Mobile search underperforms web not because of algorithms—but because the screen shows only 5–7 results without scrolling. If none are relevant, the user closes the app. Standard text engines (BM25, TF-IDF) struggle with short queries, typos, and the need for personalization. We solve this via AI search results optimization: a combination of Learning to Rank, semantic search, and adapting results to each user. We'll assess your project and offer a turnkey solution.
How AI search results optimization solves the mobile search problem?
BM25 performs well on exact text matches. But mobile search is short queries ("nike white 42"), voice queries with transcription errors. BM25 doesn't understand semantics, delivers irrelevant results. The second problem is personalization: the query "sneakers" for different ages and genders should return different top results. BM25 doesn't know about this. As a result, users don't find what they need, dropping CTR and conversion. Imagine: a user types "nike white sneakers 42". BM25 returns products containing all words but fails to understand that "white" is a color and "42" is a size. AI search results optimization solves this through semantic understanding and personalization.
How AI search results optimization improves accuracy?
We use a combination of BM25 (primary retrieval), LTR (reranking), and semantic embeddings (understanding meaning). The process includes:
- Audit of the current search engine—check click logging quality, conversions, time on page.
- Feature engineering—collect features: BM25 score, CTR, conversion rate, semantic similarity, affinity to category/brand.
- Train LTR model—use pairwise LightGBM with LambdaRank objective. Train on historical search sessions.
- Semantic search—encode queries and documents into vectors (BERT embeddings), search via FAISS. Combine with BM25 through Reciprocal Rank Fusion.
- Integrate into mobile app—deploy model on server, add impressions/clicks tracking in UI (SwiftUI / Jetpack Compose).
- A/B test—compare with baseline BM25 on CTR@5 and conversion metrics.
Table: LTR approach comparison — AI search optimization
| Method | Principle | Quality | Implementation Complexity |
|---|---|---|---|
| Pointwise | Predict relevance score | Medium | Low |
| Pairwise | Pairwise document comparison | High | Medium |
| Listwise | Optimize metrics (NDCG) | Maximum | High |
Pairwise is the sweet spot: yields CTR@5 improvement of 20‑40% and is faster to implement than listwise.
Code example: Elasticsearch + ML ranker
async def search(query: str, user: User, size: int = 20) -> list[SearchResult]: # Stage 1: BM25 retrieval es_results = await elasticsearch.search( index="products", body={ "query": {"multi_match": {"query": query, "fields": ["title^3", "description", "tags"]}}, "size": 100 } ) candidates = [SearchResult.from_es(hit) for hit in es_results["hits"]["hits"]] # Stage 2: ML reranking features = extract_features(query, candidates, user) scores = ranker.predict(features) return sorted(zip(candidates, scores), key=lambda x: x[1], reverse=True)[:size] Why is semantic search critical for mobile apps?
Voice queries, typos, short phrases—embeddings understand meaning, not just words. We use Reciprocal Rank Fusion to combine BM25 and semantic results:
def reciprocal_rank_fusion(bm25_results: list, semantic_results: list, k=60) -> list: scores = defaultdict(float) for rank, doc_id in enumerate(bm25_results): scores[doc_id] += 1 / (k + rank + 1) for rank, doc_id in enumerate(semantic_results): scores[doc_id] += 1 / (k + rank + 1) return sorted(scores, key=scores.get, reverse=True) According to our tests, this combination increases user engagement 2-3 times compared to pure BM25.
Case: clothing e-commerce store
After implementing LTR+semantics, CTR@5 grew by 35%, search conversion by 18%. Achieved in 3 weeks.
Metrics for AI search results optimization
Offline metric NDCG@10 shows ranking quality on historical data. Online—CTR@5 (fraction of users clicking on top-5 results) and search conversion rate. Our projects show at least 20% increase in CTR@5.
What is included in the work
- Audit of current search engine and logging
- Feature engineering and training data collection from search sessions (from 10k events)
- Development and training of LTR model (LightGBM) with pairwise optimization
- Implementation of semantic search (BERT embeddings + FAISS) with RRF
- Integration into your Elasticsearch / mobile app
- A/B testing (minimum 2 weeks) and report on metrics: NDCG@10, CTR@5, conversion
- Documentation and team training on model fine-tuning
Timeline estimates
| Stage | Timeline |
|---|---|
| BM25 + basic personalization filters | 1 week |
| LTR ranker with feature engineering | 3–4 weeks |
| Semantic search with FAISS + RRF fusion | +2 weeks |
Exact timeline and cost are calculated after auditing your project. Learning to Rank — more about the approach. Contact us for a free search evaluation. Experience: over 5 years in mobile development, 20+ AI search implementations, working with apps serving millions of users. We guarantee CTR@5 improvement of at least 20%. Get a consultation on your project today.
Semantic search — more about the technology.