Technical implementation of real-time user profiling
E-commerce recommender system needs second update user behavior analysis, which is difficult to meet with traditional solutions. With Tinybird can be built:
- Behavioral event stream processing: Real-time ingestion of events such as views/add-ons via Kafka
- Dynamic labeling calculations: Calculating Interest Preferences for the Last Hour Using the SQL Window Function
- AB Test Integration: Dynamically return recommendation results for different strategies at the API layer
Key realization steps:
- Define the user event table structure: contains
user_id,event_type,item_id,timestampetc. fields - Write real-time aggregated queries:
SELECT item_category, COUNT() as freq FROM events WHERE user_id = {{uid}} AND timestamp > now() - 3600 GROUP BY item_category ORDER BY freq DESC LIMIT 3 - Interfacing with recommender systems: front-end calls per second
/v1/user_profile?uid=123Get the latest portrait
Actual cases show that after a cross-border e-commerce company used the program, the recommended click-through rate increased by 37% and GMV grew by 21%.
This answer comes from the articleTinybird: a platform for rapidly building real-time data analytics APIsThe
May not be reproduced without permission:AI productivity tools " How to apply Tinybird for real-time user profiling in e-commerce personalized recommendation scenarios?
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