Abstract
The business owners have significantly focused on improving the business to business (B2B) services to enhance business productivity. Numerous methods have already been proposed for the personalized end users recommendation systems, but there are no such attempts for business owners. The business customers buying processes consists of various marketing campaign requirements to promote their services or products effectively. It requires an intelligent B2B marketing campaigns recommendation system to meet the business goals for business customers. The B2B marketing campaigns recommendations are required to increase the business earning, but complex items and user profiles lead to challenging research problems. To end this, we propose a novel framework for intelligent B2B marketing campaign recommendations using the fuzzy preference (FP) personalized temporal graph (PTG) and low-rank graph reconstruction (LRGR). The proposed model FP-PTG-LRGR consists of three steps such as pre-processing, hybrid preference extractions, and recommendations. We first designed pre-processing algorithm using natural language processing (NLP) to remove the noisy data. The pre-processing is required to enhance the reliability and accuracy of recommendations. After that, we proposed the hybrid behavior analysis technique called FP-PTG model for extracting the business customer’s preferences. FP-PTG aims to represent the buying process information of every business customer accurately. The FP technique is integrated with PTG to overcome the crip/vague preferences that form the FP-PTG model. The B2B marketing campaign order preferences for personalized businesses have effectively represented and merged using FP-PTG. Finally, we applied LRGR to estimate the intelligent B2B marketing campaign recommendations for individual business customers by discovering the unobserved edges in FP-PTG. We have performed experimental studies using publically available B2B marketing datasets, and outcomes claim that FP-PTG-LRGR outperformed existing methods for B2B marketing campaign recommendations. Experimental results of FP-PTG-LRGR show the improvement of precision rate by 11.5%, recall rate by 11.23%, and accuracy by 12.11%.
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Data availability
The datasets generated during and/or analysed during the current study are not publicly available due to ongoing study for PHD research, but are available from the corresponding author on reasonable request.
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Patil, S., Vaze, V. & Agarkar, P. Intelligent business to business (B2B) marketing campaigns recommendation using personalized fuzzy preference temporal graph. J Ambient Intell Human Comput 14, 10219–10233 (2023). https://doi.org/10.1007/s12652-021-03684-x
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DOI: https://doi.org/10.1007/s12652-021-03684-x