To train embeddings for recommendation tasks, you typically start by representing users and items as vectors in a low-dimensional space. This is done using algorithms that learn from interaction data, such as user clicks, purchases, or ratings. A common approach is collaborative filtering, where the model identifies patterns in how users interact with items. For example, matrix factorization decomposes the user-item interaction matrix into two smaller matrices: one representing user embeddings and the other item embeddings. These embeddings are optimized so that the dot product of a user and item vector predicts their interaction (e.g., a rating). Neural methods like neural collaborative filtering (NCF) or two-tower models (e.g., user and item towers) can also be used, where embeddings are learned through neural networks trained to minimize prediction errors.
Updating embeddings involves retraining the model with new data to maintain relevance. One approach is periodic batch retraining, where the entire model is retrained on fresh data at fixed intervals (e.g., daily or weekly). This works well for platforms with predictable data patterns but can be resource-intensive. Alternatively, incremental updates can be applied using techniques like online learning. For instance, tools like TensorFlow Extended (TFX) or custom pipelines can process streaming data, adjusting embeddings in near-real time via stochastic gradient descent. For example, if a user starts interacting with new categories of products, their embedding can be updated by feeding the latest interactions into the model without full retraining. Hybrid methods, such as warm-starting retraining with previous embeddings, balance efficiency and accuracy.
Handling dynamic data requires additional strategies. For cold-start items or users (those with no interaction history), auxiliary data like item metadata (e.g., product descriptions) or user demographics can initialize embeddings. For example, a new movie on a streaming platform might use genre, director, or keywords to generate an initial embedding. Graph-based methods, like Graph Neural Networks (GNNs), can also update embeddings by propagating information through user-item interaction graphs. Tools like PyTorch Geometric simplify this by modeling interactions as edges in a graph. Finally, evaluation is critical: metrics like recall@k or A/B tests ensure embeddings remain effective after updates. For instance, an e-commerce platform might track whether updated embeddings improve click-through rates for new users.