recommendation systems
AI algorithms designed to predict user preferences and suggest relevant items or services, commonly employed in e-commerce and content platforms. Their effectiveness relies on understanding user behavior and product attributes.
- Estimating Hitting Times Locally at Scale
- FACE: A General Framework for Mapping Collaborative Filtering Embeddings into LLM Tokens
- IGD: Token Decisiveness Modeling via Information Gain in LLMs for Personalized Recommendation
- Sampled Estimators For Softmax Must Be Biased
- Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations
- VisualLens: Personalization through Task-Agnostic Visual History
- Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs