主管单位：新加坡聚知刊出版有限公司
主办单位：新加坡聚知刊出版有限公司
ISSN: 3041-0797
EISSN: 3041-0843
中文名：《全球视野研究》
-
投稿咨询：
袁编QQ：898565603 ；张编QQ：3555095919
-
稿件查询：
王编QQ：356725673 ；吴编QQ：1807295783
- 工作时间：早上9点——下午5点(周日除外)
当前位置:首页 > 学术展示
Theory and Practice of Artificial Intelligence
Recommendation System in Precision Marketing--Taking
TikTok as an Example
Li Yingying
Liaoning University of International Business and Economics, Dalian Liaoning，116000；
Abstract:The artificial intelligence recommendation system has reconstructed the precision marketing paradigm by relying on
big data models, and theTikTok e-commerce platform is a pioneer among them. TikTok's intelligent recommendation system
drives four major innovations: intelligent product selection, intelligent marketing, intelligent shopping guide and intelligent
customer service. However, the system faces problems such as increased information cocooning, algorithmic bias and user privacy anxiety. The optimization path needs to integrate technological improvement, institutional transparency and user empowerment, and promote the transformation of algorithms from "efficiency tools" to "value coordinators", so as to achieve a
balance between commercial value and social well-being. Keywords：AI recommendation system；precision marketing；TikTok；intelligent marketing
DOI:10.69979/3041-0843.25.02.004
1 Introduction
In the current rapid development of digital economy, artificial intelligence (AI) recommendation system has b
ecome the core driving force in the field of digital marketing. The 2025 AI Marketing New Paradigm Application
Guide shows that the core market size of China’s AIGC industry was 47.17 billion yuan in 2024, and the AI ma
rketing industry is expected to reach a market size of 66.9 billion yuan in 2025.Behind this data is the complete
reconstruction of the marketing ecosystem by AI. This technological innovation not only re-configures the marke
ting logic of “People-Goods-Place”, but also gives birth to a new business ecology of “algorithm as channel”. As a phenomenal platform in the field of short videos, TikTok relies on the recommendation algorithm that ha
ndles 20 billion user interactions per day, and successfully builds an accurate marketing network covering 600 mi
llion daily users.Therefore, the technical implementation and theoretical innovations behind it need to be systema
tically researched. Nowadays, when algorithms increasingly dominate the digital business ecology, this research is valuable for p
romoting the sustainable development of intelligent marketing. At the theoretical level, this paper will deconstruc
t the coupling mechanism between intelligent recommendation system and precision marketing theory,and explo
re how to integrate the principles of user profiling and recommendation algorithm with consumer behavior theor
y and personalized marketing theory, so as to provide theoretical support for brands to establish precision marke
ting strategies based on the characteristics of recommendation system. At the practical level, this paper will prov
ide a replicable application paradigm for the precision marketing practice of brands in the era of digital e-comm
erce by deconstructing the algorithmic operation mechanism and marketing cases of the Jitterbug platform. At th
e same time, it will provide methods for the optimization of AI recommendation system in precision marketing
against the existing drawbacks.
Recommendation System in Precision Marketing--Taking
TikTok as an Example
Li Yingying
Liaoning University of International Business and Economics, Dalian Liaoning，116000；
Abstract:The artificial intelligence recommendation system has reconstructed the precision marketing paradigm by relying on
big data models, and theTikTok e-commerce platform is a pioneer among them. TikTok's intelligent recommendation system
drives four major innovations: intelligent product selection, intelligent marketing, intelligent shopping guide and intelligent
customer service. However, the system faces problems such as increased information cocooning, algorithmic bias and user privacy anxiety. The optimization path needs to integrate technological improvement, institutional transparency and user empowerment, and promote the transformation of algorithms from "efficiency tools" to "value coordinators", so as to achieve a
balance between commercial value and social well-being. Keywords：AI recommendation system；precision marketing；TikTok；intelligent marketing
DOI:10.69979/3041-0843.25.02.004
1 Introduction
In the current rapid development of digital economy, artificial intelligence (AI) recommendation system has b
ecome the core driving force in the field of digital marketing. The 2025 AI Marketing New Paradigm Application
Guide shows that the core market size of China’s AIGC industry was 47.17 billion yuan in 2024, and the AI ma
rketing industry is expected to reach a market size of 66.9 billion yuan in 2025.Behind this data is the complete
reconstruction of the marketing ecosystem by AI. This technological innovation not only re-configures the marke
ting logic of “People-Goods-Place”, but also gives birth to a new business ecology of “algorithm as channel”. As a phenomenal platform in the field of short videos, TikTok relies on the recommendation algorithm that ha
ndles 20 billion user interactions per day, and successfully builds an accurate marketing network covering 600 mi
llion daily users.Therefore, the technical implementation and theoretical innovations behind it need to be systema
tically researched. Nowadays, when algorithms increasingly dominate the digital business ecology, this research is valuable for p
romoting the sustainable development of intelligent marketing. At the theoretical level, this paper will deconstruc
t the coupling mechanism between intelligent recommendation system and precision marketing theory,and explo
re how to integrate the principles of user profiling and recommendation algorithm with consumer behavior theor
y and personalized marketing theory, so as to provide theoretical support for brands to establish precision marke
ting strategies based on the characteristics of recommendation system. At the practical level, this paper will prov
ide a replicable application paradigm for the precision marketing practice of brands in the era of digital e-comm
erce by deconstructing the algorithmic operation mechanism and marketing cases of the Jitterbug platform. At th
e same time, it will provide methods for the optimization of AI recommendation system in precision marketing
against the existing drawbacks.
