Search relevance is critical for e-commerce platforms. Users expect fast, accurate results, and failure leads to lost revenue.
Technical Overview
Modern systems rely on Elastic search and Solr, distributed nodes, inverted indexes, and Redis caching. Advanced implementations use Sentence-BERT embeddings for semantic matching between queries and products.
Algorithmic Approaches
Stage 1: Candidate retrieval using BM25
Stage 2: Re-ranking using ML models like LightGBM/XGBoost
Hybrid: Vector databases (e.g., Pinecone) alongside inverted indexes
Zero-Result Handling
- Zero-result queries reduce sales. Solutions include:
- Synonym mapping using Word2Vec embeddings
- Fuzzy search for typos and alternative product names
- Dynamic query expansion from clickstream data
Personalization & Business Rules
Feature stores maintain behavioral data for real-time personalized search. Business rules prioritize high-margin or promotional products.
Performance Metrics
Track CTR, conversion rate, zero-result reduction, and AOV for optimization. Use streaming analytics pipelines for real-time adjustments.
RBM Software Solutions
RBM Software implements advanced search architectures combining BM25-neural ranking, semantic embeddings, and real-time optimization to improve user experience and revenue.
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