To evaluate semantic search quality, developers should use a combination of traditional information retrieval metrics, semantic-specific measures, and human evaluation. These metrics help assess how well the search system retrieves results that match the user’s intent, not just keyword overlap. Below, we’ll break down practical metrics and their applications.
First, consider traditional retrieval metrics adapted for semantic contexts. Precision@k (the fraction of relevant results in the top-k results) and Recall@k (the proportion of all relevant results found in the top-k) are foundational. For example, if a user searches for “affordable winter jackets,” Precision@5 measures how many of the top five results are truly relevant to budget-friendly options, even if the results don’t include the exact keyword “affordable.” Mean Average Precision (MAP) and Normalized Discounted Cumulative Gain (NDCG) are also useful for evaluating ranked lists. NDCG, for instance, weighs higher-ranked relevant results more heavily, which aligns with real-world user behavior where top results matter most. These metrics require labeled relevance judgments (e.g., annotators tagging results as “relevant” or “irrelevant”), which can be time-consuming but provide objective benchmarks.
Second, use semantic similarity metrics to measure alignment between queries and results. Embedding-based measures like cosine similarity between query and result embeddings (e.g., from models like BERT or Sentence-BERT) quantify how closely the meaning of results matches the query. For example, a search for “movies about space exploration” might return results with embeddings close to terms like “sci-fi,” “astronauts,” or “interstellar travel.” Tools like FAISS or Annoy can help compute these similarities efficiently. Another option is to use ROUGE or BERTScore, typically used for text generation, to compare overlap in semantic concepts rather than exact words. However, these scores should be paired with retrieval metrics, as high similarity alone doesn’t guarantee relevance—for example, a semantically similar result might still be off-topic.
Finally, incorporate human evaluation and task-specific success criteria. Even the best automated metrics can’t fully capture context or subjective relevance. Use A/B testing to compare user engagement (e.g., click-through rates, time spent) between different search configurations. For domain-specific applications, define custom success metrics. In an e-commerce search, this might include conversion rates for product searches or support ticket resolution rates for help-document searches. Additionally, conduct qualitative surveys or ask annotators to rate results on a scale (e.g., 1–5) for graded relevance. For example, in a legal document search, experts might rate whether a result addresses the specific legal precedent mentioned in the query. Combining automated metrics with human judgment ensures a balanced evaluation of both technical performance and real-world usability.
By blending these approaches, developers can create a robust evaluation framework that captures accuracy, semantic alignment, and practical utility.