Evaluating video search performance relies on metrics that measure relevance, ranking quality, and user engagement. The most common metrics include precision, recall, Mean Average Precision (MAP), and Normalized Discounted Cumulative Gain (NDCG). These metrics help developers assess how well a search system retrieves and ranks videos that match user intent. For example, a search for “tutorial on Python loops” should return relevant instructional videos ranked by usefulness, with minimal irrelevant content.
Precision and recall are foundational. Precision measures the fraction of retrieved videos that are relevant (e.g., if 8 out of 10 results are tutorials, precision is 80%). Recall measures the fraction of all relevant videos in the dataset that were retrieved (e.g., if 20 relevant videos exist and 8 are returned, recall is 40%). However, these metrics alone don’t account for ranking order. MAP addresses this by averaging precision scores across different recall levels, emphasizing the position of relevant results. For instance, if relevant videos appear earlier in the list, MAP increases. NDCG further refines ranking evaluation by weighting higher positions more heavily and normalizing scores against an ideal ranking. This is useful for graded relevance (e.g., a “very relevant” video in position 1 contributes more to the score than a “somewhat relevant” one in position 5).
User-centric metrics like click-through rate (CTR) and watch time are also critical. CTR measures how often users click on search results, indicating perceived relevance. Watch time (e.g., average seconds viewed per video) reflects how well the content matches user needs. For example, a video with high watch time likely aligns with the query’s intent. Developers often combine these metrics with A/B testing to optimize algorithms. For instance, testing two ranking models might reveal that Model A has higher MAP but Model B achieves longer watch times, suggesting a trade-off between relevance and engagement. Together, these metrics provide a comprehensive view of video search effectiveness.