A personalized commodity dynamic recommendation method based on users' real - time behaviors

Abstract

A personalized commodity dynamic recommendation method based on users' real - time behaviors. The behavior tracking module deployed on the client device collects the interaction event data of users in the target application interface at a frequency of not less than once per second. The interaction event data is packaged into a structured behavior log package and pushed in real - time to the behavior processing engine on the server - side through a low - latency message queue. In the data input layer of this invention, the system automatically monitors the changes of the DOM tree through non - intrusive JavaScript hook functions. This ensures that newly inserted commodity elements are bound with event listeners the moment they are first exposed, thus completely capturing micro - interaction behaviors such as hovering duration, sliding speed, and click heat - zone at the millisecond level.