Preprocessing a dataset for recommender systems involves cleaning, transforming, and structuring data to make it suitable for training models. Start by handling missing values, duplicates, and outliers. For example, if user ratings are missing, you might fill gaps with a median value or exclude incomplete records. Next, encode categorical data like user IDs or item categories into numerical formats. One-hot encoding or label encoding can convert movie genres (e.g., “Action” or “Comedy”) into binary vectors. Normalize numerical features such as ratings (e.g., scaling 1–5 stars to 0–1) to ensure consistent input ranges for algorithms. This step is critical for collaborative filtering models, which rely on user-item interaction matrices.
The second step focuses on feature engineering and creating meaningful representations. For explicit feedback (e.g., star ratings), structure data into a user-item matrix where rows represent users, columns represent items, and cells contain interaction values. For implicit feedback (e.g., clicks or views), convert interactions into binary values (1 for interaction, 0 otherwise). Generate user-specific features like average rating frequency or item-specific features like release year. If timestamps are available, derive time-based features (e.g., day of week) to capture temporal patterns. For text-based data (e.g., movie descriptions), use techniques like TF-IDF or embeddings to extract semantic features. These steps help models identify patterns, such as users preferring certain genres during weekends.
Finally, split the data into training, validation, and test sets. Use time-based splits for temporal datasets (e.g., train on older data, test on recent interactions) or stratified sampling to preserve user-item distributions. Address the “cold start” problem by reserving a subset of new users/items for testing how well the model handles unseen data. Use libraries like pandas for data manipulation, scikit-learn for scaling/encoding, and Surprise or TensorFlow for building recommendation models. For large datasets, consider dimensionality reduction (e.g., PCA) or sparse matrix formats to improve efficiency. Proper preprocessing ensures your model can learn meaningful patterns and generalize well to real-world scenarios.