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Beyond “People Also Liked”: Building Intelligent Food Recommenders with LLMs | by Faisal Hussain Sabir | Aug, 2025 | Medium

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It’s 7 PM. You’re hungry, you open your favorite food delivery app, and you’re greeted with: “Because you ordered pizza, you might like… and more pizza options.” This is the fundamental limitation of traditional recommendation systems. In our diverse culinary landscape, user cravings are nuanced, contextual, and deeply personal. Queries like “I want something comforting but healthy,” “I’m craving the spicy street food I had on vacation,” or “I need a vegan dessert for a celebration” remain incomprehensible to collaborative filtering and matrix factorization approaches. These conventional systems rely exclusively on historical user-item interactions, creating several critical limitations: Large Language Models like GPT-4, Claude, and LLaMA represent far more than advanced chatbots. They are sophisticated reasoning engines capable of understanding semantics, context, and nuance — making them ideal for revolutionizing recommendation systems. Unlike traditional approaches limited by histor

Beyond “People Also Liked”: Building Intelligent Food Recommenders with LLMs Faisal Hussain Sabir 8 min read · Aug 24, 2025 -- Listen Share Press enter or click to view image in full size The Modern Dining Dilemma It’s 7 PM. You’re hungry, you open your favorite food delivery app, and you’re greeted with: “Because you ordered pizza, you might like… and more pizza options.” This is the fundamental limitation of traditional recommendation systems. In our diverse culinary landscape, user cravings are nuanced, contextual, and deeply personal. Queries like “I want something comforting but healthy,”

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