How does collaborative filtering deals with a new user?

Part 1 (2018)
1

Hello, I watched the video 5 from fast.ai course Part 1 about collaborative filtering.

I have understood the concept, but when I want to use the model in the real situation, when there is a new user, I have to recommend the movies according to the ratings he/she made. Similar for new movie.

Many deep learning models are feed-forward, so when I give it a new data, it pops out a result(prediction, classification, etc). But the model from lecture 5 first need the embedding vector for a new user or new movie first. Is there a way the collaborative filtering handles this new data issue, or should I use another model?

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I think this is a good question. I’d like to know the answer, too.

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This is called the cold start problem. In general, you would have another model that combines with your collab filtering model or “jumps in” when collab filtering cannot help.

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Are there any resources related to DL applications?

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Deep learning for recommenders is a huge and complex topic. The Youtube Recsys channel from 2016 is a good place to start. Paul Covington’s recommender from youtube was one of the first.

There are a number of ways to solve cold start, either through projecting the new user into the latent space based on early actions, starting them from a popularity based model, or by representing all users in item space as some aggregation of the latent representations of items they view.

It’s a huge research area though, and not a lot of intro material is available.

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