Build an AI-Powered Product Recommendation Engine with Web Scraping

Creating a personalized recommendation engine traditionally requires complex algorithms and massive datasets. However, with modern web scraping tools and large language models, this process has become remarkably simplified. This article explores how to combine web scraping with AI to build a powerful recommendation system that can be applied to various e-commerce scenarios.

Turning Web Pages into LLM-Ready Content

The foundation of this recommendation engine relies on transforming web page content into structured data that large language models (LLMs) can process. Rather than dealing with the complexities of web scraping directly, tools like Scraper API provide ready-to-use solutions that handle the technical challenges.

The process begins by accessing Amazon search results through a specialized API endpoint. This approach eliminates common scraping obstacles like IP blocking, proxy management, and HTML parsing, allowing developers to focus on building the recommendation logic instead.

Step-by-Step Implementation

The implementation consists of two main components:

Data collection through web scraping
AI-powered recommendation generation

1. Data Collection

To collect product data from Amazon:

Get your API key from Scraper API
Specify your search query (e.g., “smartphones between 30,000 rupees and 70,000 rupees” or “nonfiction books for learning programming”)
Send a request to the Amazon structured endpoint
Store the retrieved data in a JSON file

The resulting JSON contains structured product information that’s ready for analysis by an LLM.

2. AI-Powered Recommendations

Once the product data is collected, the recommendation engine leverages Google’s Gemini models:

Create a user profile with demographic and interest information
Authenticate with the Gemini API
Choose a suitable model (e.g., Gemini 2.5 Flash or Pro)
Define the output structure for recommendations
Pass the structured data and user profile to the model
Receive and store personalized product recommendations

Customization Options

The system allows for extensive customization:

Change search queries to target different product categories
Modify user profiles to get personalized recommendations
Adjust recommendation criteria and output format
Use different Gemini models based on speed and accuracy requirements

In testing, the system successfully recommended relevant products across different categories. For example, when searching for business books, it suggested titles like “48 Laws of Power” and “The Psychology of Money.” When the query was changed to programming books, it recommended “Learn Python Programming” and “Learn to Code by Solving Problems.”

Benefits of This Approach

This method offers several advantages over traditional recommendation systems:

No need for complex algorithms like collaborative filtering or matrix factorization
Minimal data engineering requirements
On-demand recommendations without pre-built datasets
Easy adaptation to different e-commerce platforms
Cost-effective implementation using free or low-cost API tiers

Furthermore, because the recommendation logic is handled by state-of-the-art language models, the system can understand nuanced user preferences and provide detailed reasoning for its suggestions.

Conclusion

By combining web scraping tools with generative AI, developers can create sophisticated recommendation engines with remarkably little code. This approach makes advanced personalization accessible to projects of all sizes, without requiring specialized knowledge in recommendation algorithms or large-scale data processing.

As both scraping technologies and language models continue to evolve, we can expect even more powerful and accessible recommendation systems to emerge, further personalizing the online shopping experience.