Predicting the next click

Say I want to predict where a user will click next for an internal network. For the sake of discussion lets say I am only concerned about a fix number of URLs (say 60K unique addresses). If I have access to log files that track all user activity (sequence of visited URLs) could I use an approach similar to a language model (encode the URLs instead of words) to predict where a user might click next? In my head it seems possible, just wanted to check with the community to see if I am overlooking something that would make this approach impractical.

Have any of you taken an alternative approach that worked well for a similar type of problem?

Thanks for your help,

You can! At least try it. I have being working on similar use cases with transformers.
BERT in MLM mode acts as denoising autoencoder, and essentially you can train it to reconstruct an item in a sequence. In your case it will be sequence of user actions.
You would need quite at lot of user activities recorded to train it though.

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Speaking of which,
here is described a use case transformers used for protein prediction

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I don’t see why not. I think a user specific embedding could help as well. Such as “where do most users who are similar to this user go when they are in this page.” In this way you set it up like a recommendation problem. So you could combine the two approaches to see how far you get, and are able to leverage more of your data for predictions.

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