nov. 2018. https://doi.org/10.1007/s10660-017-9275-6
- [7] H. Zhang, L. Zhao, and S. Gupta, “The role of online product recommendations on customer decision making and loyalty in social shopping communities,” International Journal of Information Management, vol. 38, no. 1, pp. 150–166, Feb. 2018. https://doi.org/10.1016/j.ijinfomgt.2017.07.006
- [8] E. Frias-Martinez, S. Y. Chen, and X. Liu, “Evaluation of a personalized digital library based on cognitive styles: Adaptivity vs. adaptability,” International Journal of Information Management, vol. 29, no. 1, pp. 48–56, Feb. 2009. https://doi.org/10.1016/j.ijinfomgt.2008.01.012
- [9] E. Frias-Martinez, G. Magoulas, S. Chen, and R. Macredie, “Automated user modeling for personalized digital libraries,” International Journal of Information Management, vol. 26, no. 3, pp. 234–248, Jun. 2006. https://doi.org/10.1016/j.ijinfomgt.2006.02.006
- [10] H.-N. Kim, A.-T. Ji, I. Ha, and G.-S. Jo, “Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation,” Electron. Commer. Res. Appl., vol.9, no. 1, pp. 73–83, Jan.–Feb. 2010. https://doi.org/10.1016/j.elerap.2009.08.004
- [11] P. Boström and M. Filipsson, “Comparison of user based and item based collaborative filtering recommendation services,” 2017. [Online]. Available: https://www.semanticscholar.org/paper/Comparison-of-User-Based-and-Item-Based-Filtering-Bostr%C3%B6m-Filipsson/27984027b4fccd7323371129768c459ba16f8fbd. Accessed on: Mar. 04, 2022.
- [12] Y. Chen, Y. Lu, B. Wang, and Z. Pan, “How do product recommendations affect impulse buying? An empirical study on WeChat social commerce,” Information & Management, vol. 56, no. 2, pp. 236–248, Mar. 2019. https://doi.org/10.1016/j.im.2018.09.002
- [13] T. Donkers, B. Loepp, and J. Ziegler, “Sequential user-based recurrent neural network recommendations,” in Proceedings of the Eleventh ACM Conference on Recommender Systems, New York, NY, USA, Aug. 2017, pp. 152–160. https://doi.org/10.1145/3109859.3109877
- [14] B. Hidasi and A. Karatzoglou, “Recurrent neural networks with top-k gains for session-based recommendations,” in Proceedings of the 27th ACM International Conference on Information and Knowledge Management, New York, NY, USA, Oct. 2018, pp. 843–852. https://doi.org/10.1145/3269206.3271761
- [15] I. Islek and S. G. Oguducu, “A hierarchical recommendation system for E-commerce using online user reviews,” Electronic Commerce Research and Applications, vol. 52, Mar.–Apr. 2022, Art. no. 101131. https://doi.org/10.1016/j.elerap.2022.101131
- [16] Z. Li, H. Zhao, Q. Liu, Z. Huang, T. Mei, and E. Chen, “Learning from history and present: Next-item recommendation via discriminatively exploiting user behaviors,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, New York, NY, USA, Jul. 2018, pp. 1734–1743. https://doi.org/10.1145/3219819.3220014
- [17] N. Sachdeva and J. McAuley, “How useful are reviews for recommendation? A critical review and potential improvements,” in Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, USA, Jul. 2020, pp. 1845–1848. https://doi.org/10.1145/3397271.3401281
- [18] B. Shao, X. Li, and G. Bian, “A survey of research hotspots and frontier trends of recommendation systems from the perspective of knowledge graph,” Expert Systems with Applications, vol. 165, Mar. 2021, Art no. 113764. https://doi.org/10.1016/j.eswa.2020.113764
- [19] D. R. Stöckli and H. Khobzi, “Recommendation systems and convergence of online reviews: The type of product network matters!” Decision Support Systems, vol. 142, Mar. 2021, Art. no. 113475. https://doi.org/10.1016/j.dss.2020.113475
