Adapting Language Models to User Behavior: A Technical Analysis
DOI:
https://doi.org/10.32628/CSEIT25112493Keywords:
Attention Mechanisms, Event Processing, Sequential Modeling, Temporal Analysis, User BehaviorAbstract
The adaptation of transformer architectures to user behavior analysis marks a pivotal advancement in processing event streams and temporal data. While originally designed for natural language processing, these architectures have been successfully modified to handle the unique challenges of user behavior sequences, including concurrent events, variable time intervals, and complex temporal dependencies. Through enhanced positional encoding and specialized attention mechanisms, these models can effectively capture both short-term actions and long-term behavioral patterns in B2C environments. The integration of temporal convolution layers with traditional attention mechanisms enables comprehensive analysis of user interactions, leading to improved predictive analytics, customer journey optimization, and personalized experience delivery across digital platforms.
Downloads
References
Ashish Vaswani, et al., "Attention Is All You Need," 31st Conference on Neural Information Processing Systems (NIPS 2017),. [Online]. Available: https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
Suhas Lohit, et al., "Temporal Transformer Networks: Joint Learning of Invariant and Discriminative Time Warping," in IEEE Conference on Computer Vision and Pattern Recognition, 2019. [Online]. Available: https://openaccess.thecvf.com/content_CVPR_2019/papers/Lohit_Temporal_Transformer_Networks_Joint_Learning_of_Invariant_and_Discriminative_Time_CVPR_2019_paper.pdf
Xiao Ding, et al., "Deep Learning for Event-Driven Stock Prediction," Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015). [Online]. Available: https://www.ijcai.org/Proceedings/15/Papers/329.pdf
Bowen Zhang, et al., "Free-floating bike-sharing systems: New repositioning rules, optimization models and solution algorithms," Information Sciences, Volume 600, July 2022, Pages 239-262. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S0020025522002390
Shun-Yao Shih, et al., "Temporal Pattern Attention for Multivariate Time Series Forecasting," Machine Learning, 2019. [Online]. Available: https://arxiv.org/abs/1809.04206
Joseph Enguehard, et al., "Neural Temporal Point Processes For Modelling Electronic Health Records," arXiv preprint, 2020. [Online]. Available: https://arxiv.org/abs/2007.13794
Aatif Jamshed, et al., "Deep learning-based sequential pattern mining for progressive database," Soft Computing, Volume 24, Issue 22, 2020. [Online]. Available: https://dl.acm.org/doi/10.1007/s00500-020-05015-2
Yichao Zhou, et al., "Understanding Consumer Journey using Attention based Recurrent Neural Networks," KDD ’19, August 4–8, 2019, Anchorage, AK, USA. [Online]. Available: https://dl.acm.org/doi/pdf/10.1145/3292500.3330753
Tathagata Das, et al.,"Adaptive Stream Processing using Dynamic Batch Sizing," Electrical Engineering and Computer Sciences, University of California at Berkeley, 2014. [Online]. Available: https://www2.eecs.berkeley.edu/Pubs/TechRpts/2014/EECS-2014-133.pdf
Senzhang Wang, et al., "Deep Learning for Spatio-Temporal Data Mining: A Survey," arXiv preprint arXiv:1906.04928, 2019. [Online]. Available: https://arxiv.org/abs/1906.04928
Prachi Sharma, et al., "A comprehensive evaluation of deep models and optimizers for Indian sign language recognition," Graphics and Visual Computing, Volume 5, December 2021, 200032. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2666629421000152
Olga Fink, et al., "Potential, challenges and future directions for deep learning in prognostics and health management applications," Engineering Applications of Artificial Intelligence, Volume 92, June 2020, 103678. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0952197620301184
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.