Recommender systems handle dynamic data by continuously updating their models and incorporating real-time user interactions to maintain relevance. Unlike static datasets, dynamic data sources—like user clicks, purchases, or trending content—require systems to adapt quickly to changes in user behavior, item popularity, or contextual factors (e.g., time of day). To achieve this, most systems combine periodic batch updates with real-time processing. For example, streaming frameworks like Apache Kafka or Flink are used to ingest clickstream data, which triggers immediate model adjustments. Meanwhile, batch processes might retrain models nightly on full datasets to capture longer-term trends.
A key technique is incremental learning, where models update incrementally instead of retraining from scratch. Collaborative filtering models, for instance, might update user-item interaction matrices in real time as new ratings arrive. Matrix factorization algorithms can be adjusted to weigh recent interactions more heavily. Session-based recommenders, common in e-commerce, prioritize short-term user behavior within a single browsing session by tracking clicks or cart additions. For example, if a user starts searching for hiking gear, the system might temporarily boost outdoor-related recommendations, even if their historical data suggests a preference for cooking content. Hybrid approaches often blend real-time signals (e.g., current search queries) with historical data to balance immediacy and accuracy.
To handle data drift—like sudden shifts in user preferences during holidays—systems often employ automated retraining pipelines. Netflix, for instance, updates its recommendations hourly by combining real-time watch data with batch-processed user profiles. Cold-start scenarios (new users or items) are addressed using temporary rules, like recommending popular items until enough data is collected. Additionally, some systems use contextual bandits to test new recommendations in real time and adjust based on immediate feedback. These strategies ensure recommendations stay relevant despite constantly changing input, though they require careful engineering to balance latency, computational cost, and accuracy.