To implement hierarchical embeddings, you need a structure that captures relationships between entities at different levels of abstraction. Hierarchical embeddings are useful when dealing with data that has nested categories (e.g., product taxonomies, organizational charts) where higher-level categories influence lower-level ones. The core idea is to train embeddings that reflect both the unique features of an entity and its position within the hierarchy.
Start by defining the hierarchy and how embeddings will interact across levels. For example, in a product taxonomy like "Electronics > Computers > Laptops," each level (Electronics, Computers, Laptops) could have its own embedding. Lower-level embeddings should inherit or combine information from their parent categories. One common approach is to concatenate or sum the embeddings of a child node with those of its parent. For instance, the embedding for "Laptops" could be the sum of its own learned embedding and the embedding of its parent "Computers." This ensures that child embeddings retain context from higher levels. Use a neural network architecture where each hierarchy level has its own embedding layer, and child embeddings are computed using a function (e.g., addition, matrix multiplication) that incorporates parent embeddings.
Implement this in code using frameworks like PyTorch or TensorFlow. Suppose you have a three-level hierarchy. Create embedding layers for each level: embedding_level1, embedding_level2, and embedding_level3. For a given item, fetch its level 1 embedding, then compute the level 2 embedding by combining the level 1 embedding with the level 2’s unique embedding (e.g., level2_embedding = embedding_level2(id) + level1_embedding). Repeat this for level 3. During training, ensure the loss function accounts for all hierarchy levels. For example, use a multi-task loss where predictions are made at each level, and errors propagate through the entire hierarchy. Regularization techniques like dropout or weight decay can help prevent overfitting, especially if some hierarchy levels have sparse data.
Consider practical adjustments. If the hierarchy is deep, use normalization (e.g., LayerNorm) to stabilize training when combining embeddings. Experiment with weighting mechanisms: higher-level embeddings might contribute less to child embeddings as you go deeper. For example, instead of simple addition, use a learned gate (e.g., gate * parent_embedding + (1-gate) * child_embedding) to control parental influence. If your data includes multiple hierarchies (e.g., a product belongs to both "Electronics" and "Black Friday Sale"), create separate embedding chains for each hierarchy and concatenate the results. Test the model by evaluating performance at each hierarchy level—for instance, accuracy for top-level categories and finer-grained metrics for lower levels. Libraries like gensim or Hugging Face’s transformers can provide baseline embeddings, but custom hierarchies often require building the architecture from scratch.