To improve recall in vector search when true neighbors are missed, focus on adjusting index parameters, refining search strategies, and combining approximate and exact methods. Here are three effective approaches:
1. Optimize Index Construction Parameters
Adjusting index parameters can significantly impact recall. For example, in HNSW (Hierarchical Navigable Small World) indexes, increasing efConstruction (the number of neighbors considered during graph construction) improves the quality of the index structure, leading to better recall. Similarly, increasing the number of layers in HNSW or using a higher m (maximum connections per node) ensures denser connections, which helps traverse the graph more effectively during search. For IVF (Inverted File) indexes, increasing the number of clusters (e.g., nlist in FAISS) reduces the number of vectors per cluster, making brute-force searches within clusters more precise. However, these changes trade off higher memory usage and longer build times for better recall.
2. Use Re-Ranking with Exact Search
Approximate nearest neighbor (ANN) methods sacrifice some accuracy for speed. To mitigate this, retrieve a larger candidate set (e.g., top 200 results) using the ANN index, then re-rank those candidates with exact distance calculations (e.g., brute-force). For example, if your final goal is to return 10 results, first fetch 200 approximate matches, compute their exact distances, and pick the top 10. This hybrid approach leverages ANN’s speed while ensuring the final results are accurate. Tools like FAISS support this via index.search_and_reconstruct or custom pipelines. The trade-off is increased latency during re-ranking, but the recall improvement is often worth it.
3. Experiment with Search-Time Parameters
Adjust search-time parameters to broaden the exploration scope. In HNSW, increasing efSearch (the number of nodes visited during search) allows the algorithm to explore more paths in the graph, increasing the likelihood of finding true neighbors. For IVF indexes, raising nprobe (the number of clusters searched) expands the search to more clusters, reducing the chance of missing relevant vectors. For example, increasing nprobe from 4 to 16 in FAISS’s IVF index might improve recall but will linearly increase query time. Testing these parameters incrementally on a validation set helps balance recall and performance.
Implementation Example
Suppose you’re using FAISS with an IVF index. Start by increasing nlist during index creation to 4096 (from 1024) to create finer clusters. During search, set nprobe to 64 (from 16) to scan more clusters. If latency is acceptable, retrieve 500 candidates with ANN and re-rank them exactly. Monitor recall metrics like [email protected] or [email protected] to validate improvements. If using HNSW, set efConstruction=400 and efSearch=200 to prioritize recall over build/search speed. Always benchmark parameter changes against a labeled dataset to avoid over-optimizing for specific edge cases.