Imagine an AI trying to figure out what movie you'd like next. It can read titles and descriptions just fine, but what about your five-star rating of 'Taxi Driver' or the fact you watched it back in 1999? Traditional AI struggles with these numerical details. New research introduces ELCoRec, a clever system that teaches AI to appreciate numbers like ratings and timestamps. It uses a specialized network (a Graph Attention Network) to understand the relationships between users, items, and their features, including those crucial numerical details. This 'numerical co-propagation' helps the AI grasp not just what items are similar, but also how your preferences change over time, leading to more relevant recommendations. ELCoRec's innovation lies in its efficient use of this extra information. Instead of overwhelming the AI with too much text, it injects the numerical understanding in a compact, digestible format. This means better recommendations without slowing things down. Plus, it's remarkably data-efficient, learning from fewer examples than existing methods. The researchers tested ELCoRec on movie and book datasets, showing significant boosts in prediction accuracy. They also cleverly visualized the AI's learning process, demonstrating how it forms a richer understanding of your tastes. While this research focuses on recommendations, it suggests a broader shift in AI. By connecting the world of language with the world of numbers, we can build AI systems that are not only smart but also attuned to the subtleties of human behavior. As AI increasingly influences our choices, ELCoRec's approach promises more personalized and relevant experiences, creating a smarter future where technology understands us a little better each day.