Drive Better Results With AI-Powered Event Matchmaking and Networking
Artificial intelligence is here to take the matchmaking experience of your networking event to a whole other level. With a little help, your participants will have more meetings with the right people.Contact Sales
Understand How Our Smart AI Recommender Generates Relevant Matches
The AI Meeting Recommender system uses actual machine learning algorithms that process large amounts of data in order to recommend interesting profiles to every participant. The result is increased engagement and high quality matchmaking interactions.
Collecting Participants’ Interests and Preferences
In order to generate relevant profile recommendations, a lot of data is required. Participants naturally produce this data through their profile details and by simply using the platform.
- Participants visit each other’s profiles and get in touch through messages and meeting requests
- This leaves a trail of connections and creates a complex network of participant interactions
- Along with their profile data, this information is continuously being fed to our machine learning algorithm in near real-time
Understanding Participants’ Needs with Machine Learning
While gathering data, the algorithm is also processing it at the same time. This continuous cycle allows it to find out what each participant is interested in.
- The algorithm is constantly analyzing participant behavior and their profile information
- It’s interpreting the data to understand what each participant’s interests and objectives are
- Once it understands, it is able to predict which profiles a participant might find interesting
Providing Relevant Profile Recommendations
Once the recommendations are generated, they are ready to be presented to participants. This stimulates engagement and interactions in your networking event.
- Each participant sees a different set of profile recommendations, depending on their interests
- As the algorithm continues to process data, the recommendations improve
- A participant can mark some recommendations as irrelevant, which in turn helps the algorithm
- The more active a participant is, the better the recommendations
Knowledge Is Key, We Happily Share It
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