What are the Recommendations?

The “Recommendations” in OnePass are designed to help our users target the right companies (startups or investors), by mapping and segmenting the potential matches based on your profile and funding needs (if a startup) or investment interests (if an investor). These recommendations are automatically generated by OnePass, and offered to you in the “Recommended for you” section of your homepage. For startups, this section will show the allied investor and funding opportunities, and the investors’ breakdown by the tags in your main interests. For investors, it will show the startups that match your investment criteria, the startups that have recently joined OnePass, as well as the breakdown of the startups by tags in your main interests.

Key Benefits of Recommendations

For Startups

1. Quality (of matches) matters

   • Find investors who are actively looking for startups like yours, based on your profile, funding needs and your own selection.

2. Time-saving

   • Focus on the most relevant investors and funding opportunities, reducing the time spent searching for the right match.

3. Dinamic Updates

   • As your profile evolves, so do your recommendations, ensuring you always see the most relevant opportunities.

For Investors

1. Down to the point

   • Discover startups that align with your investment interests, ensuring you see the most relevant opportunities first.

2. Efficient Deal Flow

   • Spend less time searching and more time evaluating high-potential startups that match your criteria.

3. Be the first

   • Get early access to new startups that have just joined OnePass, allowing you to connect before they become widely known.

How Recomendations Work

The recommendations are generated based on a “score” which is calculated as a combination of factors, including:

• Your Company Profile: The hig-concept pitch and the description you provide in your profile, 2 critical criteria (country and round) in your funding needs (for startups) or investment criteria (for investors) and the amount of hits in the rest of answers in those two sections.
• Your feedback: OnePass will consider your use of the Like and Dislike icons in each of the recommended companies to show More or Less companies like the one you are providing feedback about.

Tips for Optimizing Your Recomendations

• Complete Your Company Profile: Ensure your profile is filled out, paying special attention your funding needs (if a startup) or investment criteria (if an investor). The more information you provide, the better the recommendations.
• Use Relevant Tags: When filling out your profile, use wisely industry and technology tags: e.g. using a generic tag to position your business in an industry and other more specific to identify accurately the market segment you are targeting or the technologies you are using for that. This helps OnePass categorize and match you with the right companies.
• Update Regularly: Keep your profile updated with any changes in your funding needs, or investment interests. This helps OnePass refine your recommendations over time.
• Engage with the Platform: Actively use OnePass by adding companies to your watchlist, and exploring new companies or opportunities. This engagement helps OnePass understand your preferences better.
• Provide Feedback: provide feedback through the thumbs up or down option in the recommendations lists. This helps improve the accuracy of future recommendations.

Recommendations vs Suggestions and Prioritization

Suggestions are those companies that appear in a cloud at the bottom of any list to suggest potential matches for that particular list. Suggestions work differently from recommendations in OnePass: while recommendations are automatically generated based on your workspace and generic interests, the suggestions at the bottom of each list are rather based in the similarity to other items that you added to it, even if they don’t perfectly match your profile.

On a different note, the prioritization of the items in the results lists obtained after a search using filters is based on a specific user behaviour, which is the similarity to the specific items you viewed in the list.