Boosting Ad Clicks: A Smarter Way to Predict What You'll Click

Predicting whether someone will click on an ad is a big deal for online platforms. But current methods have some issues. They don't use enough outside information. And they can't quickly figure out what a person is interested in. A new approach called Large Interest Network (LIN) tries to fix these problems. It uses big language models to create detailed profiles for users and items. This helps the system understand what people might like. LIN also uses a technique called contrastive learning. This helps the system learn from different types of data while keeping the useful parts. Plus, it groups similar interests together. This makes the system faster and more accurate. Tests show that LIN works better than other methods. It predicts clicks more accurately and does so much faster. This could mean better ads for users and more money for platforms. But is this the final answer? Maybe not. The system is complex and needs a lot of data. Also, it's not clear how well it works in the real world. Still, it's a step forward in making ad predictions smarter.