Implementing a Transfer Learning for User Behavior Analysis and Prediction Using Preference-Dependent Model

Abstract

The modelling and forecasting of personal conduct depend on the records that individuals contribute at some stage in the shared network. It is essential to conduct this analysis to forecast the pursuits, feelings, and personal options, ultimately resulting in a development in service efficiency. However, to get accurate predictions, it’s far essential to triumph over the mission of information segregation and retaining precision in evaluation. This paper offers the preference-based predictive behaviour analysis (PPBA) approach to address this problem. This approach aims to achieve the best possible accuracy stage in the desired identity. A study primarily based on analysis is done on mutated and segregated data in the proposed methodology, which uses transfer learning. A more sophisticated understanding of consumer options is made possible through the system of segregation, which is completed by studying desire deviations from the attitude of many inputs. Diverse mutations in deviation sites are recognized during the learning method, which aligns options for various statistics. State validations are completed primarily based on an individual’s preceding options and any novel deviations observed inside the present state of affairs. This involves figuring out novel deviations from preceding behaviours, which can be rooted in various person options, which, in the long run, results in the refinement of user conduct prediction and modelling across quite a few applications primarily based on social networks. The proposed PPBA achieves 14.82% high detection accuracy for different input values, 9.41% less analysis time, 7.49% less false positives, 9.5% less complexity, and 10.34% high preference ratio.

Keywords

• Behavior analysis
• information processing
• data analysis
• transfer learning
• user preferences