In today’s Intelligent Systems, the ability to under- stand and predict how customers behave is an ongoing challenge, especially for fast-moving online environments like e-commerce sites, online services and current-day recommendation systems. The bulk of Machine Learning approaches that use traditional machine learning methods usually use a static dataset with batch processes to create models that do not change over time. They are much more limited in their capacity to reflect these continual alterations in user preferences and behaviour, especially since they do not take into account the influence human intelligence may have on an individual’s behaviour, such as his or her use of information or emotional reactions to that information when making decisions. This paper proposes a new human–AI collaboration frame- work that includes learning from behavioral interactions in order to provide real-time prediction of customer behavior dynamically. This framework captures specific interaction signals from users, such as clickstream sequences, how long users have previously browsed at certain times, their level of engagement for particular sessions, and what kinds of transactions they have made. These interaction signals are then transformed into useful behavioral representations that are submitted for processing through a hybrid machine learning architecture made up of both supervised machine learning models and sequential behavioral modeling techniques in order to support short- and long-term estimation of user intent. The integration of a human-in-the-loop feedback system is an important part of this research. This allows for continuous im- provement in the interpretability, adaptability and performance of the predictive models through iterative refinements by external experts and users. Also developed was an adaptive learning approach where model parameters are periodically updated with new interaction data in response to a changing environment. The experiments show that the system has much higher accu- racy predicting behavior, is more personalized and faster than previous models. The system also demonstrates that it will work with a wide variety of new products and is therefore appropriate for deployment in practical, large scale applications. The work shows the power of a synergistic Human–AI relationship to move customer analytics systems to the next level of capability.

In the work presented in this paper, the authors have developed a new Human-AI collaboration framework that studies customer behavior by utilizing ”behavioral interaction learning.” The hybrid approach described in this research solves many of the limitations found in traditional machine learning models through the application of a hybrid archi- tecture that combines the real-time processing of behavioral data, the analysis of sequential patterns, and human-in-the- loop feedback. The structure of the framework allows for continuous learn- ing from the user and the updating of the predictive model at all times; therefore, it is an effective means of capturing dynamic user behavior and improving both prediction accuracy and personalization. By combining multiple machine learning algorithms into a single unified system, the fragmentation of machine learning systems is avoided while increasing the reliability and flexibility of the entire system. Furthermore, the use of human input adds to the interpretability and contextual relevance of the predictions made by the model. Through experimental validation, it has been shown that the methodology proposed in this paper is superior to the performance of traditional machine learning approaches with respect to accuracy, adaptability, and responsiveness. The combination of automated intelligence and human intelligence makes this framework applicable to real world missions, such as e-commerce, digital marketing, and intelligent customer relationship management. Overall, this study illustrates how effective Human-AI collaboration can be used to advance the next generation of intelligent systems. In addition to improving predictive performance, the framework outlined in this paper provides a basis for the development of more adaptive, transparent, and user centric AI-based solutions.

[1]G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems,” IEEE Trans. Knowl. Data Eng., vol. 17, no. 6, pp. 734–749, 2005. [2]Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” IEEE Computer, vol. 42, no. 8, pp. 30–37, 2009. [3]X. Su and T. M. Khoshgoftaar, “A survey of collaborative filtering techniques,” Adv. Artif. Intell., 2009. [4]H. Wang et al., “Collaborative deep learning for recommender systems,” KDD, 2015. [5]J. Davidson et al., “The YouTube video recommendation system,” RecSys, 2010. [6]P. Resnick and H. R. Varian, “Recommender systems,” Commun. ACM, 1997. [7]S. Rendle et al., “BPR: Bayesian personalized ranking from implicit feedback,” UAI, 2009. [8]X. He et al., “Neural collaborative filtering,” WWW, 2017. [9]Q. Zhao et al., “Deep reinforcement learning for recommendation,” AAAI, 2018. [10]R. Sutton and A. Barto, “Reinforcement Learning: An Introduction,” MIT Press, 2018. [11]A. Karatzoglou et al., “Multiverse recommendation,” RecSys, 2010. [12]J. L. Herlocker et al., “Evaluating collaborative filtering recommender systems,” ACM Trans., 2004. [13]C. C. Aggarwal, “Recommender Systems: The Textbook,” Springer, 2016. [14]L. Baltrunas et al., “Context-aware recommendation,” RecSys, 2011. [15]D. Jannach et al., “Recommender Systems Handbook,” Springer, 2015. [16]H. Liu et al., “Contextual recommendation systems,” IEEE Trans., 2010. [17]J. Bobadilla et al., “Recommender systems survey,” Knowl.-Based Syst., 2013. [18]K. Goldberg et al., “Eigentaste recommendation algorithm,” Inf. Retr., 2001. [19] M. Deshpande and G. Karypis, “Item-based recommendation algo- rithms,” ACM Trans., 2004. [20]X. Amatriain and J. Basilico, “Netflix recommendations,” ACM, 2012. [21]S. Zhang et al., “Deep learning based recommender systems,” ACM Comput. Surv., 2019. [22]Y. Zhang and Q. Yang, “Survey on multi-task learning,” IEEE Trans., 2021. [23]T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” KDD, 2016. [24]L. Breiman, “Random forests,” Mach. Learn., 2001. [25]C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn., 1995. [26]I. Goodfellow et al., “Deep Learning,” MIT Press, 2016. [27]S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput., 1997. [28]A. Vaswani et al., “Attention is all you need,” NeurIPS, 2017. [29]D. Bahdanau et al., “Neural machine translation by jointly learning to align,” ICLR, 2015. [30]K. He et al., “Deep residual learning,” CVPR, 2016. [31]T. Mikolov et al., “Efficient estimation of word representations,” ICLR, 2013. [32]J. Dean et al., “Large scale distributed deep networks,” NIPS, 2012. [33]M. Jordan and T. Mitchell, “Machine learning: Trends,” Science, 2015. [34]P. Domingos, “A few useful things to know about ML,” Commun. ACM, 2012. [35]F. Provost and T. Fawcett, “Data Science for Business,” O’Reilly, 2013. [36]S. Russell and P. Norvig, “Artificial Intelligence: A Modern Approach,” Pearson, 2016. [37]B. Shneiderman, “Human-centered AI,” Commun. ACM, 2020. [38]D. Amershi et al., “Guidelines for human-AI interaction,” CHI, 2019. [39]T. Davenport and R. Ronanki, “Artificial intelligence for business,” HBR, 2018. [40]R. Burke, “Hybrid recommender systems,” User Modeling, 2002. [41]M. Pazzani and D. Billsus, “Content-based recommendation systems,” 2007. [42]J. Kleinberg, “Algorithm design,” Pearson, 2005. [43]H. Chen et al., “Business intelligence and analytics,” MIS Quarterly, 2012. [44]V. Mayer-Scho¨nberger, “Big data revolution,” 2013. [45]K. Murphy, “Machine Learning: A Probabilistic Perspective,” MIT Press, 2012. [46]T. Cover and J. Thomas, “Elements of Information Theory,” Wiley, 2006. [47]A. Ng, “Machine learning yearnings,” 2018. [48]R. Kohavi et al., “Online controlled experiments,” KDD, 2007. [49]J. Leskovec et al., “Mining of Massive Datasets,” Cambridge, 2014. C. Bishop, “Pattern Recognition and Machine Learning,” Springer, 2006.

© 2025 International Journal of Engineering and Techniques (IJET).

Digital Object Identifier (DOI)

IJET assigns a unique DOI to every accepted article via Zenodo for persistent linking and citation. Your article’s DOI: https://doi.org/{{doi}}

Open DOI About Zenodo DOI

Close