Thank you so much for watching the 43rd episode of the Weaviate Podcast with Roman Grebennikov and Vesvolod Goloviznin from Metarank, as well as Erika Cardenas from Weaviate! This podcast is a masterclass on Ranking models, additionally touching on the connection between Search and Recommendation. Learning-to-rank is an exciting idea where we use models that produce more fine-grained relevance scores than the offline indexing techniques of vector search and bm25, however with the tradeoff of the speed of these inferences. Romand and Vsevolod touched on another extremely interesting part of these ranking models which is the estimation of features such as Click-through-Rates and how they use streaming technology to do this. I learned so much from this podcast about the directions in ranking, I hope you enjoy it as well! As always, we are more than happy to answer any questions or discuss any ideas with you! In reflecting on this podcast, Erika and I wrote up our latest thoughts on Ranking Models in a Weaviate blogpost, check it out here if interested: https://weaviate.io/blog/ranking-models-for-better-search. Chapters 0:00 Welcome Everyone! 0:40 Recommendation with Weaviate 4:20 Metarank - Founding Story 8:20 Ranking MLOps 9:52 User Friendliness Perspective 15:10 Retrieval vs. Ranking 17:45 Ranking Optimization 25:20 Multi-Vector Object Representations 27:55 Click-through-Rate Feature Streaming 33:06 Weaviate Properties vs. Feature Stores 40:06 Cold-Start Recommendation Problem 46:04 Ranklens Demo - RecSys Datasets 52:02 Cross Encoders