Personalizing Search Using Multimodal Latent Behavioral Embeddings

Explore the cutting-edge approach to personalizing search using multimodal latent behavioral embeddings in this 45-minute conference talk from Haystack US 2024. Delve into the importance of incorporating user context and behavioral signals to optimize search relevance, moving beyond traditional keyword-based and content embedding methods. Learn how to integrate user behavior into modern search retrieval pipelines for RAG and end-user search, combining content, domain, and user understanding for a holistic approach to search relevance. Discover techniques for training embedding models using behavioral signals, implementing personalized search experiences, and applying appropriate contextual guardrails. Gain insights into traditional signals-based models for AI-powered search and their mapping into multimodal embedding approaches. Witness live, open-source code examples demonstrating how modern hybrid search approaches can learn user and group affinities. Benefit from the expertise of Trey Grainger, lead author of "AI-Powered Search" and founder of Searchkernel, as he shares his extensive experience in developing semantic search, personalization, and recommendation systems.