Enhance the company similarity engine for better accuracy & filtering

We are improving our company similarity engine by refining how we calculate similarity, adding advanced filters, improving pagination, and ensuring greater accuracy in results. Additionally, we will provide a detailed explanation of how similarity is computed to improve transparency for users.

🔹 Why?

• Helps users understand how company similarity is calculated.

• Enables better filtering by allowing users to refine results based on industry, employee count, and other key attributes.

• Improves pagination for better navigation through large sets of similar companies.

• Increases accuracy and relevance by fine-tuning similarity scoring.

🔹 How it works?

1. Explanation of Similarity Calculation

• Add a dedicated pop-up or help page that explains:

• The factors used in similarity scoring (e.g., industry, revenue, employee count, business model, tech stack, location).

• The weighting system for each factor (e.g., industry similarity > technology similarity > revenue proximity).

• Why some companies are ranked higher than others in similarity results.

2. Advanced Filtering for Similar Companies

• Add new search conditions to refine results:

• Industry filter → Only show companies in a specific industry.

• Employee count range → Filter companies based on workforce size.

• Revenue range → Find similar businesses with comparable revenue.

• Geographic location → Show similar companies within a specific region.

• Provide an intuitive UI for users to apply and modify these filters easily.

3. Improved Pagination & Performance

• Implement proper pagination controls to allow viewing more than 25 companies in the results.

• Optimize queries to ensure fast response times, even with large datasets.

• Allow users to load more results dynamically without refreshing the page.

4. Data Quality Enhancements

• Ensure high data accuracy by refining how similar companies are retrieved.

• Validate that newly added companies are properly categorized for better recommendations.

• Regularly update similarity metrics as company data evolves.

5. Additional Recommendations

• Implement a “Why is this company similar?” tooltip for each result.

• Allow users to adjust similarity weighting dynamically (e.g., prioritize industry over employee count).

• Consider user feedback integration where users can confirm whether suggested companies are indeed similar.

This update will provide more control, transparency, and accuracy, helping users find the most relevant similar companies efficiently.