To assess video search performance, several evaluation metrics can be used, each targeting different aspects of how well the search system is functioning. One of the most common metrics is Precision, which indicates the proportion of relevant videos retrieved out of all videos returned by the search. For example, if a user searches for "action movies" and the system returns 10 videos, with 6 of them being actual action movies, the precision would be 60%. This metric helps identify how accurately the search results match user expectations and needs.
Another important metric is Recall, which measures the proportion of relevant videos found out of all relevant videos available in the database. Continuing with the earlier example, if there are a total of 20 relevant action movies in the database and the system retrieved 6, the recall would be 30%. Recall is vital for understanding if the search engine is comprehensive in providing useful results. Balancing precision and recall is crucial as focusing too heavily on one can negatively impact the other.
Additionally, Mean Average Precision (MAP) and Normalized Discounted Cumulative Gain (NDCG) are more advanced metrics commonly used in video search evaluation. MAP combines precision and recall across multiple query results, while NDCG also accounts for the ranking of results, making it useful when considering user preferences for higher-ranked items. For instance, if a user is more likely to click on the first few results, NDCG will provide a more nuanced view of search performance, ensuring that the most relevant results are prioritized. Utilizing these metrics allows developers to analyze how effectively their video search systems meet user requirements and enhance retrieval quality.