Product and user data are represented as vectors by converting their attributes into numerical arrays where each dimension corresponds to a specific feature. For products, features might include price, category, or technical specifications. For users, features could be age, purchase history, or interaction patterns. These vectors enable algorithms to process and compare data points mathematically. For example, a product vector might look like [price=99.99, category=electronics, weight=1.2kg], while a user vector could be [age=30, total_purchases=15, last_login=7_days_ago]. The key is to structure these attributes in a way that captures meaningful patterns for machine learning tasks.
The process often involves techniques like one-hot encoding, normalization, or embeddings. One-hot encoding converts categorical data (e.g., product categories like “books” or “clothing”) into binary vectors where each category becomes a separate dimension. Normalization scales numerical values (e.g., user age or product price) to a standard range, ensuring features contribute equally during analysis. For more complex relationships, embeddings (dense vectors learned via models like matrix factorization or neural networks) map high-dimensional data into lower-dimensional spaces. For instance, collaborative filtering in recommendation systems creates user and product embeddings by analyzing interactions (e.g., user A bought product X), resulting in vectors that capture latent similarities between users and items.
These vector representations are foundational for tasks like recommendation systems, search, and personalization. For example, in a recommendation engine, calculating the dot product between a user’s embedding and product embeddings identifies items the user might prefer. In search, product vectors allow algorithms to rank results by comparing a query’s vector (e.g., “budget laptops”) to product features. Developers often use libraries like Scikit-learn for basic feature engineering or PyTorch/TensorFlow for training custom embeddings. The choice of method depends on the problem: simple systems might use handcrafted feature vectors, while complex ones leverage learned embeddings to capture nuanced patterns in large datasets.