Hello Data Scientists,
A Recommendation System (or Recommender System) is a Machine Learning model that suggests relevant items to users based on their preferences, behavior, or similarity with other users. These systems are commonly used by platforms like Netflix, Amazon, and Spotify to recommend products, movies, or songs. Developing Recommendation Systems with Large Language Models (LLMs) is an emerging approach that leverages the deep understanding of language and user preferences embedded within LLMs. LLMs offer several advantages for building recommendation systems: 1. LLMs excel at capturing and understanding the nuances of language, enabling them to generate recommendations based on rich, user-specific contexts. 2. Unlike traditional systems, LLMs can recommend items to new users by leveraging general world knowledge and user prompts. 3. LLMs can take into account individual user preferences expressed in natural language and suggest highly personalized recommendations.4. LLMs are capable of incorporating diverse knowledge sources (e.g., movies, books, and news) into the recommendation process, providing multi-domain recommendations. This presentation well be covering the whole Recommendation Systems development cycle using LLMs for a movie and patient diagnosis-treatment datasets. Content-Based and Collaborative Filtering will be applying. If you would to see and learn another application of LLMs feel free to join this meetup meeting.
Thanks
Ernest Bonat, Ph.D.