What is the role of recall in evaluating recommender systems?

Recall measures a recommender system’s ability to identify and present all relevant items to a user. In simple terms, it answers the question: Of the items a user would actually find useful, how many did the system successfully include in recommendations? For example, if a streaming service has 20 movies a user would enjoy, and the system recommends 15 of them, the recall is 75%. High recall ensures that users don’t miss out on items they care about, which is critical for building trust and satisfaction. Without sufficient recall, the system risks overlooking key items, leading to a fragmented or incomplete user experience. This is especially important in domains like e-commerce or content platforms, where users expect a broad selection of relevant options.

Recall often competes with precision, which focuses on minimizing irrelevant recommendations. For instance, a system that recommends 100 items (including all 20 relevant ones) achieves perfect recall but might have low precision if most recommendations are unhelpful. Conversely, a system recommending only 5 highly relevant items out of 20 would have high precision but poor recall. Developers must balance these metrics based on use cases. In news recommendation, high recall might prioritize surfacing all important stories, even with some noise. In contrast, a luxury retail platform might prioritize precision to avoid cluttering recommendations with irrelevant products. The choice depends on whether the goal is breadth (recall) or accuracy (precision) of suggestions.

Practical implementation of recall requires defining what “relevant” means. For example, in a music app, relevance could be tracks a user listened to repeatedly, skipped rarely, or explicitly liked. However, calculating recall becomes challenging with large item catalogs, as checking all possible relevant items is computationally expensive. To address this, developers often sample a subset of items or use offline evaluation with historical data. Techniques like collaborative filtering or hybrid models (combining user behavior and item metadata) can improve recall by diversifying recommendations. For example, Netflix might use genre-based filters alongside viewing history to ensure niche content isn’t overlooked. Ultimately, recall helps developers ensure their systems serve the user’s full range of interests, not just the most obvious ones.