Best Practices for Data Preprocessing in Recommender Systems
Data preprocessing in recommender systems focuses on improving data quality, handling missing values, and structuring features to enhance model performance. First, address missing or inconsistent data, which is common in user-item interaction datasets (e.g., ratings, clicks). For example, if users rarely rate products, the interaction matrix becomes sparse. Imputation methods like filling missing values with user/item averages or using collaborative filtering-based approximations can mitigate this. However, overly aggressive imputation may introduce noise. Instead, consider removing users or items with extremely sparse interactions (e.g., users with fewer than five interactions). For numerical data like ratings, normalization (e.g., scaling to a 0-1 range) or standardization (centering around the mean) ensures consistent input scales for algorithms like matrix factorization.
Feature engineering is critical for capturing meaningful signals. Categorical variables like user IDs or item categories require encoding—one-hot encoding for small categories or embedding layers in neural networks for high-cardinality features. Time-based features (e.g., timestamp of interactions) can be split into day-of-week or session-based intervals to model temporal trends. For example, Netflix might track binge-watching patterns by aggregating user activity into hourly buckets. Text data (e.g., product descriptions) benefits from TF-IDF or pre-trained embeddings (e.g., Word2Vec) to represent semantic meaning. Additionally, aggregate features like “average time spent per item” or “number of interactions in the last week” provide contextual signals. Always validate engineered features by testing their correlation with target variables (e.g., click-through rates).
Finally, split data strategically to avoid leakage and evaluate effectively. Use time-based splits (e.g., train on older data, test on newer interactions) to simulate real-world scenarios where past behavior predicts future actions. For cold-start problems (new users/items), create holdout sets containing only unseen entities to test generalization. During preprocessing, ensure metadata (e.g., item categories) is consistently available in both training and inference pipelines. For example, if a recommendation model uses genre information for movies, verify that new movies added to the catalog have genre tags. Tools like Apache Spark or pandas can automate validation checks (e.g., ensuring no null values in critical columns). By prioritizing clean data, relevant features, and robust evaluation splits, developers build recommender systems that generalize well and adapt to changing user behavior.