Embeddings handle mixed data types by converting each type into a numerical vector representation in a shared space, enabling machine learning models to process them together. The core idea is to transform diverse data—like text, categorical variables, or numerical features—into dense vectors where similar values are closer in the vector space. For example, text might be tokenized and embedded using techniques like Word2Vec or BERT, while categorical data (e.g., product categories) can be mapped to embeddings via lookup tables. Numerical data might be normalized or directly used as part of the vector. By unifying these representations, embeddings allow models to learn relationships across data types, such as associating a user’s age (numerical) with their purchase history (text) or location (categorical).
To illustrate, consider a dataset containing customer information: age (numerical), city (categorical), and product reviews (text). The numerical age can be scaled to a fixed range (e.g., 0-1) and treated as a single dimension. The city might be converted using an embedding layer that maps each unique city to a vector (e.g., 5 dimensions). The review text could be processed with a pre-trained language model to generate a 10-dimensional vector. These three vectors are then concatenated into a single 16-dimensional input for a recommendation model. This approach avoids issues like one-hot encoding sparsity for categorical data and captures semantic meaning in text. For mixed data in tabular formats, frameworks like TabTransformer or custom neural networks often apply separate embedding layers per data type before merging them.
Challenges arise in balancing the contributions of different embeddings and handling missing data. For instance, if text embeddings dominate due to higher dimensionality, models might underutilize numerical features. Techniques like feature scaling, dimensionality reduction (e.g., PCA), or attention mechanisms can help weigh embeddings appropriately. Missing values might be addressed by masking or using default embeddings (e.g., a zero vector for absent text). Developers must also decide whether to train embeddings from scratch (for task-specific data) or use pre-trained ones (for common text/categories). Testing different embedding sizes and fusion strategies (concatenation, summation) is critical to optimize performance for specific use cases.