Async Learned User Embeddings for Ads Delivery Optimization
This work addresses ad delivery optimization for large-scale recommendation systems, but it appears incremental as it builds on existing embedding and graph learning techniques.
The paper tackled the problem of improving ad delivery in recommendation systems by asynchronously learning high-quality user embeddings from multimodal activities, resulting in significant gains in offline and online experiments.
In recommendation systems, high-quality user embeddings can capture subtle preferences, enable precise similarity calculations, and adapt to changing preferences over time to maintain relevance. The effectiveness of recommendation systems depends on the quality of user embedding. We propose to asynchronously learn high fidelity user embeddings for billions of users each day from sequence based multimodal user activities through a Transformer-like large scale feature learning module. The async learned user representations embeddings (ALURE) are further converted to user similarity graphs through graph learning and then combined with user realtime activities to retrieval highly related ads candidates for the ads delivery system. Our method shows significant gains in both offline and online experiments.