Maria: Multi-Scenario Ranking with Adaptive Feature Learning
Maria is a multi‑scenario ranking framework that adaptively learns features across heterogeneous e‑commerce query types—visual search, similar‑product search, and interest search—by employing Feature Scaling, Feature Refinement, and Feature Correlation Modeling modules, achieving superior performance and reducing the seesaw effect on the Ali‑CCP and Alimama datasets.
This paper proposes Maria, a multi-scenario ranking model with adaptive feature learning for heterogeneous query modalities in e-commerce search. The model addresses the challenge of modeling both commonality and differences across multiple search scenarios (visual search, similar product search, and interest search) through three key modules: Feature Scaling (FS), Feature Refinement (FR), and Feature Correlation Modeling (FCM). Experimental results on both Ali-CCP and Alimama datasets demonstrate superior performance compared to existing methods, effectively mitigating the seesaw effect in multi-scenario learning.
Signed-in readers can open the original source through BestHub's protected redirect.
This article has been distilled and summarized from source material, then republished for learning and reference. If you believe it infringes your rights, please contactand we will review it promptly.
Alimama Tech
Official Alimama tech channel, showcasing all of Alimama's technical innovations.
How this landed with the community
Was this worth your time?
0 Comments
Thoughtful readers leave field notes, pushback, and hard-won operational detail here.