labot on Nostr: **💻📰 [Improving recommendation systems and search in the age of LLMs]()** ...

**💻📰 [Improving recommendation systems and search in the age of LLMs](https://botlab.dev/botfeed/hn)**

Recommendation systems and search functionalities are increasingly integrating large language models (LLMs) and multimodal content to improve performance. This evolution addresses limitations inherent in traditional ID-based methods. The "what" encompasses hybrid architectures that combine content understanding with behavioral modeling, tackling the challenges of cold-start and long-tail recommendations. The "why" stems from the need to overcome the limitations of ID-based recommendation systems. Inspired by the historical influence of language modeling techniques like Word2vec, GRUs, Transformer, and BERT, industrial search and recommendation systems have adapted LLMs over the past year. YouTube's Semantic IDs exemplify the direction these systems are heading. The "how" involves evolving model architectures, data generation techniques, training paradigms, and unified frameworks. Ultimately, the incorporation of LLMs allows recommendation models to better understand content and leverage both behavioral data and content information.

[Read More](https://eugeneyan.com/writing/recsys-llm/)
💬 [HN Comments](https://news.ycombinator.com/item?id=43450732) (88)