Building user embeddings from browsing behavior involves transforming a user’s interaction data into a numerical representation that captures their preferences and habits. This is typically done by collecting and processing raw event data (like page views, clicks, or search queries), then applying machine learning models to create dense vectors (embeddings) that summarize behavior patterns. For example, if a user frequently visits electronics product pages and spends time comparing prices, their embedding might highlight an interest in tech products and price sensitivity. The key steps include data collection, feature engineering, and model training to map behavior to a vector space.
The process starts with aggregating user activity logs, such as URLs visited, time spent per page, click-through rates, or search terms. These raw events are cleaned and structured into sequences or sessions. For instance, a user’s browsing session could be represented as a sequence of product category IDs (e.g., /electronics, /books) with timestamps. Categorical features like page types or item IDs are often encoded using techniques like one-hot encoding or TF-IDF, while temporal features (e.g., session duration) are normalized. To handle sequences, methods like Recurrent Neural Networks (RNNs) or Transformers can process time-ordered data, while simpler approaches like averaging word embeddings (e.g., Word2Vec) for visited pages might suffice for non-sequential behavior.
Once the data is structured, models like neural networks or matrix factorization generate embeddings. For example, a two-tower neural network could take a user’s browsing history as input and output a 128-dimensional vector. The model is trained to minimize a loss function that encourages similar users (e.g., those who bought the same product) to have closer embeddings. Negative sampling—comparing a user’s behavior against random or dissimilar users—is often used to improve contrast. Libraries like TensorFlow or PyTorch simplify implementing these models. After training, the embeddings can be used for tasks like recommending products, clustering users, or predicting churn. A practical example is an e-commerce platform using embeddings to group users who browse DIY tools, enabling targeted promotions for that segment. Challenges include handling sparse data (users with minimal activity) and updating embeddings efficiently as behavior evolves.