Seasonal patterns affect user-product embeddings by introducing temporal shifts in user preferences and product relevance, which embedding models must capture to stay accurate. User-product embeddings are vector representations that encode relationships between users and products based on interaction data (e.g., purchases, clicks). Seasonal trends—like holiday shopping, weather-dependent purchases, or back-to-school cycles—alter the distribution of user interactions, causing embeddings to drift over time if not properly accounted for. For example, a user might show strong affinity for winter clothing in December but shift to gardening tools in spring. Embeddings trained on static historical data may fail to reflect these changes, leading to suboptimal recommendations or search results.
To address this, embedding models often incorporate time-aware mechanisms. One approach is to partition training data by season and retrain embeddings periodically. For instance, an e-commerce platform might generate separate embeddings for holiday-season data (November–December) versus summer data (June–August). Another method is to augment embeddings with temporal features, such as month or week of the year, directly in the model architecture. For example, a matrix factorization model could include a time-dependent bias term that adjusts user-product interaction scores based on the season. Temporal attention mechanisms in neural networks can also weight recent interactions more heavily during specific periods. Without such adjustments, a user embedding trained on year-round data might average out seasonal preferences, reducing its ability to predict short-term needs.
Developers must also consider how to balance seasonal signals with long-term preferences. For instance, a user who buys ski gear annually might have a persistent interest in outdoor sports, but their immediate needs vary by season. Hybrid models, such as combining a static embedding (for baseline preferences) with a dynamic seasonal component, can help. A practical implementation could involve a two-tower neural network where one tower processes time-agnostic user features (e.g., demographics) and the other handles time-sensitive features (e.g., recent clicks filtered by season). Additionally, techniques like dynamic negative sampling—prioritizing out-of-season products as negative examples during training—can sharpen seasonal distinctions. For example, during summer, swimsuits would be positive samples for many users, while winter coats might be treated as negatives. These strategies ensure embeddings adapt to seasonal shifts without losing sight of broader user interests.