- [20] J. Zhang, M. Li, W. Liu, S. Lauria, and X. Liu, “Many-objective optimization meets recommendation systems: A food recommendation scenario,” Neurocomputing, vol. 503, pp. 109–117, Sep. 2022. https://doi.org/10.1016/j.neucom.2022.06.081
- [21] J. Zheng, Q. Li, and J. Liao, “Heterogeneous type-specific entity representation learning for recommendations in e-commerce network,” Information Processing & Management, vol. 58, no. 5, Sep. 2021, Art. no. 102629. https://doi.org/10.1016/j.ipm.2021.102629
- [22] F. T. Nobibon, R. Leus, and F. C. R. Spieksma, “Optimization models for targeted offers in direct marketing: Exact and heuristic algorithms,” European Journal of Operational Research, vol. 210, no. 3, pp. 670–683, May 2011. https://doi.org/10.1016/j.ejor.2010.10.019
- [23] I. Viktoratos, A. Tsadiras, and N. Bassiliades, “A context-aware web-mapping system for group-targeted offers using semantic technologies,” Expert Systems with Applications, vol. 42, no. 9, pp. 4443–4459, Jun. 2015. https://doi.org/10.1016/j.eswa.2015.01.039
- [24] K. Sajithra and R. Patil, “Social media – History and components,” IOSRJBM, vol. 7, no. 1, pp. 69–74, Jan.–Feb. 2013. https://doi.org/10.9790/487X-0716974
- [25] D. Darshana and C. Vyas, “Problem of customer information overload and interactive marketing as its solution,” Synergy Journal of Management, vol. 1, pp. 10–16, Jan. 2011.
- [26] P. K. Singh, P. K. D. Pramanik, and P. Choudhury, “Collaborative filtering in recommender systems: Technicalities, challenges, applications, and research trends,” in New Age Analytics, 1st ed., G. Shrivastava, S.-L. Peng, H. Bansal, K. Sharma, and M. Sharma, Eds. Apple Academic Press, 2020. https://doi.org/10.1201/9781003007210-8
- [27] J. Cao, Z. Wu, Y. Wang, and Y. Zhuang, “Hybrid collaborative filtering algorithm for bidirectional Web service recommendation,” Knowl. Inf. Syst., vol. 36, no. 3, pp. 607–627, Sep. 2013. https://doi.org/10.1007/s10115-012-0562-1
- [28] P. B.Thorat, R. Goudar, and S. Barve, “Survey on collaborative filtering, content-based filtering and hybrid recommendation system,” International Journal of Computer Applications, vol. 110, no. 4, pp. 31–36, Jan. 2015. https://doi.org/10.5120/19308-0760
- [29] F. Bo and C. Jiujun, “Collaborative filtering and recommendation algorithm based on multiple similarities among users,” J. Computer Science, vol. 39, pp. 23–26, 2012.
- [30] G. D. Linden, J. A. Jacobi, and E. A. Benson, “Collaborative recommendations using item-to-item similarity mappings,” U.S. patent US6266649B1, 1998. [Online]. Available: https://patents.google.com/patent/US6266649/en. Accessed on: Mar. 04, 2022.
- [31] S. Jayalakshmi, N. Ganesh, R. Čep, and J. S. Murugan, “Movie recommender systems: Concepts, methods, challenges, and future directions,” Sensors, vol. 22, no. 13, Jan. 2022, Art. no. 13. https://doi.org/10.3390/s22134904926975235808398
- [32] Ç. Odabaşı, P. Dologlu, F. Gülmez, G. Kuşoğlu, and Ö. Çağlar, “Investigation of the factors affecting reverse osmosis membrane performance using machine-learning techniques,” Computers & Chemical Engineering, vol. 159, Mar. 2022, Art no. 107669. https://doi.org/10.1016/j.compchemeng.2022.107669
- [33] P. Jirapatsil and N. Phumchusri, “Market basket analysis for fresh products location improvement: A case study of E-commerce business warehouse,” in Proceedings of the 4th International Conference on Management Science and Industrial Engineering, New York, NY, USA, Apr. 2022, pp. 23–28. https://doi.org/10.1145/3535782.3535786
- [34] A. Monteserin and M. G. Armentano, “Influence-based approach to market basket analysis,” Information Systems, vol. 78, pp. 214–224, Nov. 2018. https://doi.org/10.1016/j.is.2018.01.008
