0 views collected around this technical thread.
This talk explores how 360's advertising recommendation system leverages language models—from BERT to large‑scale LLMs—to improve user interest modeling, feature extraction, and conversion‑rate prediction, detailing practical challenges, engineering solutions, experimental results, and future research directions.
This article reviews recent advances in user representation learning for recommender systems, covering self‑supervised pre‑training, lifelong learning, multi‑task modeling, and large‑scale contrastive methods, and provides code and dataset links for key papers such as PeterRec, Conure, DUPN, ShopperBERT, PTUM, UPRec, and LURM.
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.
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”.