Nov 25, 2019 · Artificial Intelligence

Real-time Attention-based Look-alike Model for Recommender Systems

This talk presents a real-time attention-based look‑alike model (RALM) designed to address the long‑tail problem in recommendation systems by efficiently expanding seed users, leveraging user representation learning, attention mechanisms, and clustering to deliver timely, diverse content without retraining the model.

Long Tailattentionclustering

0 likes · 24 min read

Real-time Attention-based Look-alike Model for Recommender Systems

Tencent Cloud Developer

Aug 9, 2019 · Artificial Intelligence

Real-time Attention-based Look-alike Model (RALM) for Recommender Systems

The Real‑time Attention‑based Look‑alike Model (RALM) converts recommendation to a user‑user problem by representing items with aggregated seed‑user embeddings, employs shared projection, local and global attention towers, and enables instant, diverse, high‑CTR recommendations without retraining, as demonstrated by its deployment in WeChat “Look‑at”.

look-alikereal-time attentionrecommendation system

0 likes · 13 min read

Real-time Attention-based Look-alike Model (RALM) for Recommender Systems