What are the best offline evaluation methods for recommendations?

The best offline evaluation methods for recommendation systems typically focus on accuracy, ranking quality, and real-world generalization. These methods use historical interaction data to simulate how well a model might perform in production. Key approaches include accuracy metrics, ranking-based evaluation, and time-aware data splitting, each addressing different aspects of recommendation quality.

First, accuracy metrics like Precision@K, Recall@K, and Mean Average Precision (MAP) measure how well recommendations align with known user preferences. For example, Precision@10 calculates the percentage of top-10 recommended items that a user actually interacted with in the test data. If a user watched 5 movies and the model recommends 3 relevant ones in its top 10, Precision@10 is 30%. MAP extends this by averaging precision scores across all users while emphasizing correct rankings (e.g., rewarding models that place relevant items higher in the list). These metrics are straightforward but may overlook nuances like item diversity or positional bias in rankings.

Second, ranking metrics such as Normalized Discounted Cumulative Gain (NDCG) and Hit Rate evaluate the order of recommendations. NDCG assigns higher scores when relevant items appear at the top of the list. For instance, a recommendation list with a relevant item in position 1 would score higher than one where it’s in position 10. Hit Rate measures whether at least one relevant item exists in the top-N recommendations, which is useful for scenarios like homepage carousels where immediate engagement matters. To ensure realistic evaluation, data should be split temporally (e.g., train on interactions before March 2023, test on interactions after) rather than randomly, as this mimics real-world scenarios where models predict future behavior.

Finally, coverage and diversity metrics help assess whether recommendations are overly narrow or repetitive. Coverage measures the percentage of items in the catalog that the model can recommend, preventing bias toward popular items. Diversity metrics, like intra-list similarity, check how distinct recommended items are from one another (e.g., avoiding suggesting three action movies in a row). For example, a movie recommender might use genre or director metadata to compute similarity between items. While these metrics don’t directly measure accuracy, they ensure the system serves varied user needs and avoids stagnation. Combining these methods provides a balanced view of a model’s effectiveness before deployment.