Profiilivektori

Profiilivektori is a numerical vector that represents the attributes of a profile, typically a user profile or an organizational profile, in a fixed-length format. The vector is designed to enable efficient computation of similarities, predictions, and classifications in data-driven systems. Profiilivektorit may encode both explicit information (age, location, stated interests) and implicit signals (past interactions, clicks, purchases).

Construction methods combine various feature types. Categorical attributes can be encoded with one-hot vectors or learned embeddings, numeric attributes can be scaled, and interaction features can be created. Techniques used to obtain profiilivektori include matrix factorization, dimensionality reduction (such as PCA), autoencoders, and neural network-based embeddings. In some systems, profile vectors are updated continuously as new data arrives.

Applications of profiilivektorit are central to recommender systems and personalized search, where similarity between profiles guides suggestions. They are also used in targeted advertising, segmentation, fraud detection, and cross-system identity matching. They facilitate fast retrieval and scalable machine learning by providing a fixed-size representation.

Challenges and considerations include the need for careful feature design to avoid biases and preserve privacy. Data sparsity, non-stationarity, and concept drift can degrade vector quality. Interpretability is often limited, and there are regulatory and ethical considerations around using personal data.

In Finnish-language technical contexts, profiilivektori is used to refer to profile embeddings employed in analytics and personalization pipelines, and is related to terms such as profile embedding and user embedding.