基于大数据的互联网商城运营管理系统
By creating a deep buyer database and dynamic evaluation model in the online marketplace, and combining social interaction and search behavior, the problem of low matching accuracy of recommended content in the existing system was solved, enabling personalized and efficient adjustment of recommendation strategies and improving user satisfaction.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHANDONG OUTSTANDING TALENT DEV GRP CO LTD
- Filing Date
- 2025-08-31
- Publication Date
- 2026-07-17
AI Technical Summary
Existing online shopping mall recommendation systems fail to effectively mine product mentions in social relationships, resulting in a low degree of matching between recommended content and users' actual needs. Furthermore, the lack of a dynamic permission management mechanism leads to the loss of high-value users or excessive disturbance from low-value users.
A deep buyer database is created through the deep buyer update module. Buyer permissions are updated regularly using a dynamic evaluation model. Combined with the recommendation acceptance judgment module and the friend recommendation determination module, the recommendation status is adjusted in real time, including the blocking, preparation, and immediate push status. The recommendation strategy is dynamically adjusted based on users' social interactions and search behavior.
It improves the matching degree between recommended content and users' real needs, avoids excessive push notifications, reduces user aversion, and achieves personalized recommendations and efficient resource utilization.
Smart Images
Figure CN121073604B_ABST