👉 Recommendation math involves using mathematical techniques to predict user preferences and suggest items that align with those preferences. It typically starts with a model that learns from historical data, such as user ratings or interactions, to estimate the likelihood of a user liking an item. Common approaches include collaborative filtering, which finds similarities between users or items, and content-based filtering, which recommends items similar to those a user has liked in the past. More advanced methods, like matrix factorization or neural networks, can capture complex patterns and improve recommendation accuracy. The goal is to balance personalization with diversity, ensuring users are presented with relevant options while also discovering new items they might enjoy.
