To boost user engagement, we could implement sorting based on machine learning models (not to be confused with language models!). The idea is to build a pipeline that tags each new domain with relevant labels—basically adding some meaningful context to each one. This tagging part can actually be handled by a language model.
Then, we track each user’s activity—what kind of domains they’re bidding on. Using pre-trained machine learning models (no need to build one from scratch!), we can categorize domains and show the most relevant ones at the top of the list for each user.
For first-time users, we could start with a short quiz. We’d present it as a way to “personalize your auction feed.” For example, if someone wants to do dropshipping, they might prefer a used domain (which is great for SEO) and one related to online stores. They can tell us that in the quiz—and boom, we show them domains that match that theme more often when they browse the auction.
To boost user engagement, we could implement sorting based on machine learning models (not to be confused with language models!). The idea is to build a pipeline that tags each new domain with relevant labels—basically adding some meaningful context to each one. This tagging part can actually be handled by a language model.
Then, we track each user’s activity—what kind of domains they’re bidding on. Using pre-trained machine learning models (no need to build one from scratch!), we can categorize domains and show the most relevant ones at the top of the list for each user.
For first-time users, we could start with a short quiz. We’d present it as a way to “personalize your auction feed.” For example, if someone wants to do dropshipping, they might prefer a used domain (which is great for SEO) and one related to online stores. They can tell us that in the quiz—and boom, we show them domains that match that theme more often when they browse the auction.