Personalized Marketing and Recommendation Systems on TikTok

Abstract

Given the proliferation of data, personalized marketing and recommendation algorithms have become essential components of digital platform marketing. The paper examines the terrain of customized marketing and recommendation systems in the digital age, specifically concentrating on TikTok. The study utilizes a literature review method to clarify the core principles and mechanisms that form the basis of TikTok's recommendation algorithms. The importance of this research is in identifying the effects of tailored marketing methods on user engagement and satisfaction. The study explores how TikTok combines content-based and collaborative filtering methods, shedding light on the issues presented by content similarity and the platform's unique solutions. The methodological framework includes the analysis of data such as user engagement, click-through rates, and feedback channels to assess the efficacy of tailored content. This study offers valuable insights into improving recommendation algorithms, tackling ethical issues, and adjusting to changing user preferences.