scenario adaptation

DataFunSummit

Dec 5, 2023 · Artificial Intelligence

Scenario-Adaptive and Self-Supervised Multi-Scenario Personalized Recommendation (SASS)

This article presents a comprehensive study of a scenario‑adaptive and self‑supervised multi‑scenario recommendation model (SASS) for Taobao, detailing its motivation, adaptive multi‑scenario architecture, two‑stage pre‑training and fine‑tuning, experimental validation, deployment in the recall stage, and practical challenges addressed through Q&A.

AlibabaRecommendation systemsmulti-scenario modeling

0 likes · 36 min read

Scenario-Adaptive and Self-Supervised Multi-Scenario Personalized Recommendation (SASS)

DataFunTalk

Apr 10, 2023 · Artificial Intelligence

Scenario-Adaptive and Self-Supervised Multi-Scenario Personalized Recommendation (SASS): Design, Training, and Deployment

This article presents a comprehensive study of multi‑scenario personalized recommendation, introducing a scenario‑adaptive and self‑supervised model (SASS) that jointly addresses data sparsity, domain adaptation, and recall‑stage deployment through a two‑stage training pipeline and extensive experiments on Alibaba’s Taobao platform.

AlibabaRecommendation systemsmulti-scenario modeling

0 likes · 36 min read

Scenario-Adaptive and Self-Supervised Multi-Scenario Personalized Recommendation (SASS): Design, Training, and Deployment

58 Tech

Jan 8, 2021 · Artificial Intelligence

Deep Learning Practices for Multi‑Business Integrated Recommendation: From Dual‑Channel to Multi‑Channel Interest Models and Multi‑Scenario Adaptation

The article details how 58.com tackled the challenges of multi‑business recommendation by evolving its ranking models from a dual‑channel deep interest architecture to a 1+N multi‑channel deep interest model, incorporating customized feature cross layers, scenario‑adaptation mechanisms, and extensive engineering optimizations that yielded significant CTR and conversion gains.

Feature EngineeringMulti-ChannelRanking

0 likes · 27 min read

Deep Learning Practices for Multi‑Business Integrated Recommendation: From Dual‑Channel to Multi‑Channel Interest Models and Multi‑Scenario Adaptation