Quantile Normalized Neighbor Combinatorial Machine Learning Based Recommendation in Digital Marketing

Abstract

The scientific advancement of the contemporary years has set industries on the move. The advancement in marketing has led to the point where reshaping to digital movements is essential. Even though it appears to be a plunge for marketers, as a matter of fact, all mechanized applications and systems that are designed on the basis of artificial intelligence only reduces the complication of conventional targeting and customization procedure. In several applications, the platforms utilized for online promotion carry algorithms for recognizing the best combinations whereas in other cases, the business establishments or institutions involving digital marketing take advantage to design and execute in-house personalized arrangements. As a case study, a method called, Quantile Normalized Neighbor Combinatorial Learning-based Recommendation (QNNCL-R) is applied for generating new leads that will ultimately become customers (i.e., promoting student higher education to admission branding in our scenario) via twitter dataset. The data obtained from the twitter dataset (i.e., higher education) is fed to the recommendationsystem. Then, the relevant set of features and event labels (i.e., tweets) is selected by Quantile Normalized Chi-square Feature selectionand a Neighbor Combinatorial Learning-based Recommendation algorithm with the best performance is selected for the recommendation process for higher education. QNNCL-R method is compared with other algorithms and indicating that QNNCL-R method performs better than other methods.