The Great Convergence: Recommendation, Search, and Conversation in AI

By doc. Pavel Kordík (FIT CTU in Prague, Recombee): For most of their history, recommendation, search, and conversational interfaces have been built as separate systems, with distinct architectures, evaluation methods, and engineering teams. This separation is rapidly disappearing. Embeddings, foundation-model backbones, and large language models are pushing these three paradigms toward a single underlying stack, in which a query, a click, a scroll, and a natural-language request are increasingly different entry points into the same retrieval-and-ranking machinery. This talk examines what such convergence means in practice for the applications we build, and the new challenges it brings, both theoretical and practical. On the theoretical side, unifying interaction-based and semantic signals raises hard questions about evaluation, cold-start behavior, bias and alignment. On the practical side, deploying these systems in the real world forces an uncomfortable trade-off: they must be not only accurate, but also fast and economically viable at the scale of hundreds of millions of users — a constraint that reshapes architectural decisions far more than benchmark leaderboards suggest. Drawing on experience from both academic research and the production systems behind Recombee. Slides: https://drive.google.com/file/d/1BvM1... Meetup page: https://www.meetup.com/miton-times/