Today, e-commerce thrives on personalization, but expanding into the global market is challenging due to language barriers.
Creating a multilingual recommended system ensures a more personalized shopping experience for all users.
Why does your business need the recommended multilingual system?
Language diversity can make or break a global user experience. This ensures that every customer can understand, regardless of their native language.
This approach boosts engagement, increases conversions and builds loyalty across different markets.
Creation of a multilingual recommended system
Product data collection for the recommended multilingual system
- Gather all product data, including product titles and text descriptions.
- Make sure the dataset covers all languages spoken by your target audience.
- Example: The description of a T-shirt is available in English, Hindi and Polish, or several products in different languages, to appeal to a diverse customer base.
Select the right integration model
- Selecting the right integration model is very important. The template must support multilingual.
- Multilingual embedding models are trained on multilingual data.
- Two similar products but written in different languages, their multilingual models generate similar context integrations.
- Some templates for multilingual embeddings are openai – text-embedding-3-large, cohere embed-multilingual-light-v3.0, embed-multilingual-v3.0, etc.
Creating product integrations
Example, generation of integrations from an openai
from openai import OpenAI client = OpenAI() def get_embedding(text, model="text-embedding-ada-002"): text = text.replace("\n", " ") return client.embeddings.create(input=[text], model=model).data[0].embedding product_embedding = get_embedding(""" Wireless Noise-Canceling Headphones Experience superior sound quality with our wireless noise canceling headphones. Designed for comfort,durability and they offer up to 30 hours of battery life and seamless bluetooth connectivity. """)
The result is a vector representation:
[ -0.006929283495992422, -0.005336422007530928, ... (truncated for brevity) ]
Save integrations to VectorDB
Store generated embeds in a vector database with metadata like product ID, language, and category. Popular VectorDBs include:
Recommended products
Step 1: calculate similarity
When a user views a product, the API generates embeds for its title and description.
Query the vector database using these embeddings to find similar products based on cosine similarity or other distance measures.
Step 2: Generate results
The vector database retrieves products with the most similar integrations, providing recommendations.
Even if the recommended products are in different languages, the multilingual model ensures that they share the same contextual meaning.
Benefits of a Multilingual Recommendation System
- Seamless user experience: Users see recommendations in their preferred language.
- Global Reach: Cater to diverse markets without language limitations.
- Increase in sales: Personalized suggestions drive higher conversions.
- Efficient scaling: Multilingual integrations reduce the need for separate models per language.
You can also check out the Magento 2 recommendation system. It works with a multilingual integration model and supports dynamic models.
This is a pair of wireless earbuds with the product description in English.
The recommended products are available in different languages: the first in French, the second in Spanish and the last in Italian.
Our recommendation system suggests products even in different languages.
Conclusion
A multilingual recommendation system is vital for any e-commerce business targeting global markets.
By leveraging multilingual integrations, vector databases, and similarity metrics, you can build an inclusive and effective recommendation engine that resonates with users around the world.
Start your artificial intelligence development with Webkul.