To measure the effectiveness of a semantic search system, focus on three categories of metrics: traditional information retrieval (IR) metrics, embedding-based similarity scores, and task-specific success indicators. Each provides a different lens to evaluate how well your system matches user intent with relevant results.
First, consider standard IR metrics like Precision@k and Recall@k. Precision@k measures the percentage of relevant results in the top k retrieved items. For example, if a user searches for “how to fix a leaky pipe” and 3 out of the top 5 results are truly about plumbing repairs, Precision@5 is 60%. Recall@k tracks how many of the total relevant documents in your dataset appear in the top k results. These metrics are straightforward but require labeled data (human judgments of relevance) and may not fully capture semantic nuances. Pair them with manual evaluations to ensure relevance aligns with user intent, especially when queries involve synonyms or ambiguous terms (e.g., “Apple” as a company vs. fruit).
Next, use embedding-based metrics to evaluate semantic alignment. Compute the cosine similarity or dot product between query and document embeddings (e.g., from models like BERT or SBERT) to quantify semantic closeness. For ranked results, calculate Mean Reciprocal Rank (MRR) or Normalized Discounted Cumulative Gain (NDCG). MRR focuses on the rank of the first relevant result—if the correct answer is at position 3, MRR is 1/3. NDCG assigns higher weights to top-ranked relevant items, making it useful for multi-grade relevance (e.g., “perfect,” “good,” “bad” matches). These metrics work well when you have ground-truth labeled pairs but may miss edge cases where embeddings fail to capture domain-specific context.
Finally, track task-specific success metrics tied to user behavior or business outcomes. For example, in e-commerce search, measure click-through rates (CTR) or conversion rates for retrieved products. In a support chatbot, track resolution rates or reduced escalations when agents use search results. A/B testing is critical here: compare metrics like session duration or bounce rates between search algorithm versions. For instance, if a new semantic model increases CTR by 15%, it likely better aligns with user intent. Combine these with error analysis—log frequent low-confidence queries or irrelevant results to identify gaps in your embedding model or training data.
In summary, start with Precision@k and Recall@k for baseline relevance, add embedding-based metrics like NDCG for semantic alignment, and validate with task-specific indicators like CTR. Use a mix of automated scores and human evaluation to ensure your system handles both explicit and contextual relevance.