المفاهيم الأساسية
The author explores the challenges of ad recommendation systems, focusing on representation learning to address dimensional collapse and interest entanglement effectively.
الملخص
The paper delves into the complexities of ad recommendation systems, emphasizing the importance of representation learning. It discusses encoding features with priors, tackling dimensional collapse, and managing interest entanglement. Various training techniques and analysis tools are presented to enhance system performance.
The online advertising industry heavily relies on machine learning for accurate prediction of ad click-through rates. Deep learning has been successful in various domains, including recommender systems.
Key points include:
Encoding sequence, numeric, and embedding features with priors is crucial.
Dimensional collapse in embeddings can lead to wasted model capacity.
Interest entanglement across tasks or scenarios requires disentanglement.
Training techniques like ranking loss and weighted sampling improve model performance.
Exploration-exploitation balance is essential for effective ad recommendations.
الإحصائيات
The reported performance is based on an online advertising platform handling billions of requests daily.
GMV lift observed in various scenarios due to different techniques implemented.
اقتباسات
"We propose Temporal Interest Module (TIM) to learn semantic-temporal correlations between user behaviors and targets."
"Multi-Embedding paradigm mitigates dimensional collapse significantly."
"STEM paradigm incorporates task-specific embeddings to disentangle user interests across tasks."