- [35] S. Halim, T. Octavia, and C. Alianto, “Designing facility layout of an amusement arcade using market basket analysis,” Procedia Computer Science, vol. 161, pp. 623–629, Jan. 2019. https://doi.org/10.1016/j.procs.2019.11.165
- [36] Y. Chen, “Research on personalized recommendation algorithm based on user preference in mobile e-commerce,” Information Systems and e-Business Management, vol. 18, pp. 837–850, Dec. 2020. https://doi.org/10.1007/s10257-019-00401-2
- [37] M. Gupta, S. Kochhar, P. Jain, and P. Nagrath, “Hybrid recommender system using A-priori algorithm.” in Proceedings of International Conference on Sustainable Computing in Science, Technology and Management (SUSCOM), Jaipur, India, Feb. 24, 2019. https://doi.org/10.2139/ssrn.3349290
- [38] R. Katarya and O. P. Verma, “A collaborative recommender system enhanced with particle swarm optimization technique,” Multimed Tools Appl, vol. 75, no. 15, pp. 9225–9239, Aug. 2016. https://doi.org/10.1007/s11042-016-3481-4
- [39] W. J. Saunders, S. R. Mousa, and J. Codjoe, “Market basket analysis of safety at active highway-railroad grade crossings,” Journal of Safety Research, vol. 71, pp. 125–137, Dec. 2019. https://doi.org/10.1016/j.jsr.2019.09.00231862023
- [40] K. Sherly and R. Nedunchezhian, “A improved incremental and interactive frequent pattern mining techniques for market basket analysis and fraud detection in distributed and parallel systems,” Indian Journal of Science and Technology, vol. 8, no. 18, pp. 1–12, 2015. https://doi.org/10.17485/IJST/2015/V8I18/55109
- [41] A. Valarmathi, “Market basket analysis for mobile showroom,” International Journal for Research in Applied Science & Engineering Technology, vol. 5, no. X, Oct. 2017. https://doi.org/10.22214/IJRASET.2017.10185
- [42] M. A. Valle, G. A. Ruz, and R. Morrás, “Market basket analysis: Complementing association rules with minimum spanning trees,” Expert Systems with Applications, vol. 97, pp. 146–162, May 2018. https://doi.org/10.1016/j.eswa.2017.12.028
- [43] H. Ko, S. Lee, Y. Park, and A. Choi, “A survey of recommendation systems: Recommendation models, techniques, and application fields,” Electronics, vol. 11, no. 1, Jan. 2022, Art. no. 141. https://doi.org/10.3390/electronics11010141
- [44] H. Shorman and Y. Jbara, “An improved association rule mining algorithm based on Apriori and Ant Colony approaches,” IOSR Journal of Engineering, vol. 07, pp. 18–23, Jul. 2017. https://doi.org/10.9790/3021-0707011823
- [45] N. Zeng and H. Xiao, “Inferring implications in semantic maps via the Apriori algorithm,” Lingua, vol. 239, Feb. 2020, Art. no. 102808. https://doi.org/10.1016/j.lingua.2020.102808
- [46] R. Agrawal and R. Srikant, “Fast algorithms for mining association rules in large databases,” in Proceedings of the 20th International Conference on Very Large Data Bases, San Francisco, CA, USA, Sep. 1994, pp. 487–499.
- [47] J. Deshmukh and U. Bhosle, “Image mining using association rule for medical image dataset,” Procedia Computer Science, vol. 85, pp. 117–124, Jan. 2016. https://doi.org/10.1016/j.procs.2016.05.196
- [48] E. M. H. Saeed and B. A. Hammood, “Estimation and evaluation of students’ behaviors in E-learning environment using adaptive computing,” Materials Today: Proceedings, May 2021. https://doi.org/10.1016/j.matpr.2021.04.519
- [49] M. John and H. Shaiba, “Apriori-based algorithm for Dubai road accident analysis,” Procedia Computer Science, vol. 163, pp. 218–227, Jan. 2019. https://doi.org/10.1016/j.procs.2019.12.103
- [50] U. Çiçekli and İ. Kabasakal, “Market basket analysis of basket data with demographics: A case study in E-retailing,” Alphanumeric Journal, vol. 9, no. 1, pp. 1–12, Jun. 2021. https://doi.org/10.17093/alphanumeric.752505
- [51] P. Harrington, Machine Learning in Action. Manning, 2012.
