So when making a deep recommender system, I see an obvious problem with making it work with a NN, versus some kind of correlation analysis. Basically when you have a network doing the recommending, whenever you add new things to recommend (new products, movies, books, etc) you’ll have to retrain the model on the new “products” to get recommendations including those new things.

Is there an easy way to structure a network’s architecture to mitigate this problem? Or do you just have to retrain the model all the time?

Thanks!