Ever wonder how AI knows what you want before you do? Recommender systems, the AI behind those uncanny suggestions for movies, products, or even friends, have become incredibly sophisticated. But they often operate as black boxes, leaving users in the dark about the 'why' behind the recommendations. New research introduces XRec, a framework that leverages the power of large language models (LLMs) to explain AI's reasoning. Imagine clicking on a recommended product and instantly seeing an explanation like, 'You might like this based on your past purchases of similar items and positive reviews of products from this brand.' XRec makes this possible by combining collaborative filtering, the method that identifies users with similar tastes, with LLMs’ ability to understand and generate human language. It works by creating a 'collaborative relation tokenizer' that transforms complex user-item relationships into a format LLMs can understand. A 'collaborative information adapter' then fine-tunes the LLM to align with user preferences, creating a powerful system that understands both 'what' to recommend and 'why'. Testing XRec on datasets from Amazon, Yelp, and Google, researchers found it generates more comprehensive and unique explanations than existing methods. Importantly, XRec works well even with limited user data, addressing the 'cold start' problem that plagues new users and items. The ability to explain recommendations isn't just about transparency. It builds trust, empowers users to make informed decisions, and allows developers to refine their recommendation models. While currently limited to text and graph data, future versions of XRec could incorporate visual information from images and videos, further enriching the 'why' behind AI's choices.