- [52] H.-K. Lin, C.-H. Hsieh, N. Wei, and Y. Peng, “Association rules mining in R for product performance management in industry 4.0,” Procedia CIRP, vol. 83, pp. 699–704, 2019. https://doi.org/10.1016/J.PROCIR.2019.04.099
- [53] R. Agrawal and R. Srikant, “Fast algorithms for mining association rules,” in Readings in Database Systems, 3rd ed. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1998, pp. 580–592.
- [54] M. A. Mahdi, K. M. Hosny, and I. Elhenawy, “FR-Tree: A novel rare association rule for big data problem,” Expert Systems with Applications, vol. 187, Jan. 2022, Art. no. 115898. https://doi.org/10.1016/j.eswa.2021.115898
- [55] T. Kim, D. Kim, and Y. Ahn, “Instant customer base analysis in the financial services sector,” Expert Systems with Applications, vol. 202, Apr. 2022, Art. no. 117326. https://doi.org/10.1016/j.eswa.2022.117326
- [56] K. Jerath, P. S. Fader, and B. G. S. Hardie, “New perspectives on customer ‘death’ using a generalization of the Pareto/NBD model,” 2011. [Online]. Available: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=99555810.2139/ssrn.995558
- [57] X. Chen, R. Geng, and S. Cai, “Predicting microblog users’ lifetime activities – A user-based analysis,” Electronic Commerce Research and Applications, vol. 14, no. 3, pp. 150–168, May 2015. https://doi.org/10.1016/j.elerap.2014.06.001
- [58] J. Dipak and S. S. Siddhartha, “Customer lifetime value research in marketing: A review and future directions,” 2002. [Online]. Available: https://www.proquest.com/openview/1f7fc325cb76ec2ede9989ff9defb177/1?pq-origsite=gscholar&cbl=32002. Accessed on: Dec. 06, 2021.
- [59] T. Reutterer, M. Platzer, and N. Schröder, “Leveraging purchase regularity for predicting customer behavior the easy way,” International Journal of Research in Marketing, vol. 38, no. 1, pp. 194–215, Mar. 2021. https://doi.org/10.1016/j.ijresmar.2020.09.002
- [60] R. Colombo and W. Jiang, “A stochastic RFM model,” Journal of Interactive Marketing, vol. 13, no. 3, pp. 2–12, Aug. 1999. https://doi.org/10.1002/(SICI)1520-6653(199922)13:3<2::AIDDIR1>3.0.CO;2-H10.1002/(SICI)1520-6653(199922)13:3<2::AID-DIR1>3.3.CO;2-8
- [61] P. S. Fader and B. G. S. Hardie, “The Gamma-Gamma model of monetary value,” 2013. [Online]. Available: http://brucehardie.com/notes/025/10.1088/1475-7516/2013/09/025
- [62] J. Romero, R. van der Lans, and B. Wierenga, “A partially hidden Markov model of customer dynamics for CLV measurement,” Journal of Interactive Marketing, vol. 27, no. 3, pp. 185–208, Aug. 2013. https://doi.org/10.1016/j.intmar.2013.04.003
- [63] J. Benesty, J. Chen, Y. Huang, and I. Cohen, “Pearson correlation coefficient,” in Noise Reduction in Speech Processing. Springer Topics in Signal Processing, vol 2. Springer, Berlin, Heidelberg, 2009, pp. 1–4. https://doi.org/10.1007/978-3-642-00296-0_5
- [64] K. H. Kim, E. Ko, B. Xu, and Y. Han, “Increasing customer equity of luxury fashion brands through nurturing consumer attitude,” Journal of Business Research, vol. 65, no. 10, pp. 1495–1499, Oct. 2012. https://doi.org/10.1016/j.jbusres.2011.10.016
- [65] F. F. Reichheld and T. Teal, The Loyalty Effect the Hidden Force behind Growth, Profits, and Lasting Value. Harvard Business School Press, Jan. 1996.
- [66] M. Utz, S. Johanning, T. Roth, T. Bruckner, and J. Strüker, “From ambivalence to trust: Using blockchain in customer loyalty programs,” International Journal of Information Management, vol. 68, Mar. 2022, Art. no. 102496. https://doi.org/10.1016/j.ijinfomgt.2022.102496
- [67] D. Di Fatta, D. Patton, and G. Viglia, “The determinants of conversion rates in SME e-commerce websites,” Journal of Retailing and Consumer Services, vol. 41, pp. 161–168, Mar. 2018. https://doi.org/10.1016/j.jretconser.2017.12.008