Introduction: The Challenge of Feature Engineering

Building models for recommender systems is exciting, but designing the right features is often a frustrating task. While I enjoy the modeling side, I frequently find myself struggling to come up with useful features. This article explores the challenges of feature engineering, focusing on data leakage, the costs of new features, and the engineering trade-offs we must consider.

Understanding Data Leakage

Data leakage occurs when information from outside the training dataset is used to create the model, giving it an unfair advantage. For instance, consider the MovieLens dataset, which contains explicit features such as userId, movieId, rating, and timestamp. These explicit features can be used to derive implicit features.

• Explicit Features: Directly provided by users, such as a rating score or the exact time a movie was rated.
• Implicit Features: Inferred from these explicit features, reflecting user behavior or preferences indirectly.

Deriving Implicit Features from Explicit Data

Instead of using the explicit rating itself, you could create implicit features where all non-zero ratings are treated as positive feedback (1), and missing or zero ratings as negative feedback (0). Similarly, you could extract temporal patterns from the timestamp feature, such as the day of the week or time of day when ratings are given.

For example, a feature like “number of movies rated on Sunday by a user” could be computed. However, it should be done at a specific point in time to avoid leakage, ensuring that future ratings do not impact past predictions. By carefully designing implicit features from explicit ones, you can avoid leakage while still capturing important user preferences and behavior.

Evaluating New Features: Key Considerations

Feature engineering is a critical but often challenging part of building recommender systems. While browsing through others’ solutions can be helpful, it’s important to evaluate whether a feature fits your specific dataset and problem.

When considering new features, it’s crucial to ask:

• Are they consistently available?
• How reliable are they for future use?
• What’s the cost to compute and store them?

Balancing Model Performance with Engineering Costs

Improving model performance from 50% to 70% on a given focus metric might justify the effort needed to implement a new feature. However, a 0.5% improvement, achieved at a high computational cost, might not be worth the trade-off, especially if the engineering effort is unsustainable long-term.

In feature engineering, it’s essential to balance model improvement with the engineering cost of implementing new features. Avoiding data leakage and carefully considering the practical implications of new features can help build more sustainable and effective recommender systems.

(Pending…)