I have a question regarding the use of model based approaches to recommender systems.
So, the goal is to create a model that predicts the user reaction to a specific item. Either a rating scale or a “like/dislike” expectation. The problem that I can’t quite understand is that, in order to so, you would need a specific model for each user. How can that be done in practice?
Let’s take, for instance, a content based recommender that uses a classifier or regression model to predict the user’s interest on items based on its features. Ex: rate movies based on director, cast, genre, etc. Since every user has a distinct individual taste you would need to learn a specific parameter for each user-feature.
So you need to fit a model for each user? How can that work given the fact that the number of observations per user is usually very small compared to number of features? There will be more explanatory variables than observations. Besides the resulting model would be prone to overfitting given the lack of data would it not?