TriBIG AI Market Recommendation Algorithm: What Is Cosine Similarity?
The TriBIG AI platform compares and analyzes Korean export companies' product and capability data against global import market data using cosine similarity algorithms to automatically recommend optimal export markets for each company. Unlike conventional statistics-based market analysis, the core innovation lies in mathematically computing the "similarity" between companies and markets in a multi-dimensional vector space.
This algorithm is a core technology among KOTRA DX Innovation Lab's 28 projects, processing over 100,000 trade data records annually to support new market development for small and medium-sized enterprises.
Data Pipeline: From Collection to Vectorization
TriBIG's market recommendation system consists of a four-stage data pipeline: raw data collection, preprocessing and normalization, feature vector generation, and cosine similarity calculation. Each stage is automated to enable real-time recommendations.
Matching Logic: Multi-Dimensional Analysis with 120+ Variables
The variables used for company-market matching are classified into four dimensions. The analysis goes beyond simple export-import volumes to comprehensively consider market growth potential, competitive landscape, trade barriers, and logistics accessibility.
| Dimension | Key Variables | Data Source | Weight |
|---|---|---|---|
| Market Size | Total imports, product-level imports, CAGR | UN Comtrade, ITC | 30% |
| Competitive Landscape | Korea market share, competitor shares, HHI concentration | Korea Customs, TradeMap | 25% |
| Trade Barriers | Tariff rates, non-tariff barriers, FTA benefits | WTO, FTA Portal | 20% |
| Logistics & Accessibility | Shipping costs, lead time, payment terms | Freightos, K-SURE | 25% |
Real-World Cases: Bangladesh Market
TriBIG AI's market recommendation algorithm demonstrates particularly high accuracy in emerging South Asian markets including Bangladesh. Multiple cases have translated data-driven identification of previously overlooked niche markets into actual export results.
| Product | Cosine Score | Recommendation Basis | Export Outcome |
|---|---|---|---|
| Medical Devices (HS9018) | 0.87 | Import growth 22%, Korea share 3.2% | 5 new buyer connections |
| Cosmetics (HS3304) | 0.83 | Growing middle class, Korean Wave, 15% tariff | USD 500K annual exports achieved |
| Auto Parts (HS8708) | 0.81 | Growing auto assembly, parts import dependency | Local OEM supply contract |
| Electronic Components (HS8542) | 0.79 | Growing electronics manufacturing, FTA under review | Sample export followed by expansion |
| Plastics (HS3926) | 0.76 | Surging packaging demand, price competitiveness | Annual contract signed |
Technical Advancement: Deep Learning Hybrid Model
Starting in 2025, TriBIG is advancing to a hybrid recommendation system that combines traditional cosine similarity with deep learning-based embedding models. Transformer-based trade data embeddings that learn time-series patterns are enabling "predictive recommendations" that forecast future market demand changes.