The Differences in the Commercialization Paths of the Recommendation Algorithms of Douyin and Taobao

Recommendation algorithms have become a key driving force for the commercialization of platforms in the contemporary digital economy, profoundly influencing user experience and revenue generation methods. E-commerce platforms reshape consumption patterns by relying on massive user data and commodity resources. This article uses a comparative analysis method to examine the different commercialization paths of recommendation algorithms on content platform Douyin and e-commerce platform Taobao. Research shows that the Douyin algorithm takes models such as graph neural networks (GNN) as its core. Its primary optimization goal is to increase user dwell time and interaction rate to accumulate traffic value. Subsequently, it achieves commercialization through native advertising and the "content seeding combined with live-streaming sales" model. Taobao's algorithm is based on collaborative filtering and transformer architecture, directly and precisely optimizing the transaction conversion rate (GMV) by "goods finding people". Its commercialization mainly relies on advertising monetization and transaction commissions. This article delves deeply into the causes of the differences, attributing them to the fundamental differences in the essence of the platform ecosystem, the logic of user behavior, the iterative paths of algorithms, and the priorities of business goals. This research can deeply analyze the coupling relationship between technical design and business strategy, providing decision-making references for platform operators and algorithm designers.